================================================================================
CRATE-AUGMENTED SOURCE BUNDLE (de novo fork)
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Project: VOICE
Document bundle: data/preprocessed/concatenated/VOICE_preprocessed.txt
Crate package: data/ro-crate_packages/VOICE
Crate manifest: data/ro-crate_packages/crate_manifest.yaml

This bundle is the document corpus plus RO-Crate evidence. Artifacts
that are already in D4D or datasheet form are deliberately withheld so
that this arm extracts rather than transcribes; see the exclusion list
below and notes/D4D_GENERATION_ARMS.md.

CRATE EVIDENCE INCLUDED
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  + VOICE_crate_metadata_reduced.json — crate JSON-LD with file inventories collapsed; the substantive evidence (rai:* fields, ethics, access, provenance)
  + ai_ready_score.json — AI-readiness self-assessment

CRATE ARTIFACTS WITHHELD
--------------------------------------------------------------------------------
  - ro-crate-datasheet.html — upstream-authored datasheet rendering of the same content; a datasheet is the artifact being generated, so it is withheld as input
  - ro-crate-preview.html — per-file listing, up to 11.8 MB, no prose

================================================================================
================================================================================
CONCATENATED DOCUMENT
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Input Directory: data/preprocessed/individual/VOICE
Total Files: 11
Extensions: ['.txt']
Recursive: False
Selection Manifest: data/preprocessed/source_manifest.yaml
================================================================================

TABLE OF CONTENTS
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  1. pmc_ncbi_nlm_nih_gov_articles-PMC12037532_row2.txt
  2. gdrive_1PiK_YlEoFhte1i4LMv7yAPCt2mRA-Si5_row5.txt
  3. reporter_nih_gov_project-details-11376382_row7.txt
  4. docs_b2ai-voice_org_row10.txt
  5. gdrive_1gTFzAM-FoYlM_X9qF0s7fXoswmaz8IqN_row13.txt
  6. gdrive_1z4zZ_Z_Jb017IoVZn5btJnSLKdEOHZPA_row14.txt
  7. physionet_b2ai-voice_1.1_row17.txt
  8. physionet_b2ai-voice_3.0.0_row18.txt
  9. physionet_b2ai-voice_3.1.0_2026-07-24.txt
 10. physionet_b2ai-voice-pediatric_1.1.0_2026-07-24.txt
 11. github_eipm_bridge2ai-docs_README_row22.txt
================================================================================

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SOURCE METADATA
Project: VOICE
Source ID: feasibility_publication
Source type: publication
Source URL: https://pmc.ncbi.nlm.nih.gov/articles/PMC12037532/
Raw file: data/raw/VOICE/pmc_ncbi_nlm_nih_gov_articles-PMC12037532_row2.html
--------------------------------------------------------------------------------
The Bridge2AI-voice application: initial feasibility study of voice data acquisition through mobile health - PMC
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Front Digit Health
. 2025 Apr 15;7:1514971. doi:
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The Bridge2AI-voice application: initial feasibility study of voice data acquisition through mobile health
Elijah Moothedan
Elijah Moothedan
1
Charles E. Schmidt College of Medicine, Florida Atlantic University, Boca Raton, FL, United States
Find articles by
Elijah Moothedan
1
,
Micah Boyer
Micah Boyer
2
USF Health Voice Center, Department of Otolaryngology-Head & Neck Surgery, University of South Florida, Tampa, FL, United States
Find articles by
Micah Boyer
2
,
Stephanie Watts
Stephanie Watts
2
USF Health Voice Center, Department of Otolaryngology-Head & Neck Surgery, University of South Florida, Tampa, FL, United States
Find articles by
Stephanie Watts
2
,
Yassmeen Abdel-Aty
Yassmeen Abdel-Aty
2
USF Health Voice Center, Department of Otolaryngology-Head & Neck Surgery, University of South Florida, Tampa, FL, United States
Find articles by
Yassmeen Abdel-Aty
2
,
Satrajit Ghosh
Satrajit Ghosh
3
McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, United States
Find articles by
Satrajit Ghosh
3
,
Anaïs Rameau
Anaïs Rameau
4
Sean Parker Institute for the Voice, Department of Otolaryngology-Head & Neck Surgery, Weill Cornell Medical College, New York, NY, United States
Find articles by
Anaïs Rameau
4
,
Alexandros Sigaras
Alexandros Sigaras
5
Englander Institute for Precision Medicine, Weil Cornell Medical College, New York, NY, United States
Find articles by
Alexandros Sigaras
5
,
Olivier Elemento
Olivier Elemento
5
Englander Institute for Precision Medicine, Weil Cornell Medical College, New York, NY, United States
Find articles by
Olivier Elemento
5
;
Bridge2AI-Voice Consortium
,
Yael Bensoussan
Yael Bensoussan
2
USF Health Voice Center, Department of Otolaryngology-Head & Neck Surgery, University of South Florida, Tampa, FL, United States
Find articles by
Yael Bensoussan
2,
*
Author information
Article notes
Copyright and License information
1
Charles E. Schmidt College of Medicine, Florida Atlantic University, Boca Raton, FL, United States
2
USF Health Voice Center, Department of Otolaryngology-Head & Neck Surgery, University of South Florida, Tampa, FL, United States
3
McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, United States
4
Sean Parker Institute for the Voice, Department of Otolaryngology-Head & Neck Surgery, Weill Cornell Medical College, New York, NY, United States
5
Englander Institute for Precision Medicine, Weil Cornell Medical College, New York, NY, United States
Edited by:
Toshiyo Tamura, Waseda University, Japan
Reviewed by:
Diala Haykal, Centre Médical Laser Palaiseau, France
Tongyue He, Northeastern University, China
*
Correspondence:
Yael Bensoussan
yaelbensoussan@usf.edu
Received 2024 Oct 22; Accepted 2025 Mar 31; Collection date 2025.
© 2025 Moothedan, Boyer, Watts, Abdel-Aty, Ghosh, Rameau, Sigaras, Elemento, Bridge2AI-Voice Consortium and Bensoussan.
This is an open-access article distributed under the terms of the
Creative Commons Attribution License (CC BY)
. The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
PMC Copyright notice
PMCID: PMC12037532  PMID:
40302934
Abstract
Introduction
Bridge2AI-Voice, a collaborative multi-institutional consortium, aims to generate a large-scale, ethically sourced voice, speech, and cough database linked to health metadata in order to support AI-driven research. A novel smartphone application, the Bridge2AI-Voice app, was created to collect standardized recordings of acoustic tasks, validated patient questionnaires, and validated patient reported outcomes. Before broad data collection, a feasibility study was undertaken to assess the viability of the app in a clinical setting through task performance metrics and participant feedback.
Materials & methods
Participants were recruited from a tertiary academic voice center. Participants were instructed to complete a series of tasks through the application on an iPad. The Plan-Do-Study-Act model for quality improvement was implemented. Data collected included demographics and task metrics including time of completion, successful task/recording completion, and need for assistance. Participant feedback was measured by a qualitative interview adapted from the Mobile App Rating Scale.
Results
Forty-seven participants were enrolled (61% female, 92% reported primary language of English, mean age of 58.3 years). All owned smart devices, with 49% using mobile health apps. Overall task completion rate was 68%, with acoustic tasks successfully recorded in 41% of cases. Participants requested assistance in 41% of successfully completed tasks, with challenges mainly related to design and instruction understandability. Interview responses reflected favorable perception of voice-screening apps and their features.
Conclusion
Findings suggest that the Bridge2AI-Voice application is a promising tool for voice data acquisition in a clinical setting. However, development of improved User Interface/User Experience and broader, diverse feasibility studies are needed for a usable tool.
Level of evidence
: 3.
Keywords:
artificial intelligence, voice, biomarkers, mobile application, voice biomarkers
Introduction
The human voice constitutes a rich source of information as it relates to disease status (
1
). With its spectrum of acoustic features coupled with its cost-effectiveness and accessibility, voice has gained recognition for its utility as a potential biomarker for disease, screening, diagnosis and monitoring (
2
,
3
). Furthermore, recent development technology, such as artificial intelligence (AI) and machine learning (ML), have seen its introduction into the realm of voice analysis that can now be automated to process large amounts of data (
1
). Combining AI/ML with voice analysis allows for efficient analysis of voice data, which promises discovery of scalable acoustic markers in association with health diagnosis, screening, and monitoring to improve patient outcomes (
4
).
Voice data collection is low cost inexpensive, often only requiring recording device with a microphone (i.e., computer, smart device). This simplicity makes voice-based screening and diagnostics an attractive tool to utilize in low-resource settings. However, to unlock the full potential of voice as a tool, there is a crucial need for large datasets that capture diverse populations and disease statuses along with other established physiologic biomarkers (
2
,
5
,
6
). Current literature on this topic has only been studied on small- to medium-sized data sets with limited data outside of acoustic measures not linked to multi-modal health data. Inclusion of speech and voice data in large-scale trials adds an additional longitudinal variable that has the potential to improve scientific discovery and patient outcomes (
7
), but comparing studies and pooling data is challenging due to a lack of existing standards in how voice and other acoustics are measured and collected (
8
).
In hopes of advancing the potential of voice as a biomarker, the Bridge2AI-Voice consortium has the goal of establishing an ethically sourced, diverse, and publicly available voice database linked to multimodal health biomarkers (
9
). This extensive and open-access voice database will serve as the foundation for voice AI research, facilitating the development of predictive models that can significantly advance the field of voice through improved quality acoustic data, establishment of voice bioinformatic standards, development of an infrastructure for audiomic data storage, and formulation of training algorithms for clinicians and scientists.
In order to create this voice database, the Bridge2AI-Voice Consortium developed a novel mobile application hosting the data acquisition protocols to collect data through various acoustic tasks, surveys, questionnaires, and validated patient-reported outcomes (PROs). With the goal of creating data collection with users at home, there needs to be an evaluation of its utility in order to identify technical constraints and challenges that exist. A pilot feasibility study allows for us to gain a preliminary understanding of user interaction and general feedback of this app for a smoother transition to broad implementation (
10
). This pilot feasibility study assesses the possible implementation of this application through task performance metrics and participant feedback.
Materials & methods
Study setting and participants
This study was conducted at a tertiary academic voice center, University of South Florida Health Voice Center, in Tampa, Florida between June 5, 2023, and July 28, 2023. The study used a mixed sample of participants, with and without voice disorders. The eligibility criteria included: participants who were at least 18 years old and could read the English language. Exclusion criteria were as follows: an inability to provide informed consent in English and inability to read English. All patients meeting inclusion criteria were offered to participate in the study.
Enrollment
Participants were recruited by providers or research staff for enrollment. Participants were informed during the consent process that the app was created by the Bridge2AI-Voice consortium and outlined its purpose. Participants were explicitly informed about the data that was being collected, the methods used to secure these data, and the information that would be used for the study. All participants provided written informed consent for all the study procedures. The participants did not receive financial incentives for completion of the study. The study was approved by the Institutional Review Board of the University of South Florida (IRB number 004890).
App development
A multi-institutional, multi-disciplinary group of researchers participated in the development of this novel tool. The group consisted of researchers from 14 different institutions and with expertise in software engineering, data science, machine learning, laryngology, speech pathology, acoustic science, bioethics, pulmonary science, neurological biomarkers, and mood biomarkers. The aim of the app was to collect demographic information, validated questionnaires, and acoustic tasks for four different categories of diseases in the adult population: vocal pathologies, neurological and neurodegenerative disorders, mood and psychiatric disorders, and respiratory disorders. Full protocols for data acquisition were developed including the following categories:
-
Demographics: The group was asked to include common demographic data and include other demographics that could affect voice and speech (e.g., weight, socio-economic status, literacy status, etc.).
-
Past medical history (PMHx): The group was asked to include common disorders with care being taken to include diseases and conditions that are known to affect voice and speech (e.g., COPD, chronic sinusitis).
-
Confounders: The group was asked to include confounders and social habits that are known to affect voice and speech (e.g., smoking status, hydration status).
-
Acoustic tasks: The group was asked to include common acoustic tasks performed for screening or diagnosis of the conditions studied in the clinical setting or research setting.
-
Validated questionnaires and PROs: Patient-reported outcomes and validated patient questionnaires commonly used in clinical or research practice with evidenced-based correlation with the diseases studied (e.g., GAD-7 for anxiety, VHI-10 for dysphonia).
-
Clinical Validation: The group was asked to develop a section including questions that would confirm the diagnosis and treatment obtained by a clinician.
-
“Gold Standards”: The group was asked to add data modality that are used for confirmation or included in the basic work-up of the diseases studied (e.g., pathology report for laryngeal cancer, pulmonary function test for asthma).
Full data acquisition protocols will be available in the REDCap instrument Shared Library and are also available for download at our GitHub repository:
https://github.com/eipm/bridge2ai-redcap
.
All current tasks on the app during the study period are listed in
Table 1
. The Bridge2AI-Voice is undergoing constant alpha- and beta-testing in order to better understand its practicality and usability among the general public before large-scale data collection and therefore, some tasks may be altered, added, or removed based on patient feedback, auditing and validation experiments conducted by our group (
11
).
Figures 1
,
2
showcase the current design of the app.
Table 1.
Available tasks, PROs, questionnaires, and mean time for completion on the Bridge2AI-voice app.
Task
Type of task
Mean time for completion (min:sec)
Demographics
Questionnaire
2:29
Confounders
Questionnaire
11:22
Voice perception
Questionnaire
0:20
Voice problem severity
Questionnaire
0:12
Voice handicap index-10 (VHI-10)
Validated PRO
0:37
Patient health questionnaire-9 (PHQ-9)
Validated PRO
1:19
General anxiety disorder-7 (GAD-7)
Validated PRO
1:04
Positive and negative affect scale (PANAS)
Validated PRO
0:49
Custom affect scale
Validated PRO
1:14
DSM-5 adult
Validated PRO
5:24
PTSD adult
Validated PRO
2:47
ADHD adult
Validated PRO
2:23
Audio check
Acoustic Task
0:30
Open in a new tab
Figure 1.
Open in a new tab
Bridge2AI-Voice app interface.
Figure 2.
Open in a new tab
Bridge2AI-Voice app interface.
Outcome measures
Demographics
Participants completed a basic sociodemographic questionnaire at enrollment which included: age, gender, primary language, education level, employment status. Additional information collected included any history of a voice disorder, self-reported disabilities, smart device ownership, and mobile health app use.
Feasibility metrics
A six-item feasibility metric questionnaire was created by the research team to better understand participant feedback. Metrics related to task completion and time of completion were collected for every task; other metrics were only answered when applicable to task. Completion time did include any time that research staff was asked for assistance and assisted. Feasibility metrics were answered as yes (Y) or no (N). Answers were determined by the research staff collecting data.
•
Was the task completed?
•
Was the acoustic task successfully recorded?
•
Did the acoustic task have to be re-recorded?
•
Was a headset used?
•
What was the time of completion?
•
Did the participant ask for assistance?
Exit survey
A 6-item interview-style questionnaire was given to participants at the end of the study completion to better understand participants engagement/interaction and to gauge general feedback. Exit survey questions, and subsequent follow up questions, were modified from the Mobile App Rating Scale (MARS) from the Functionality and Engagement sections (
12
). Responses were qualitative and not scored on a scale.
1.
How easy were the tasks prompts to understand?
•
Was the vocabulary, wording, and grammar clear, unambiguous, and appropriate?
•
Did you have to go back and reread the prompt to understand was it was asking for?
2.
How easy was the app to interact with?
•
Did you understand how to interact with the app to successfully complete the tasks?
•
Were the interactions consistent and intuitive?
•
Did you understand whether you had completed a task correctly, how to progress to the next screen, etc.?
3.
Was the app interesting/engaging for you to use?
4.
Did you find the tasks physically difficult or taxing to perform?
5.
Did you find the tasks mentally difficult or taxing to perform?
6.
Was the interface physically difficult to interact with (e.g., taps, swipes, pinches, scrolls)?
Data collection
Participants were brought to a private clinic room and introduced to the app on a study iPad by a member of the research staff. Each participant was asked to complete a one to three tasks followed by the feedback interview for a total time of less than 20 min. Participants were then informed of what task(s) they would be completing, that they would be timed from when they began until they had completed the task, and that a research member would be available for assistance/questions if needed. Participants were instructed to wear a headset with microphone if the task included voice recording, as per the Bridge2AI-Voice suggested standards, and begin. A research member observed and timed the participant. At completion of each task, time to completion and feasibility metrics were recorded. The research personnel then began the exit survey questionnaire with participants in which qualitative responses were recorded. This procedure was completed for each task the participant completed. Participants were limited to 1–3 tasks at a time, in which task assignment depended on the length of the task and the time available by the participant. Audio data was not collected at this stage of the app development. Current research by the consortium is attempting to outline techniques and recommend appropriate protocols for quality voice data collection in future iterations of the app as well as other clinical research involving voice data collection (
13
).
PDSA model of improvement
We employed the Plan-Do-Study-Act (PDSA) model for three phases of data collection (
14
). The PDSA model is a four-step iterative approach for quality improvement and is widely used in quality improvement initiatives. The model begins with a strategy to assess improvement approaches (Plan), followed by a small-scale trial of data collection (Do). The study team evaluates and gains insight from the outcomes (Study), determining whether to implement alterations or initiate a new cycle of improvement (Act). This model was used to inform the app development team of weaknesses or concerns observed by the research team during participant completion. The phases in this study consisted of 10–20 participants per cycle. Minimal yet effective improvements related to recruitment, data recording, and interface changes were made. None of the improvements made affected participant data collection and subsequent metric measurement. No major changes to features or content were made to the app during the research study timeline.
Results
Overview
47 participants were recruited over a two-month enrollment period. Participant characteristics are shown in
Table 2
. Mean and median age was 58.3 and 64, respectively. 61.7% were female, 91.5% spoke English as a primary language, 55.3% held a bachelor's or graduate degree, and 40.4% were employed. 36% of participants had a self-reported disability, most commonly reporting a physical, visual, or auditory impairment/deficit. 100% of participants owned a smart device, with 49% using a mobile health application currently. Over 20 primary referral diagnoses were reported by participants in
Table 3
.
Table 2.
Participant characteristics (
n
= 47).
Characteristic
Value
b
Age in years, median (range)
58.3 (19–92)
Gender,
n
(%)
Male
18 (38.3%)
Female
29 (61.7%)
Highest Level of Education,
n
(%)
High School Diploma
7 (14.9%)
Some College
8 (17%)
Associate's Degree
6 (12.8%)
Bachelor's Degree
17 (36.2%)
Graduate Degree
9 (19.1%)
Primary Language,
n
(%)
English
43 (91.5%)
Other
a
4 (8.5%)
Employment Status,
n
(%)
Student
3 (6.4%)
Employed
19 (40.4%)
Retired
21 (44.7%)
Unemployed
1 (2.3%)
Disability
3 (6.4%)
Self-Reported Disability Status,
n
(%)
Yes
17 (36.2%)
No
33 (63.8%)
Own a smartphone or tablet?
n
(%)
Yes
47 (100%)
No
0 (0%)
Do you use a mobile health application?
n
(%)
Yes
23 (48.9%)
No
24 (51.1%)
Open in a new tab
a
Other languages included Spanish, Mandarin, Bengali, and Thai.
b
Percentages may not sum to 100% due to rounding.
Table 3.
Participant voice diagnoses.
Irritable Larynx Syndrome
Chronic Cough
Vocal Cord Paralysis
Vocal Cord Hypomobility
Vocal Cord Leukoplakia
Muscle Tension Dysphonia
Interstitial Lung Disease
Chronic Obstructive Pulmonary Disease
Spasmodic Dysphonia
Velopharyngeal Insufficiency
Recurrent Respiratory Papillomatosis
Sulcus
Oropharyngeal Dysphagia
Asthma
Amyloidosis
Gastroesophageal Reflux Disease
Presbyphonia
Vocal Cord Paresis
Vocal Cord Scarring
Current/Post Tracheostomy Tube
History of Glottic Cancer/High Grade Dysplasia
Open in a new tab
Three PDSA cycles were completed. There were 15 participants for PDSA 1, 20 participants for PDSA 2, 12 participants for PDSA 3. PDSA 1 focused on improving recruitment practices of participants, PDSA 2 focused on improving the research staff assistance, and PDSA 3 focused on improving feedback relaying to the app development team. Alpha- and beta-testing as well as app updates were ongoing throughout this study period.
Feasibility metrics
There was a total of 29 different questionnaires and tasks at the time of data collection (
Table 1
). The “confounders” questionnaire was stratified into 5 different “tasks”, thus a total of 34 “tasks” were available to complete. The 47 participants completed a total of 68 tasks. Of these 68, only 46 (68%) were able to successfully complete the task as instructed. Moreover, of these 68 tasks, 32 fell under the “acoustic” category in which an audio recording by the participant was required. 13 (41%) were able to successfully complete as instructed. Participants asked for assistance by the research staff 41.2% of the time, often asking multiple times for an individual task. Notably, the Glides task (i.e., a task requiring moving from high to low and low to high pitches) required assistance 100% of the time. A total of 19 participants asked for assistance on one or multiple tasks with a mean age of 63.8. Those who asked for assistance were mainly female (66.6%), employed (46.7%), and held a bachelor's degree (43.3%).
Table 1
reports the average completion time for each task. When all current task average completion times were added together, the total completion time of all tasks in the app was approximately 51 min and 30 s. The longest task to complete was the DSM-5 Adult survey and the shortest task to complete was the Voice Problem Severity scale.
Exit survey
Upon completion of the 2-month study period, 47 participants completed the interview-style questionnaire.
The user responses reflected a favorable perception of a voice-screening app and its features, with one participant saying, “I am excited for this app to be ready one day. I would definitely use something like this with my condition”.
Moreover, responses also highlighted the utility of the application as it currently stands. One user mentions that “[they] thought it was very easy and intuitive to complete”. However, a majority of users made comments in regard to the current interface and/or with the clarity of the instructions. Many users emphasized the need for more explicit instructions regarding how to audio record the task, how to play back the recorded audio, or even when to record. One user says “I couldn't remember what the scale meant, and I had to keep scrolling back up to remind myself what it meant and then scroll way back down to where I left off” in regard to the PTSD Adult survey. Beyond this, some users felt that some of the survey and questionnaire tasks on the app were dense and difficult to engage with, making some tiresome to complete. One user notes that “I felt that the questionnaire had too many questions on the screen and could have been made into two pages” in regard to the DSM-5 Adult questionnaire.
Additionally, user responses pointed out different ways to improve the app design and experience. While the app is currently in its base model, with design and aesthetic being developed, participants suggested different modalities that could potentially reduce mental exhaustion. One user suggested an incorporation of some motivational elements to better user engagement.
Discussion
Principal findings
The Bridge2AI-Voice consortium developed and pilot-tested a novel mobile application designed for eventual voice data collection to improve voice data research. This study aimed to assess the practicality and utility of this application through task performance metrics and participant discussion. Results highlight that the feasibility of utilizing the data collected through this app presents both promises and challenges that need to be addressed.
While completion rates did vary across tasks, the majority of users were able to successfully complete the tasks as instructed indicating a certain level of usability. However, a majority of the acoustic tasks that would require audio collection were unsuccessfully completed, which is a very important finding to consider as we eventually aim to transition data collection in the remote setting, without assistance from research personnel. With Bridge2AI-Voice's ultimate goal of introducing at-home data collection with this mobile app, this highlights a concern that needs to be addressed. If voice and audio tasks were unable to be performed, subsequently the app would be collecting insufficient voice and audio data, weakening the diversity of the database and consequently the AI/ML models to be trained. Addressing this fundamental issue needs to be
a priori
ty for the Bridge2AI-Voice consortium in order to ensure the best voice practices and its technology are being employed in order to capture of the best audio samples. Through results of this feasibility study, a special focus on user experience/user interface (UX/UI) was initiated, with the addition of a UX/UI expert to the team for further iterations of the app.
Furthermore, based on the feasibility metrics, the task completion time remains a barrier for this application to be an efficient screening tool. As it currently stands, the summed average time for completion of all tasks available on the app is 51 min and 30 s. It's important to highlight this is subject to change as the app continues to be updated and modified, but if more elements are added to the protocol, it is reasonable to assume that this total completion time is to increase. However, one goal of the app is to ideally bundle tasks and surveys when appropriate in relation to a user's disease status. Regardless, this raises concern about user fatigue and engagement sustainability. This fatigue experienced often towards the latter half of surveys and tasks has been shown to reduce the quality of responses or even lead to premature termination of participation, potentially leading to nonresponse bias (
15
,
16
). Factors known to influence this phenomenon include survey length, survey topic, question complexity, and question type, with open ended questions contributing more to exhaustion (
16
). As the app continues to go through new iterations, it's critical to understand the quality of the responses being received from the app task protocol early on. Users have already expressed dissatisfaction with the length and density of the surveys and questionnaires, highlighting a necessity to create a more streamlined protocol to ensure the best quality of responses. Moreover, the recorded completion time may be affected if the surveys and tasks are not being authentically answered by participants, resulting in a potential under- or overestimation of the test parameter. Future beta-testing in the app should attempt to better understand the influence fatiguability has on response quality and completion time by having participants complete more tasks by bundling tasks across the app. While there is no universal standard for mobile health apps and the time it takes to complete certain protocols, one goal of the application protocol should be to reduce user burden while still collecting sufficient comprehensive data.
With respect to the exit survey interviews, participants were receptive to this mobile application as a future screening tool and support the utility of voice-screening tools in disease diagnosis, screening, and maintenance based on participant opinions. However, a recurrent theme that presented itself from feedback was that there is a need for more explicit instructions and the incorporation of a more better user experience (UX). This ambiguity of task instructions poses a significant challenge to the app's utility, as exemplified by the large, measured percentage of participants that required assistance. Since the app was developed by clinicians and scientists, some of the language used in the surveys, questionnaires, and audio tasks could reflect a higher reading level. Future iterations of the app should investigate the current reading level using existing tools, like the Flesch-Kincaid readability tests, and seek to match the health literacy of the general population (
17
). The addition of questionnaire to gauge healthy literacy may help better understand the population this app intends to serve. Beyond instruction clarity, there was a call by participants for a more user-friendly interface. The app as it stands is in the process of developing its aesthetic and translating that into the UX. However, we are seeing very early into beta-testing how common comments are on the design of an app and can affect the UX by participants. Once the interface is thoroughly developed, future beta-testing should include more questions from the MARS questionnaire to evaluate UX. Addressing these concerns can further optimize the user experience, potentially improving some of the metric measures.
Audio data has already been shown to serve a potential diagnostic tool in patients with certain disease states that can present with unique vocal changes, including Parkinson's disease, chronic obstructive pulmonary disease (COPD), diabetes, chronic pain, and laryngeal cancer to name a few (
18
–
22
). However, without adequate high-quality voice samples, the reliability and accuracy of voice biomarker research may be restricted. The integration of voice-based assessments into digital health tools has the potential to advance early disease detection, continuous disease monitoring, and personalized treatment strategies. For example, an individual using a voice-assessment tool through an app may be identified to have softened consonants, abnormal silences and monotonous speech may point to recommended evaluation for Parkinson's disease (
18
). Through ongoing refinement, the Bridge2Ai-Voice app attempts to bridge the gap between research and clinical utility.
Limitations
This study is not without limitations. Firstly, this feasibility study was conducted at a single site. Consequently, this leads to a small sample size (
n
= 47) without geographical diversity, as evidenced by some of the demographic information collected (i.e., primary language, gender). The homogeneity reflected in our demographics greatly limits the generalizability. As the app continues to be modified and early feedback is incorporated, research around the practicality and utility of this app needs to expand to other sites within the consortium to have a more robust, diverse study population to better understand the measured metrics and interview responses as they relate to different subgroups of the populations. As a feasibility pilot, this study did not have a control arm and thus we were unable to test the true efficacy or other measured metrics between groups. Moreover, the determination of whether a task was successfully completed or recorded was subjective as it was made at the decision of the research assistant. While the research assistants are trained on the tasks, a more structured framework with objective benchmarks for what is considered successful vs. unsuccessful in regard to completion and acoustic recording could help with future testing in pinpointing specific faults within the protocol.
Conclusions
The findings of this pilot feasibility study indicates that the Bridge2AI-Voice smartphone application shows promise as a tool for voice data collection. However, several challenges need to be addressed to enhance its practicality. Refinement of task instructions, interface design, and incorporation of engagement enhancement strategies are crucial for maximizing the app's utility in voice data collection. The smartphone app is need for further adaptation and refinement before large scale voice data collection can be implemented in real-world settings.
Funding Statement
The author(s) declare that financial support was received for the research and/or publication of this article. This work was supported by the National Institute for Health Grant #1OT2OD032720-01.
Contributor Information
Bridge2AI-Voice Consortium:
Yael Bensoussan
,
Olivier Elemento
,
Anais Rameau
,
Alexandros Sigaras
,
Satrajit Ghosh
,
Maria Powell
,
Vardit Ravitsky
,
Jean Christophe Belisle-Pipon
,
David Dorr
,
Phillip Payne
,
Alistair Johnson
,
Ruth Bahr
,
Donald Bolser
,
Frank Rudzicz
,
Jordan Lerner-Ellis
,
Kathy Jenkins
,
Shaheen Awan
,
Micah Boyer
,
William Hersh
,
Andrea Krussel
,
Steven Bedrick
,
Toufeeq Ahmed Syed
,
Jamie Toghranegar
,
James Anibal
,
Duncan Sutherland
,
Enrique Diaz-Ocampo
,
Elizabeth Silberhoz
,
John Costello
,
Alexander Gelbard
,
Kimberly Vinson
,
Tempestt Neal
,
Lochana Jayachandran
,
Evan Ng
,
Selina Casalino
,
Yassmeen Abdel-Aty
,
Karim Hanna
,
Theresa Zesiewicz
,
Elijah Moothedan
,
Emily Evangelista
,
Samantha Salvi Cruz
,
Robin Zhao
,
Mohamed Ebraheem
,
Karlee Newberry
,
Iris De Santiago
,
Ellie Eiseman
,
JM Rahman
,
Stacy Jo
, and
Anna Goldenberg
Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
Ethics statement
The studies involving humans were approved by University of South Florida Institutional Review Board. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.
Author contributions
EM: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Validation, Writing – original draft, Writing – review & editing, Supervision, Visualization. MB: Conceptualization, Data curation, Investigation, Methodology, Project administration, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing. SW: Conceptualization, Project administration, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing. YA-A: Supervision, Validation, Writing – review & editing. SG: Conceptualization, Project administration, Supervision, Validation, Visualization, Writing – review & editing. AR: Conceptualization, Project administration, Supervision, Validation, Visualization, Writing – review & editing. AS: Conceptualization, Project administration, Software, Supervision, Validation, Visualization, Writing – review & editing. OE: Conceptualization, Funding acquisition, Project administration, Resources, Supervision, Validation, Visualization, Writing – review & editing. YB: Conceptualization, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing.
Group members of Bridge2AI-Voice Consortium
University of South Florida, Tampa, FL, US: Yael Bensoussan. Weill Cornell Medicine, New York, NY, USA: Olivier Elemento. Weill Cornell Medicine, New York, NY, USA: Anais Rameau. Weill Cornell Medicine, New York, NY, USA: Alexandros Sigaras. Massachusetts Institute of Technology, Boston, MA, USA: Satrajit Ghosh. Vanderbilt University Medical Center, Nashville, TN, USA: Maria Powell. University of Montreal, Montreal, Quebec, Canada: Vardit Ravitsky. Simon Fraser University, Burnaby, BC, Canada: Jean Christophe Belisle-Pipon. Oregon Health & Science University, Portland, OR, USA: David Dorr. Washington University in St. Louis, St. Louis, MO, USA: Phillip Payne. University of Toronto, Toronto, Ontario, Canada: Alistair Johnson. University of South Florida, Tampa, FL, USA: Ruth Bahr. University of Florida, Gainesville, FL, USA: Donald Bolser. Dalhousie University, Toronto, ON, Canada: Frank Rudzicz. Mount Sinai Hospital, Sinai Health, University of Toronto, Toronto, ON, Canada: Jordan Lerner-Ellis. Boston Children's Hospital, Boston, MA, USA: Kathy Jenkins. University of Central Florida, Orlando, FL, USA: Shaheen Awan. University of South Florida, Tampa, FL, USA: Micah Boyer. Oregon Health & Science University, Portland, OR, USA: William Hersh. Washington University in St. Louis, St. Louis, MO, USA: Andrea Krussel. Oregon Health & Science University, Portland, OR, USA: Steven Bedrick. UT Health, Houston, TX, USA: Toufeeq Ahmed Syed. University of South Florida, Tampa, FL, USA: Jamie Toghranegar. University of South Florida, Tampa, FL, USA: James Anibal. New York, NY, USA: Duncan Sutherland. University of South Florida, Tampa, FL, USA: Enrique Diaz-Ocampo. University of South Florida, Tampa, FL, USA: Elizabeth Silberhoz Boston Children's Hospital, Boston, MA, USA: John Costello. Vanderbilt University Medical Center, Nashville, TN, USA: Alexander Gelbard. Vanderbilt University Medical Center, Nashville, TN, USA: Kimberly Vinson. University of South Florida, Tampa, FL, USA: Tempestt Neal. Mount Sinai Health, Toronto, ON, Canada: Lochana Jayachandran. The Hospital for Sick Children, Toronto, ON, Canada: Evan Ng. Mount Sinai Health, Toronto, ON, Canada: Selina Casalino. University of South Florida, Tampa, FL, USA: Yassmeen Abdel-Aty. University of South Florida, Tampa, FL, USA: Karim Hanna. University of South Florida, Tampa, FL, USA: Theresa Zesiewicz. Florida Atlantic University, Boca Raton, FL, USA: Elijah Moothedan. University of South Florida, Tampa, FL, USA: Emily Evangelista. Vanderbilt University Medical Center, Nashville, TN, USA: Samantha Salvi Cruz. Weill Cornell Medicine, New York, NY, USA: Robin Zhao. University of South Florida, Tampa, FL, USA: Mohamed Ebraheem. University of South Florida, Tampa, FL, USA: Karlee Newberry. University of South Florida, Tampa, FL, USA: Iris De Santiago. University of South Florida, Tampa, FL, USA: Ellie Eiseman. University of South Florida, Tampa, FL, USA: JM Rahman. Boston Children's Hospital, Boston, MA, USA: Stacy Jo. Hospital for Sick Children, Toronto, ON, Canada: Anna Goldenberg.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declare that no Generative AI was used in the creation of this manuscript.
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
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Opinion

Voice as an AI Biomarker of Health—Introducing Audiomics

Voice, speech, and respiratory sounds provide impor-
tant clinical insights into patients’ health status. In the
age of artificial intelligence (AI), patients’ audio record-
ings are being investigated as digital biomarkers for early
detection of a broad range of conditions, including la-
ryngeal pathology, neurological and psychological dis-
orders, head and neck cancers, and diabetes. Besides
neurologists, speech language pathologists, and inter-
nists, otolaryngologists also have unique perspectives
and expertise on voice, speech, and respiratory sounds
that can fuel this line of research and innovation.

While the potential of using voice as a biomarker of
health has been explored for decades, development
of recent technology such as machine learning (ML)
(a branch of AI focusing on predictive algorithms that
learn from data without explicit instructions) allows for
efficient analysis of a voice data and makes discovery
of scalable acoustic biomarkers a possibility. The im-
pact of such a discovery on patient care could be sub-
stantial, including in screening, diagnosis, remote moni-
toring, and development of new digital end points for
clinical trials.

Although the future of voice biomarkers is promis-
ing, there remain important limitations to broad inte-
gration into clinical care. Within academic research, many
studies1,2 remain at the level of proof of concept with
small- to medium-sized datasets often using voice as the
only data type. Comparing studies and pooling data are
challenging tasks due to the lack of standards in how we
collect voice and speech data. Within the industry, data-
sets are often private, limiting the ability to audit ML
models and the accuracy of training data labels.

Herein, we highlight the need to define and create
standards for “audiomics,” the interdisciplinary field of
audio analysis applied to biomedicine to identify unique
audio biomarkers of health and disease. Recent ad-
vances in data science have set the stage for the emer-
gence of new “omics,” that is, data science subfields
founded on a large amount of data representing the
structure or function of a biological system.3 Examples
of such omics subfields include radiomics, genomics,
proteomics, and, more recently, videomics, referring
to the application of omics in video analysis, such as
endoscopy.4 Videomics was developed for gastrointes-
tinal endoscopy for lesion identification during colonos-
copy and has recently been expanded to endoscopy vid-
eos in otolaryngology.5 Central to omics are efforts
toward standardization of data collection, storage, and
analysis and ongoing discussion of ethical and regula-
tory challenges, which will take simple acoustic analy-
sis to a new level. Otolaryngologists have a rare oppor-
tunity with audiomics to contribute to cutting-edge
medicine by sharing how voice, speech, and respira-
tory sounds may be interpreted in the context of hu-
man health and by guiding the collection of acoustic data

at scale to offer populationwide solutions. Tangible use
cases in otolaryngology include voice screening for de-
tection and monitoring of laryngeal cancer in at-risk
populations for timely referral, or remote voice moni-
toring in spasmodic dysphonia for assessing botulinum
toxin response to fine-tune and personalize treatment.
Among digital biomarkers, voice, speech, and
respiratory sounds are particularly attractive due to
their noninvasive, accessible, and low-cost collection in
the setting of recording capability via computers or
smartphones.1 These digital data have come in focus in
the age of automated speech recognition systems and
large language models, with unparalleled ability to ana-
lyze voice and speech. Furthermore, digital biomarkers
are increasingly sought as clinical data outcome mea-
sures for therapeutics, particularly in the pharmaceuti-
cal industry. Such data could overcome limitations of
clinical trials and research ethics concerns, such as time
required for participant enrollment, challenges related
to intervention delivery, data collection burden, and lack
of diversity among enrolled participants.

Many industry giants, such as Pfizer, Mozilla,
Amazon, Google, and Apple, and start-ups are invest-
ing millions in research and development of voice,
speech, and respiratory sound biomarkers.6 Although
the industry has access to vast amounts of voice data
from users, it lacks access to accurate clinical data or
demographic information. This deficiency limits the abil-
ity to annotate data for AI training and to compare
algorithmic outputs with criterion standard medical as-
sessment and prevents external auditing of datasets
used for training to ensure fair representation of di-
verse populations.

Despite significant advances, important limitations
currently prevent implementation of voice biomarkers
in clinical care. Despite considerable literature1,2 pub-
lished especially in the past decade, there is a lack of pro-
spective study and validation of AI algorithms in audiom-
ics on external datasets. These factors, compounded by
the limited size, quality, and diversity of existing open-
source datasets, likely explain the lack of validated and
US Food and Drug Administration–approved AI algo-
rithms for disease detection and monitoring in this space.
When ML applications from screening and diagnosis of
laryngeal cancer were considered, researchers found that
most studies reported training datasets of fewer than
300 patients, and only 2 studies used more than 1 data
modality to train their models, limiting generalization of
the results.7 Furthermore, audiomics will inevitably need
to be integrated in multi-omics efforts, in which audio
data are analyzed together with other health data, such
as clinical, imaging, and even genomic data, for im-
proved accuracy. When a patient presents with a dys-
phonic voice, we clinicians inquire about duration of
symptoms and smoking history to gauge risk of laryn-

VIEWPOINT

Yaël Bensoussan, MD,
MSc
USF Health Voice
Center, Department of
Otolaryngology–Head
& Neck Surgery,
University of
South Florida Health
Morsani College of
Medicine, Tampa.

Olivier Elemento, PhD
Englander Institute for
Precision Medicine,
Weill Cornell Medicine,
New York, New York.

Anaïs Rameau, MD,
MPhil
Sean Parker Institute
for the Voice,
Department of
Otolaryngology–Head
and Neck Surgery,
Weill Cornell Medicine,
New York, New York.

Multimedia

Corresponding
Author: Yaël
Bensoussan, MD, MSc,
Division of
Laryngology, USF
Health Voice Center,
Department of
Otolaryngology–Head
& Neck Surgery,
University of
South Florida Morsani
College of Medicine,
13330 USF Laurel Dr,
Tampa, FL 33609
(yaelbensoussan@usf.
edu).

jamaotolaryngology.com

(Reprinted) JAMA Otolaryngology–Head & Neck Surgery April 2024 Volume 150, Number 4

283

Downloaded from jamanetwork.com by University of Colorado user on 12/05/2025

© 2024 American Medical Association. All rights reserved.
© 2024 American Medical Association. All rights reserved.


Opinion Viewpoint

geal cancer. Machine learning models trained on voice data need the
same type of multimodal integration to achieve scalable accuracy.
For audiomics to be implemented and reach its full public health
potential, the following tripartite agenda must be met: (1) stan-
dards for audiomics data acquisition, interoperability, and AI readi-
ness must be defined; (2) diverse teams with broad skills and ex-
pertise, including clinicians, engineers, bioethicists, and social
scientists, must collaborate in this complex field; and (3) the unique
ethical and legal challenges in audiomics linked to the Health Insur-
ance Portability and Accountability Act, potential reidentification
of patients by their voice, voice hacking, and data ownership call for
a new governance framework for issues concerning diversity, data
handling, and privacy safeguards.

Through the Bridge2AI program, an endeavor to leverage team
science in AI sponsored by the National Institutes of Health Com-
mon Fund, our team, Bridge2AI-Voice, gathered 50 multidisci-
plinary experts from 12 North American institutions to generate
high-quality, ethically sourced datasets for biomedical research
to advance the field of audiomics. Besides our primary deliverable
to build a publicly available database of 30 000 human voices, our

team aims to address current gaps in the burgeoning field of au-
diomics by (1) ensuring quality and accuracy of acoustic data and as-
sociated clinical data through clinical validation and evidence-
based recording protocols; (2) aiming for interoperability of data by
creating new bioinformatics standards for voice data; (3) creating
benchmarks of diversity metrics and minimizing risk of algorithmic
bias; (4) defining ethical and legal norms safeguarding patients’ data
while supporting data-sharing efforts; (5) developing infrastruc-
ture for audiomics data storage sharing within and across research
institutions; and (6) formulating training pathways for scientists, en-
gineers, bioethicists, and clinicians to develop skills in audiomics.

The only scalable way to achieve such a complex endeavor and
implement change beyond the current hype around acoustic bio-
markers is to bring experts from different fields, including otolar-
yngologists, speech pathologists, data scientists, AI engineers, bio-
ethicists, and acousticians, to collaborate and build a solid, ethically
sourced infrastructure for voice data collection linked to other health
data. In this way, the Bridge2AI-Voice data generation project serves
as a model from which current and future generations of audiom-
ics researchers can learn.

ARTICLE INFORMATION

Published Online: February 22, 2024.
doi:10.1001/jamaoto.2023.4807

Conflict of Interest Disclosures: Dr Elemento
reported owning equity in Owkin and in Volastra
Therapeutics outside the submitted work.
Dr Rameau reported receiving grants from the
National Institute on Aging and owning equity
in Perceptron Health, Inc, and Savorease, Inc,
outside the submitted work. No other disclosures
were reported.

Funding/Support: All authors are funded
by Bridge2AI award OT2 OD032720 from the
National Institutes of Health Common Fund.

Role of the Funder/Sponsor: The National
Institutes of Health had no role in the preparation,
review, or approval of the manuscript or the
decision to submit the manuscript for publication.

Additional Contributions: We thank the following
members of the Bridge2AI-Voice Collaborators
for their contributions to the Bridge2AI program:
Drs Bensoussan, Elemento, and Rameau as well
as Jean-Christophe Bélisle-Pipon, PhD, Faculty of
Health Science, Department of Health Ethics,
Simon Fraser University; David A. Dorr, MD, MS,

Department of Health Informatics and Clinical
Epidemiology, Oregon Health Sciences University;
Satrajit S. Ghosh, PhD, McGovern Institute,
Massachusetts Institute of Technology; Alistair
Johnson, PhD, Division of Biostatics, Hospital for
Sick Children; Philip R. O. Payne, PhD, Institute
for Informatics, Data Science and Biostatistics,
Washington University School of Medicine in
St Louis; Maria Powell, PhD, CCC-SLP, Department
of Otolaryngology–Head & Neck Surgery, Vanderbilt
University Medical Center; Vardit Ravitsky, PhD,
The Hastings Center; and Alexandros Sigaras, MS,
Englander Institute for Precision Medicine,
Weill Cornell Medicine. None were directly
compensated for their contributions.

REFERENCES

1. Fagherazzi G, Fischer A, Ismael M, Despotovic V.
Voice for health: the use of vocal biomarkers from
research to clinical practice. Digit Biomark.
2021;5(1):78-88. doi:10.1159/000515346

2. Idrisoglu A, Dallora AL, Anderberg P,
Berglund JS. Applied machine learning techniques
to diagnose voice-affecting conditions and
disorders: systematic literature review. J Med
Internet Res. 2023;25:e46105. doi:10.2196/46105

3. Micheel CM, Nass SJ, Omenn GS, et al.
Omics-based clinical discovery: science, technology,
and applications. In: Evolution of Translational
Omics: Lessons Learned and the Path Forward.
National Academies Press; 2012. doi:10.17226/13297

4. Dai X, Shen L. Advances and trends in omics
technology development. Front Med (Lausanne).
2022;9. doi:10.3389/fmed.2022.911861

5. Paderno A, Holsinger FC, Piazza C. Videomics:
bringing deep learning to diagnostic endoscopy.
Curr Opin Otolaryngol Head Neck Surg. 2021;29(2):
143-148. doi:10.1097/MOO.0000000000000697

6. Apple introduces new features for cognitive
accessibility, along with Live Speech, Personal
Voice, and Point and Speak in Magnifier. Apple
Newsroom (Canada). May 16, 2023. Accessed
July 24, 2023. https://www.apple.com/ca/
newsroom/2023/05/apple-previews-live-speech-
personal-voice-and-more-new-accessibility-
features/

7. Bensoussan Y, Vanstrum EB, Johns MM III,
Rameau A. Artificial intelligence and laryngeal
cancer: from screening to prognosis: a state of the
art review. Otolaryngol Head Neck Surg. 2023;168
(3):319-329. doi:10.1177/01945998221110839

284

JAMA Otolaryngology–Head & Neck Surgery April 2024 Volume 150, Number 4 (Reprinted)

jamaotolaryngology.com

Downloaded from jamanetwork.com by University of Colorado user on 12/05/2025

© 2024 American Medical Association. All rights reserved.


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Source ID: nih_reporter_project
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NIH RePORTER Project
Source: https://reporter.nih.gov/project-details/11376382
Application ID: 11376382
Project number: 3OT2OD032720-01S3
Core project number: OT2OD032720
Title: Bridge2AI: Voice as a Biomarker of Health - Building an ethically sourced, bioaccoustic database to understand disease like never before
Principal investigator: BENSOUSSAN, YAEL EMILIE
Organization: UNIVERSITY OF SOUTH FLORIDA
Fiscal year: 2025
Award amount: 4660942
Project start: 2022-09-01T00:00:00
Project end: 2026-11-30T00:00:00

Our group aims to integrate the use of voice as biomarker of health in clinical care by generating a substantial multi-institutional, ethically sourced, and diverse voice database linked to multimodal health biomarkers to fuel voice AI research and build predictive models to assist in screening, diagnosis, and treatment of a broad range of diseases. Data collection will be made possible by software through a smartphone application linked to electronic health records (EHR) and other health biomarkers such as radiomics, and genomics, and supported by federated learning technology to protect data privacy.
             Based on the existing literature and ongoing research in different fields of voice research, our group has identified 5 disease categories for which voice changes have been associated to specific diseases and around which we aim to center the data acquisition efforts:
1.	Vocal Pathologies (Laryngeal cancers, Vocal fold paralysis, Benign laryngeal lesions)
2.	Neurological and Neurodegenerative Disorders (Alzheimer’s, Parkinson’s, Stroke, ALS)
3.	Mood and Psychiatric Disorders (Depression, Schizophrenia, Bipolar Disorders)
4.	Respiratory disorders (Pneumonia, COPD, Heart Failure, OSA)
5.	Pediatric diseases (Autism, Speech Delay)
Specific Aim #1: Data Acquisition Module:
-	To build a multi-modal, multi-institutional, large scale, diverse and ethically sourced human voice database linked to other biomarkers of health that is AI/ML friendly to fuel voice AI research
Specific Aim #2: Standard Module:
-	To introduce the field of acoustic biomarkers by developing new standards of acoustic and voice data collection and analysis for voice AI research.
Specific Aim #3: Tool Development and optimization
-	To develop a software and cloud infrastructure for automated voice data collection through a smartphone application that allows non-invasive, user-friendly, high quality voice data collection while minimizing human manipulation. This will include integrated acoustic amplifiers and acoustic quality standardization.
-	To implement Federated Learning technology to allow analysis of multi-institutional data while minimizing data sharing and preserving patient privacy
Specific Aim #4: Ethics Module
-	To integrate existing scholarship, tools, and guidance with development of new standard and normative insights for identifying, anticipating, addressing, and providing guidance on ethical and trustworthy issues from voice data generation and AI/ML research and development to clinical adoption and downstream health decisions and outcomes.
-	To develop new guidelines for consenting to voice data collection, voice data sharing and utilization in the context of voice AI technology
Specific Aim # 5: Teaming Module:
-	To build bridges between the medical voice research world, the acoustic engineers, and the AI/ML world to promote the integration of tangible clinical application for Voice AI algorithms
Specific Aim #6: Skills and Workforce Development Module
-	To develop a unique curriculum on voice biomarkers of health and the development, validation, and implementation for AI models that are FAIR and CARE
-	To create a community of voice AI researchers, especially those from underserved communities, and foster collaborations to promote application of ML for Voice Research
-	To engage a broad range of learners with competency assessment and mentorship

As Voice is increasingly being recognized as a biomarker of health by the tech world and Voice AI is gaining attention from  multi-nationals such  as Google, Amazon, Mozilla  and Apple  amongst others, many important issues related to patient privacy protection, ethical and fair representation of population, and clinical accuracy are arising. As a multidisciplinary group of academic experts, we aim to influence and guide the world of Voice AI by ensuring patient protection through ethical and fairness principles and create safe, innovative infrastructures to disseminate ethically sourced data for the future generations of Voice AI researchers.

Preferred terms:
Acoustics;Address;Adoption;Alzheimer's Disease;Amplifiers;Apple;Attention;Benign;Biological Markers;Bipolar Disorder;Bridge to Artificial Intelligence;Categories;Childhood;Chronic Obstructive Pulmonary Disease;Clinical;Cloud Computing;Collaborations;Communities;Competence;Computer software;Consent;Data;Data Analyses;Data Collection;Data Protection;Databases;Development;Diagnosis;Disease;Educational Curriculum;Electronic Health Record;Engineering;Ensure;Ethics;FAIR principles;Fostering;Friends;Future Generations;Generations;Genomics;Guidelines;Health;Heart failure;Human;Infrastructure;Institution;Larynx;Lesion;Link;Literature;Malignant neoplasm of larynx;Medical;Mental Depression;Mental disorders;Mentorship;Mood Disorders;Nervous System Disorder;Neurodegenerative Disorders;Outcome;Paralysed;Parkinson Disease;Pathology;Patients;Pneumonia;Population;Research;Research Personnel;Respiration Disorders;Schizophrenia;Scholarship;Source;Speech Delay;Standardization;Stroke;Technology;Validation;Voice;Voice Quality;Workforce Development;artificial intelligence algorithm;artificial intelligence model;artificial intelligence technology;autism spectrum disorder;clinical application;clinical care;data acquisition;data preservation;data privacy;data sharing;federated learning;innovation;insight;multidisciplinary;multimodality;patient privacy;predictive modeling;privacy protection;radiomics;research and development;screening;skill acquisition;smartphone application;software infrastructure;tool;tool development;trustworthiness;underserved community;user-friendly;vocal cord


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Bridge2AI - Voice
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Impact
Publications
Press
Social Media
Bridge2AI-Voice Dataset
The
Bridge2AI-Voice
(B2Ai-Voice) dataset is a large, ethically sourced, and demographically diverse voice dataset linked to health information, released by the NIH’s Bridge2AI initiative. The dataset includes many derived voice recordings (such as spectrograms and other acoustic features) rather than raw audio in the public release, along with detailed participant metadata: clinical diagnoses (including voice, neurological, mood, respiratory disorders), validated questionnaires, and demographics. It is collected from multiple sites across North America, with version 3.0 containing ~61,937 voice-derived recordings from 833 adult participants. The pediatric dataset v1.0 is now available containing data from 300 participants. Access to the original raw audio is restricted to controlled access due to privacy concerns. Access can be requested by emailing
[email protected]
B2AI-Voice v3.1.0 adult dataset and v1.1.0 pediatric dataset now available (with voice upon request)
Access the Flagship B2Ai-Voice Dataset
Register for Adult Dataset Access via PhysioNet
Register for Pediatric Dataset Access via PhysioNet
How to get data access
Featurized dataset
Featurized Adult and Pediatric Datasets are Available under Registered Access
The adult and pediatric datasets are available through separate PhysioNet links under registered access. Credentialed users must be approved and sign DUA. Visit PhysioNet to begin process.
Adult Dataset
Pediatric Dataset
Datasets with Audio Data
Available under controlled access
Users interested in access the audio data from any Bridge2AI-Voice dataset can request access by emailing
[email protected]
Raw audio data is disseminated through controlled access only to protect participants’ privacy.
Documentation
Training opportunities for using the dataset:
https://www.b2aivoicescholars.org/
.
Overview
Collection Methods
Data Governance
Study Metadata
Healthsheet
Data Pre-Processing
AI-Readiness
Bridge2AI-Voice is a Precision Public Health grand challenge project funded by the NIH Common Fund Bridge2AI Program. Bridge2AI-Voice seeks to create a flagship, standardized, and ethically sourced dataset of 10,000 voices linked to health information to fuel research and discovery in voice biomarkers.
Our group aims to promote integration of voice as a biomarker of health in clinical care. To do so, we will generate a large multi-institutional, ethically sourced, and diverse voice dataset linked to multimodal health biomarkers to fuel voice AI research. Data collection is performed via a novel app (The Bridge2AI-Voice App) available as a smartphone application linked to electronic health records (EHR). The app collects breathing sounds and voice, speech, and linguistic tasks, along with a considerable amount of health information through surveys and validated questionnaires. Other multimodal data collected includes imaging, genomics, and respiratory function tests, among others. The consortium is also addressing the growing ethical, legal, and social challenges surrounding voice AI, including risks of voice re-identification, vulnerabilities like voice AI hacking, concerns around voice data sharing and privacy, and the influence of gender and racial diversity on the development and application of these technologies.
Our best ethical practices, developed through ethical inquiry, have guided the development of the voice collection protocol as well as data dissemination practices.
As voice is increasingly recognized as a biomarker of health by the tech world and voice AI is gaining attention from multinationals such as Google, Amazon, Mozilla, and Apple, many important issues related to patient privacy protection, ethical and fair representation of populations, and clinical accuracy are arising. As a multidisciplinary group of academic experts, we aim to influence and guide the world of voice AI by ensuring patient protection through ethical and fairness principles and by creating safe, innovative infrastructures to disseminate ethically sourced data for future generations of voice AI researchers.
Based on the existing literature and ongoing research in different fields of voice research, our group has identified 5 disease cohort categories for which voice changes have been associated with specific diseases with well-recognized unmet needs and for which our data acquisition efforts are focused:
Voice Disorders
Neurological and Neurodegenerative Disorders
Mood and Psychiatric Disorders
Respiratory disorders
Pediatric Voice and Speech Disorders
Please Note:
The public data releases do not contain an equal distribution of these categories of diseases. Further releases will contain additional data.
Data Access
Adult Dataset
A derived dataset containing spectrograms and combined phenotypic data is available on PhysioNet under a permissioned access mechanism. Registration on PhysioNet and signing of a data use agreement will enable access. Raw audio is available under a controlled access mechanism. The latest version of the dataset is available at the following URL:
Bridge2AI-Voice: An ethically-sourced, diverse voice dataset linked to health information
Pediatric Dataset
The Bridge2AI Voice consortium has also prepared a pediatric dataset. To access the Bridge2AI Voice pediatric dataset please click here
Bridge2AI-Voice Pediatric Dataset
Older Versions
An earlier version of the feature-only dataset is available on HealthDataNexus, which provides cloud compute alongside the dataset rather than allowing data downloads.
Request Data Access to v1.0 via Health Data Nexus
Data is collected across five disease categories. The initial data release contains data collected from four of the five categories.
Participants are recruited across different academic institutions from “high volume expert clinics” based on diagnosis and inclusion/exclusion criteria outlined below
(
Table 1
)
.
Pediatric Participants:
Pediatric participants are recruited strictly from the Hospital for Sick Children (SickKids) and are grouped by age.
High Volume Expert Clinics:
Outpatient clinics within hospital systems or academic institutions that have developed an expertise in a specific disease area and see more than 50 patients per month from the same disease category. Ex: Asthma/COPD pulmonary specialty clinic.
Data is collected in the clinic with the assistance of a trained research assistant. Future data collection will also occur remotely; however, remote data collection did not occur for the initial dataset release. Voice samples are collected prospectively using a custom software application (the Bridge2AI-Voice App) with the Bridge2AI-Voice protocols.
For Pediatrics, all data is collected using
reproschema-ui
with the Bridge2AI-Voice pediatric protocol.
Clinical validation:
Clinical validation is performed by a qualified physician or practitioner based on established gold standards for diagnosis
(
Table 1
)
.
Acoustic Tasks:
Voice, breathing, cough, and speech data are recorded with the app for adults and with reproschema-ui for pediatrics. A total of 22 acoustic tasks are recorded through the app
(
Table 2
)
.
Demographic surveys and confounders:
Detailed demographic data and surveys about confounding factors such as smoking and drinking history are collected through the smartphone application.
Validated Questionnaires:
The Bridge2AI-Voice protocols contain validated tools and questionnaires for each disease category within the app for data collection
(
Table 3
)
.
Other Multimodal Data:
The rest of the multimodal data, including imaging, genomic data (for the neuro cohort), laryngoscopy imaging, and other EHR data, is extracted from different sites independently and will be uploaded through the REDCap database. Please note that no external data is released in this v3.0.0 release.
Please see the following publication for a description of protocol development:
Bensoussan, Yael, et al. “Developing Multi-Disorder Voice Protocols: A team science approach involving clinical expertise, bioethics, standards, and DEI.” Proc. Interspeech 2024. 2024.
https://www.isca-archive.org/interspeech_2024/bensoussan24_interspeech.html
.
The supporting REDCap data dictionary, metadata, and instrument PDFs are available at
https://github.com/eipm/bridge2ai-redcap
.
When using the REDCap data dictionary and metadata, please cite:
Bensoussan, Y., Ghosh, S. S., Rameau, A., Boyer, M., Bahr, R., Watts, S., Rudzicz, F., Bolser, D., Lerner-Ellis, J., Awan, S., Powell, M. E., Belisle-Pipon, J.-C., Ravitsky, V., Johnson, A., Zisimopoulos, P., Tang, J., Sigaras, A., Elemento, O., Dorr, D., … Bridge2AI-Voice. (2024). eipm/bridge2ai-redcap. Zenodo.
https://zenodo.org/doi/10.5281/zenodo.12760724
.
Protocols can be found in the Bridge2AI-Voice documentation of the dataset for each cohort in the following:
Voice Disorders
Respiratory
Mood/Psychiatric
Neurological
Controls
Pediatrics
Peds 10+
Peds 6-10
Peds 4-6
Peds 2-4
Table 1 – Disease cohort inclusion/exclusion criteria and validation methods
Disease Cohort
Diagnosis
Inclusion Criteria
Exclusion Criteria
Gold Standard Validation Methods
Voice Disorders Cohort
Laryngeal Cancer
Active laryngeal cancer T1-T4 Biopsy proven (if no biopsy at time of collection and suspicion is very high, provider will have to go back to confirm in the clinical validation section after the biopsy is obtained)
Previously treated laryngeal cancer with no evidence of disease on scope
Laryngoscopy Images Stroboscopy Videos
Voice Disorders Cohort
Laryngitis
Acute laryngitis Chronic laryngitis Bacterial laryngitis Fungal laryngitis Autoimmune laryngitis *need to have evidence of information of the vocal cords on laryngoscopy as well as dysphonia
Subjective laryngitis without evidence on scope
Laryngoscopy Images Stroboscopy Videos
Voice Disorders Cohort
Pre-cancerous lesions
Keratosis Leukoplakia (note if with or without dysplasia)
Low grade –
Laryngoscopy Images Stroboscopy Videos
Voice Disorders Cohort
Benign Lesions of the vocal cord (nodule, polyp, cyst)
Vocal fold nodules Vocal fold polyp Vocal fold cyst Reinke’s Edema Vocal fold ulcers Recurrent respiratory Papilloma Fibrous mass Rheumatoid nodules Recurrent Laryngeal Papilloma (RLP)
Patient has received surgery for any condition and does not have evidence of pathology when scoped Immediate post op prior less than 30 days from laryngeal surgery
Laryngoscopy Images Stroboscopy Videos
Voice Disorders Cohort
Muscle Tension Dysphonia (MTD)
Laryngology & SLP diagnosis
Laryngoscopy Images Stroboscopy Videos
Voice Disorders Cohort
Spasmodic Dysphonia/Laryngeal Tremor
Adductor laryngeal dystonia (ADLD) previously called spasmodic dysphonia Abductor laryngeal dystonia (ABLD) Vocal tremor Mixed laryngeal dystonia Singer’s laryngeal dystonia (SLD) Adductor laryngeal spasms during inspiration (ARLD)
Laryngoscopy Images Stroboscopy Videos
Voice Disorders Cohort
Unilateral Vocal Fold Paralysis
Laryngology & SLP diagnosis
Vocal fold paresis Bilateral VF paralysis Immediately or less than 2 weeks post injection Immediately post-op thyroplasty (less than a month)
Laryngoscopy Images Stroboscopy Videos CT scan Spirometry
Voice Disorders Cohort
Glottic insufficiency/presbyphonia
Patients with glottic gap on STROBOSCOPY due to vocal fold atrophy related to aging, rapid weight loss, severe illness, or other causes
Patients with glottic gap 2nd to unilateral paresis or paralysis
Respiratory Disorders Cohort
Airway Stenosis: Bilateral Vocal fold paralysis, Supraglottic stenosis, Glottic stenosis, Posterior glottic stenosis, subglottic stenosis, tracheal stenosis, multi-level upper airway stenosis
Nasopharyngeal stenosis
Spirometry and flow volume loops CT scan of neck/chest
Respiratory Disorders Cohort
Chronic Cough
bothersome cough > 8 weeks, negative for exclusion criteria
Cough less than 8 weeks, current smoking, lung/laryngeal cancer, COPD, asthma, GERD, bronchiectasis, TB infection, pneumonia, pulmonary granuloma, idiopathic pulmonary fibrosis, ACE inhibitor use, eosinophilic bronchitis, tracheomalacia, upper airway cough syndrome, laryngitis, chest Xray/CT indicative of airway foreign body
Spirometry and flow volume loops
Neurological and Neurodegenerative Disorders
Mild Cognitive Impairment (MCI)
clinical diagnosis of MCI/ cognitive decline/short term memory loss/cognitively impaired Being over the age of 44 and under 85 Able to read, speak the English language
Not having a clinical diagnosis Being less than the age of 44 and above 85 Unable to speak and read the English language Having had a surgical intervention significantly altering the symptoms of the disease studied
CT Brain MRI Brain Whole Genome Sequencing
Neurological and Neurodegenerative Disorders
Alzheimer’s disease (AD)
clinical diagnosis of AD Being over the age of 44 and under 85 Able to read, speak the English language
Not having a clinical diagnosis Being less than the age of 44 and above 85 Unable to speak and read the English language Having had a surgical intervention significantly altering the symptoms of the disease studied
CT Brain MRI Brain Serum Bloodmarker Ptau proteins Whole Genome Sequencing
Neurological and Neurodegenerative Disorders
Other types of Dementia
clinical diagnosis of frontotemporal dementia/ Lewy body dementia/ vascular dementia/ mixed dementia/ alcohol induced dementia (to make a note in diagnosis form) Being over the age of 44 and under 85 Able to read, speak the English language
Not having a clinical diagnosis Being less than the age of 44 and above 85 Unable to speak and read the English language Having had a surgical intervention significantly altering the symptoms of the disease studied
CT Brain MRI Brain Whole Genome Sequencing
Neurological and Neurodegenerative Disorders
Amyotrophic Lateral Sclerosis (ALS)
clinical diagnosis of sporadic ALS/ Familial ALS/ Spinal or limb-onset ALS/ Bulbar-onset ALS Being over the age of 44 and under 85 Able to read, speak the English language
Not having a clinical diagnosis Being less than the age of 44 and above 85 Unable to speak and read the English language Having had a surgical intervention significantly altering the symptoms of the disease studied
CT Brain MRI Brain Whole Genome Sequencing
Neurological and Neurodegenerative Disorders
Parkinson’s Disease (PD)
clinical diagnosis of idiopathic PD/multiple system atrophy/progressive Supranuclear palsy/ Corticobasal degeneration/dementia with Lewy bodies/other or atypical parkinsonism Parkinson’s patients enrolled in Deep Brain Stimulation studies (to make a note of this in diagnosis form)
Not having a clinical diagnosis Being less than the age of 44 and above 85 Unable to speak and read the English language Having had a surgical intervention significantly altering the symptoms of the disease studied
CT Brain MRI Brain Whole Genome Sequencing
Mood and Psychiatric Disorders
Alcohol or Substance Use Disorder
Co-morbid with depression, bipolar disorder and anxiety disorder.
Not applicable
Electronic health summary; clinical diagnosis from psychiatrist; medication history
Mood and Psychiatric Disorders
Anxiety Disorder
Existing clinical diagnosis of anxiety and/or currently experiencing an anxious episode.
Not having a clinical diagnosis
Electronic health summary; clinical diagnosis from psychiatrist; medication history
Mood and Psychiatric Disorders
Attention-Deficit/Hyperactivity Disorder (ADHD)
Co-morbid with depression, bipolar disorder and anxiety disorder.
Q-Mood-ADHD Adult questionnaire is part of part B mood cohort
Electronic health summary; clinical diagnosis from psychiatrist; medication history
Mood and Psychiatric Disorders
Autism Spectrum Disorder (ASD)
Co-morbid with depression, bipolar disorder and anxiety disorder.
Not applicable
Electronic health summary; clinical diagnosis from psychiatrist; medication history
Mood and Psychiatric Disorders
Bipolar Disorder
Existing clinical diagnosis of bipolar I or II and/or currently experiencing a manic/depressive episode.
Not having a clinical diagnosis
Electronic health summary; clinical diagnosis from psychiatrist; medication history
Mood and Psychiatric Disorders
Borderline Personality Disorder
Co-morbid with depression, bipolar disorder and anxiety disorder.
Not applicable
Electronic health summary; clinical diagnosis from psychiatrist; medication history
Mood and Psychiatric Disorders
Depression or Major Depressive Disorder
Existing clinical diagnosis of depression and/or currently experiencing a depressive episode.
Not having a clinical diagnosis
Electronic health summary; clinical diagnosis from psychiatrist; medication history
Mood and Psychiatric Disorders
Eating Disorder (ED)
Co-morbid with depression, bipolar disorder and anxiety disorder.
Not applicable
Electronic health summary; clinical diagnosis from psychiatrist; medication history
Mood and Psychiatric Disorders
Insomnia/Sleep Disorder
Co-morbid with depression, bipolar disorder and anxiety disorder.
Not applicable
Electronic health summary; clinical diagnosis from psychiatrist; medication history
Mood and Psychiatric Disorders
Obsessive-Compulsive Disorder (OCD)
Co-morbid with depression, bipolar disorder and anxiety disorder.
Not applicable
Electronic health summary; clinical diagnosis from psychiatrist; medication history
Mood and Psychiatric Disorders
Panic Disorder
Co-morbid with depression, bipolar disorder and anxiety disorder.
Not applicable
Electronic health summary; clinical diagnosis from psychiatrist; medication history
Mood and Psychiatric Disorders
Post-Traumatic Stress Disorder (PTSD)
Co-morbid with depression, bipolar disorder and anxiety disorder.
Q-Mood-PTSD Adult questionnaire is part of part B of mood cohort
Electronic health summary; clinical diagnosis from psychiatrist; medication history
Mood and Psychiatric Disorders
Schizophrenia
Co-morbid with depression, bipolar disorder and anxiety disorder.
Not applicable
Electronic health summary; clinical diagnosis from psychiatrist; medication history
Mood and Psychiatric Disorders
Social Anxiety Disorder
Co-morbid with depression, bipolar disorder and anxiety disorder.
Q-Mood-GAD7 questionnaire is part of part B of mood cohort
Electronic health summary; clinical diagnosis from psychiatrist; medication history
Mood and Psychiatric Disorders
Other Psychiatric Disorder
Not applicable
Not applicable
Electronic health summary; clinical diagnosis from psychiatrist; medication history
Table 2 – Acoustic Tasks in Protocol
Name
Task
Description
Part
Non Voice/Non Speech
Respiration Part A [
Example
]
Breathing sounds
A
Non Voice/Non Speech
Cough part A [
Example
]
Voluntary Cough
A
Non Voice/Non Speech
Breath Sounds [
Example
]
Breathing sounds
Resp
Non Voice/Non Speech
Voluntary Cough [
Example
]
Voluntary Cough
Resp
Voice/Non Speech
Prolonged Vowel [
Example
]
vowel /e/
A
Voice/Non Speech
Maximum Phonation Time [
Example
]
vowel /e/
A
Voice/Non Speech
Glides [
Example
]
Lowest to Highest /e/
A
Voice/Non Speech
Loudness [
Example
]
/Hey/
A
Voice/Non Speech
Diadochokinesis [
Example
]
/pa/ta/ka/ /buttercup/
A
Speech
Rainbow Passage [
Example
]
validated passage
A
Speech
Caterpillar Passage [
Example
]
validated passage
Voice
Speech
Cape-V Sentences [
Example
]
validated sentences
Voice
Speech
Free Speech Part A [
Example
]
open questions
A
Speech
Picture Description [
Example
]
describing a picture
A
Speech
Free Speech Voice [
Example
]
open questions
Voice
Speech
Story Recall [
Example
]
speech after reading a story
A
Speech
Animal Fluency [
Example
]
name animals
Mood
Speech
Open Response Questions [
Example
]
open questions
Mood
Speech
Word-Color Stroop [
Example
]
color descriptions
Neuro
Speech
Productive Vocabulary [
Example
]
describing images
Neuro
Speech
Random Item generation [
Example
]
describing images
Neuro
Speech
Cinderella Story [
Example
]
story
Neuro
Speech
ABC’s
Recalling alphabet
Peds
Speech
Ready For School
Recalling a typical day preparing for School
Peds
Speech
Favorite Show
Recalling favorite shows
Peds
Speech
Favorite Food
Describing their favorite food
Peds
Speech
Outside of School
After school activities description
Peds
Speech
Months
Listing the months
Peds
Speech
Counting
Counting
Peds
Speech
Naming Animals
Listing animals
Peds
Speech
Naming Food
Listing foods
Peds
Speech
Identifying Pictures
Picture identification
Peds
Speech
Picture Description (Pediatrics)
describing a picture
Peds
Voice/Non Speech
Long Sounds
Sustained /ee/ and /ah/ sounds
Peds
Voice/Non Speech
Noisy Sounds
/jj/ /ah/ /ee/ /oo/ /sh/ /ss/ /muh/ /nuh/ /zz/ /hh/
Peds
Speech
Caterpillar Passage (Pediatrics)
validated passage
Peds
Speech
Repeat Words
Word repetition
Peds
Speech
Role naming
recalling days, months, and counting from 60 – 70
Peds
Speech
Repeat Sentences
Sentence Recall
Peds
Voice/Non Speech
Silly Sounds
/PUH/ /TUH/ /KUH/ /PUH TUH KUH/
Peds
Table 3 – Validated Questionnaires integrated into App
Validated Questionnaire
Voice Disorders
Respiratory
Mood/Psychiatric
Neurological
Controls
Pediatrics
Example
Voice Handicap Index-10 (VHI-10)
X
X
X
X
X
PDF
Patient Health Questionnaire (PHQ-9)
X
X
X
X
X
PDF
General Anxiety Disorder (GAD-7)
X
X
X
X
X
PDF
Positive and Negative Affect Schedule (PANAS)
X
X
PDF
Custom Affect scale
X
X
PDF
Post-Traumatic Stress Disorder Test (PTSD) Adult
X
X
PDF
Attention Deficit and Hyperactivity Disorder Questionnaire (ADHD-Adult)
X
X
PDF
The Diagnostic and Statistical Manual of Mental Disorders (DSM-5 Adult)
X
X
PDF
Dyspnea Index (DI)
X
X
PDF
Leicester Cough Questionnaire (LCQ)
X
X
PDF
Winograd Questionnaire
X
X
PDF
Montreal Cognitive Assessment (MOCA)*
X
X
PDF
Children’s Voice Handicap Index-10 (C-VHI-10)
X
PDF
Pediatric Voice Outcomes Survey (PVOS)
X
PDF
Pediatric Voice-Related Quality-of-Life (PVRQOL)
X
PDF
Patient Health Questionnaire modified for Adolescents (PHQ-A)
X
PDF
Accessing the Dataset
The feature-only and raw audio datasets are available under distinct agreements appropriate for the sensitivity of their respective content.
Registered Access (features-only data):
Register on PhysioNet and confirm your identity (
Registered Access License
).
Sign the Bridge2AI-Voice Registered Access Agreement, which outlines the terms and conditions for data use.
Controlled Access (raw audio data):
Complete the Data Access Request Form (DARF)
Complete the Data Use Agreement (DUA)
Submit your application to the Data Access Compliance Office for review
Upon approval, ensure a Data Use and Transfer Agreement (DTUA) is signed by an authorized official at your institution.
Memorandum: Ethical Justification for Controlled Access to Raw Voice Data Samples
Click the button below to download a copy of the memorandum explaining the reasoning behind the governance structure.
Download - Memorandum PDF
Oversight
Has the clinical study been reviewed and approved by at least one human subjects’ protection review board?
Submitted and approved by the USF Single IRB and subsite IRBs through the Single IRB process.
Is this clinical study for a drug product?
No
Is this clinical study for a medical device?
No
Was a data monitoring committee appointed for this study?
No
De-Identification Levels
Level of de-identification for this dataset: Identifiable information (under HIPAA and the Common Rule), as well as data considered sensitive, have been removed from this dataset.
Does this dataset remove direct identifiers?
Yes
Does this dataset apply the HIPAA de-identification rules?
Yes
Does this dataset rebase and/or replace dates by integers?
Yes
Does this dataset remove or generalize geographic information?
Yes
Does this dataset remove narrative text fields?
Yes
Does this dataset achieve K-anonymization (k>=2)?
No
De-identification Details
All direct identifiers were removed, as these would reveal the identity of the research participant. These include name, civic address, and social security numbers. Indirect identifiers were removed where these created a significant risk of participant re-identification, for example through their combination with other public data available on social media, in government registries, or elsewhere. These include select geographic or demographic identifiers, as well as some information about household composition or cultural identity. Non-identifying elements of data that revealed highly sensitive information, such as information about household income, mental health status, traumatic life experiences, and the like, were also removed. All raw voice data was removed, as this data has the potential to cause individual re-identification or to be used for illicit or unauthorized purposes.
Consent
Consent Type
Does this dataset allow only the non-commercial use of the data?
No
Does this dataset allow only the use of the data in a specific geographic location?
No
Does this dataset allow only the use of the data for a specific type of research?
No
Does this dataset allow only the use of the data for genetic research?
No
Does this dataset allow only the use of the data for research that does not involve the development of methods or algorithms?
No
Consent Details
Research data that does not contain your direct identifiers will be shared with external researchers for future research through a secure database. Data that poses a low risk of causing individual re-identification will be shared in registered access with the general public. Data that would pose a heightened risk of re-identification if shared in full open access will be shared through a controlled access mechanism with authorized researchers.
Official Title: Bridge2AI-Voice
Design: Study Type – Observational
Enrollment Count (Anticipated by 2027): 10,000
Design Observation Model
Cohort
Design Time Perspective
Cross-sectional
Biospecimens: Respiratory, Voice and speech samples
Biospecimens Description:
The Bridge2AI-Voice dataset contains samples from conventional acoustic tasks including respiratory sounds, cough sounds, and free speech prompts, capturing voice, speech and language data relating to health. Participants who consent are asked to perform speaking tasks and complete self-reported demographic and medical history questionnaires, as well as disease-specific validated questionnaires. Participants who consent also permit investigators to access medical information through EHR platforms in order to perform gold standard validation of diagnoses and symptoms.
Eligibility
Sex
All
Gender Based
No
Minimum Age
18 years (this will change when pediatric cohort is introduced, and metadata will be updated to reflect new eligibility criteria)
Maximum Age
120 years
Healthy Volunteers
Yes
Inclusion Criteria
See Collection Methods – Table 1
Exclusion Criteria
Does not read or speak English (Please note, the Spanish protocols and Data collection will be included in future releases)
See Collection Methods – Table 1
Study Population
The current v.2.0.0 dataset contains only adult populations. As the study progresses, a pediatric cohort will be introduced. Inclusion/exclusion criteria and additional information regarding the dataset and study metadata will be updated at that time. In addition, the current dataset contains fluent English speakers but will expand to include data collection in Spanish.
Sampling Method
Non-Probability Sample
Identification Information
Organization Study ID
OT2OD032720
Organization Study Type
U.S. National Institutes of Health (NIH) Grant/Contract Award Number
Secondary ID:
ID
Link
OT2OD032720
https://reporter.nih.gov/search/4XtcXzBGEkWQlwG5v8odBA/project-details/10858564
Collaborators
University of South Florida
Weill Cornell Medicine
Oregon Health & Science University
Massachusetts Institute of Technology
University of Toronto
Mount Sinai Hospital
Hospital for Sick Children
Simon Fraser University
The Hastings Center
Washington University in St. Louis
University of Florida
Vanderbilt University Medical Center
URL How to CiteIf you use this dataset for any purpose, please cite the resources specified in the Bridge2AI-Voice documentation for version 2.0.0 of the dataset at (URL)
Bridge2AI-Voice Consortium (2024). Flagship Voice Dataset from the Bridge2AI-Voice Project (2.0.0) [Dataset].
Contact For any questions, suggestions, or feedback related to this dataset, please email
[email protected]
AcknowledgementBridge2AI-Voice is supported by NIH grant OT2OD032720 through the NIH Bridge2AI Common Fund program.
General Information
The Bridge2AI Voice dataset aims to enable the development, benchmarking, or validation of clinically applicable machine-learning models for diagnosing a wide range of health conditions using voice data, including vocal pathologies, neurological, psychiatric, respiratory, and pediatric voice disorders. This dataset contains voice recordings and key metadata, and it is structured to be Findable, Accessible, Interoperable, and Reusable (FAIR).
Has the dataset been audited before? If yes, by whom and what are the results?
The dataset has been audited internally for missingness and consistency by the data release team. A missingness table is included with the dataset. Certain aspects of the data (e.g., transcription) were generated using off-the-shelf models that have not been audited for correctness.
Dataset Versioning
Does the dataset get released as static versions or is it dynamically updated?
Static
Does the current version/subversion of the dataset come with predefined task(s), labels, and recommended data splits (e.g., for training, development/validation, testing)? If yes, please provide a high-level description of the introduced tasks, data splits, and labeling, and explain the rationale behind them. Please provide the related links and references. If not, is there any resource (website, portal, etc.) to keep track of all defined tasks and/or associated label definitions? (please note that more detailed questions w.r.t labeling is provided in further sections)
Yes, the current version of the dataset comes with predefined tasks and labeling. The tasks are primarily designed for training machine-learning models for disease detection and classification using voice data. Labels include diagnostic categories such as vocal pathologies, neurological disorders, psychiatric conditions, and respiratory disorders. However, there are no predefined recommended data splits for training, validation, or testing. Researchers are encouraged to create their own data splits based on their specific requirements. More details regarding task definitions and labeling can be found in the dataset.
Motivation
For what purpose was the dataset created? Was there a specific task in mind? Was there a specific gap that needed to be filled? Please provide a description.
The Bridge2AI Voice dataset was created to address a gap in the availability of large-scale, diverse, and well-documented voice data for use in clinical machine-learning applications. Previous studies on machine learning-based voice diagnosis produced promising results, but their sample sizes were too small, or they lacked the key metadata needed for training robust, clinically useful models. The dataset aims to bridge this gap by providing an ethically sourced, large, and diverse dataset to develop, benchmark, or validate clinically applicable AI/ML models. The goal is to facilitate the use of voice as a non-invasive, cost-effective biomarker for the screening, diagnosis, and monitoring of a wide range of health conditions.
What are the applications that the dataset is meant to address? (e.g., administrative applications, software applications, research)
The Bridge2AI Voice dataset is primarily intended for research applications, specifically in the development of AI and machine-learning models for healthcare. It aims to support clinical research in disease screening, diagnosis, and monitoring through voice biomarkers. The dataset can be used for AI model pretraining, fine-tuning, benchmarking, or validation.
Are there any types of usage or applications that are discouraged from using this dataset? If so, why?
Yes, there are restrictions on the use of this dataset, as detailed in the Registered Data Access Agreement. These restrictions reflect the B2AI-Voice Consortium’s commitment to advancing ethical and trustworthy research practices that respect and protect the rights and interests of research participants. Accordingly, the dataset is intended solely for commercial and non-commercial research purposes by Authorized Researchers. Specifically, the dataset is not to be used a) to attempt to re-identify research participants, nor any actions that could reasonably lead to re-identification; and b) for any purpose that could foreseeably cause harm or stigmatization to research participants, their families, communities, or specific populations. Lastly, intellectual property protections, database rights, or related rights may not be used in a manner that restricts or limits access to any part of the dataset or to any conclusions derived from it. This restriction ensures that future use of the dataset remains unrestricted, in alignment with the Open Science principles upheld by the B2AI-Voice Consortium. Specifically, the dataset should not be used for non-research applications, such as hiring decisions, insurance premium adjustments, or any form of surveillance that could lead to discrimination or harm. These limitations are intended to prevent unethical or biased outcomes that could negatively impact individuals based on their health conditions or voice characteristics.
Who created this dataset (e.g., which team, research group), and on behalf of which entity (e.g., company, institution, organization)?
Voice as a Biomarker of Health is being co-led by Dr. Yaël Bensoussan, MD, MSc, from USF Health Morsani College of Medicine and Olivier Elemento, PhD, from Weill Cornell Medicine, who are co-principal investigators for the project, which is funded by the NIH Common Fund within the Bridge2AI Program. The project also includes lead investigators from 10 other universities in North America; Alexandros Sigaras, MSc and Anaïs Rameau, MD, MPhil (Weill Cornell Medicine), Maria Powell, CCC-SLP, PhD (Vanderbilt University Medical Center), Ruth Bahr, CCC-SLP, PhD (USF Health Morsani College of Medicine), Jennifer Sui, MD (Hospital for Sick Children), Philip Payne, PhD (Washington University in St. Louis), David Dorr, MD (Oregon Health & Science University), Jean-Christophe Bélisle-Pipon, PhD (Simon Fraser University), Vardit Ravitsky, PhD (The Hastings Center), Satrajit Ghosh, PhD (Massachusetts Institute of Technology), Frank Rudzizc, PhD (University of Toronto), Jordan Lerner-Ellis, PhD (Sinai Health) and Don Bolser, PhD (University of Florida). There are over 50 other investigators, clinicians, scholars, and trainees who have contributed to the development of this dataset. Please see full list of collaborators here:
The Bridge2AI-Voice Consortium (2024)
Who funded the creation of the dataset? If there is an associated grant, please provide the name of the grantor and the grant name and number. If the funding institution differs from the research organization creating and managing the dataset, please state how.
The NIH Common Fund
3TF-OT2ActfOD032720Projectf01S1
What is the distribution of backgrounds and experience/expertise of the dataset curators/generators?
The curators and generators of the Bridge2AI Voice dataset come from a diverse range of backgrounds and areas of expertise, reflecting the interdisciplinary nature of the project. The team includes:
Clinicians and Healthcare Professionals
: Practicing doctors and healthcare workers involved in the direct collection of clinical data and providing practical insights into the medical relevance of the dataset.
Biomedical Researchers
: Experts in clinical medicine, neurology, and psychiatry, contributing deep knowledge of the medical conditions being studied.
Machine Learning and AI Specialists
: Researchers and engineers with expertise in machine learning, artificial intelligence, and data science, focusing on developing models and algorithms for analyzing voice data.
Data Scientists and Statisticians
: Professionals skilled in data curation, preprocessing, and statistical analysis, ensuring the dataset is robust and suitable for machine learning applications.
Social Scientists and Ethicists
: Experts in ethics, sociology, and human subjects research, ensuring the dataset is ethically sourced and meets standards for privacy and consent.
Engineers and Technologists
: Individuals with experience in software development, systems engineering, and data infrastructure, contributing to the technical aspects of data collection, storage, and dissemination.
Data Composition
What do the instances that comprise the dataset represent (e.g., documents, images, people, countries)? Are there multiple types of instances? Please provide a description.
Each instance represents a person.
How many instances are there in total (of each type, if appropriate) (breakdown based on schema, provide data stats)?
There are currently around 833 instances.
How many patients/subjects does this dataset represent? Answer this for both the preliminary dataset and the current version of the dataset.
833
Does the dataset contain all possible instances, or is it a sample (not necessarily random) of instances from a larger set? If the dataset is a sample, then what is the larger set? Is the sample representative of the larger set (e.g., geographic coverage)? If so, please describe how this representativeness was validated/verified. If it is not representative of the larger set, please describe why not (e.g., to cover a more diverse range of instances, because instances were withheld or unavailable). Answer this question for the preliminary version and the current version of the dataset in question.
It is a sample of a larger, ongoing collection. The data is not representative because it was collected at a limited number of geographic locations. We hope to make it more representative by shifting to remote collection and designing our recruiting approach in a way that controls for more variables.
What data modality does each patient data consist of? If the data is hierarchical, provide the modality details for all levels (e.g., text, image, physiological signal). Break down all levels and specify the modalities and devices.
Audio recordings, questionnaire responses.
What data does each instance consist of? “Raw” data (e.g., unprocessed text or images) or features? In either case, please provide a description.
Raw audio and questionnaire response data, as well as extracted audio features.
Is any information missing from individual instances? If so, please provide a description, explaining why this information is missing (e.g., because it was unavailable).
Yes, some questions are optional. There may be data collection irregularities that caused some information to be missing from individual instances. Each individual answered a common set of questions and then responded to additional questions relevant to their primary diagnostic category.
Are relationships between individual instances made explicit? (e.g., They are all part of the same clinical trial, or a patient has multiple hospital visits, and each visit is one instance)? If so, please describe how these relationships are made explicit.
No, they are unrelated.
Are there any errors, sources of noise, or redundancies in the dataset? If so, please provide a description. (e.g., losing data due to battery failure, or in survey data subjects skip the question, radiological sources of noise).
Yes, different sites have different collection configurations. The collection protocol changed over the course of the study.
Is the dataset self-contained, or does it link to or otherwise rely on external resources (e.g., websites, other datasets)? If it links to or relies on external resources:
a. Are there guarantees that they will exist, and remain constant, over time?
NA
b. Are there official archival versions of the complete dataset (i.e., including the external resources as they existed at the time the dataset was created)?
NA
c. Are there any restrictions (e.g., licenses, fees) associated with any of the external resources that might apply to a future user? Please provide descriptions of all external resources and any restrictions associated with them, as well as links or other access points, as appropriate.
It is self-contained.
Does the dataset contain data that might be considered confidential (e.g., data that is protected by legal privilege or by doctor-patient confidentiality, data that includes the content of individuals’ non-public communications that is confidential)? If so, please provide a description.
No
Does the dataset contain data that, if viewed directly, might be offensive, insulting, threatening, or might otherwise pose any safety risk (such as psychological safety and anxiety)? If so, please describe why.
This dataset includes the transcription of free speech tasks. While the inclusion of information of that type is unlikely, it cannot be completely avoided, as research participants are responsible for their choice of language.
If the dataset has been de-identified, were any measures taken to avoid the re-identification of individuals? Examples of such measures: removing patients with rare pathologies or shifting time stamps.
This dataset has been de-identified through removal of all audio data and certain sensitive fields identified by a team of ethicists.
Does the dataset contain data that might be considered sensitive in any way (e.g., data that reveals racial or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history)? If so, please provide a description.
Yes:
racial or ethnic origins:
The dataset includes race information.
sexual orientations:
The dataset includes sexual orientation information.
financial or health data:
The dataset includes socioeconomic and health information.
Devices and Contextual Attributes in Data Collection
For data that requires a device or equipment for collection or the context of the experiment, answer the following additional questions or provide relevant information based on the device or context that is used (for example)
Data is collected on iPads (9th or 10th generation), iPad Air (5th generation) using an Avid AE-36 microphone and an Apple dongle to connect it to the iPad.
Challenges in Testing and Confounding Factors
Which factors in the data might limit the generalization of potentially derived models? Is this information available as auxiliary labels for challenge tests? For instance:
a. Number and diversity of devices included in the dataset.
Distinct iPad devices were used at each site.
b. Data recording specificities, e.g., the view for a chest x-ray image.
The data were recorded with a head-mounted headset with a microphone that could be at slightly different distances. The clinical diagnosis, depending on the disorder, was performed by one clinician or based on an EHR record or prescription.
c. Number and diversity of recording sites included in the dataset.
There are five recording sites included in the dataset.
d. Distribution shifts over time.
Changes in diagnostic criteria or practices could be a source of distribution shift.
What confounding factors might be present in the data?
Noise artifacts, variations in diagnostic practices, inaccurate questionnaire responses, underreporting.
What confounding factors might be present in the data?
Noise artifacts, variations in diagnostic practices, inaccurate questionnaire responses, underreporting.
a. Interactions between demographic or historically marginalized groups and data recordings, e.g., were women patients recorded in one site, and men in another?
Groups that have less trust in the medical system, AI, or are less proximal to the collection sites would have been less likely to be recruited.
b. Interactions between the labels and data recordings, e.g. were healthy patients recorded on one device and diseased patients on another?
Participants were screened for different disorders based on site, so they also had their data collected with different devices.
Collection and use of demographic information
Does the dataset identify any demographic sub-populations (e.g., by age, gender, sex, ethnicity)?
Age, Gender, Sex, Ethnicity, Socioeconomic status
Pre-processing / de-identification
Was there any pre-processing for the de-identification of the patients? Provide the answer for the preliminary and the current version of the dataset.
Yes, the data were extracted from the raw audio to limit re-identification and only the extracted features are being released with the dataset.
Was there any pre-processing for cleaning the data? Provide the answer for the preliminary and the current version of the dataset.
No
Was the “raw” data (post de-identification) saved in addition to the preprocessed/cleaned data (e.g., to support unanticipated future uses)? If so, please provide a link or other access point to the “raw” data.
Yes, it is saved and is not accessible publicly.
Were instances excluded from the dataset at the time of preprocessing? If so, why? For example, instances related to patients under 18 might be discarded.
No
Labeling and subjectivity of labeling
Is there an explicit label or target associated with each data instance? Please respond for both the preliminary dataset and the current version.
a. If yes:
What are the labels provided?
Who performed the labeling? For example, was the labeling done by a clinician, ML researcher, university or hospital?
Diagnostic labels are the result of a clinical assessment of the participant. At each site, a local clinician provided the diagnosis based on a clinical interview and appropriate work-up. For the psychiatric disorders cohort, this assessment was determined by using the participants EHR record or using an active prescription, which was done outside of data collection by an appropriately licensed clinician.
b. What labeling strategy was used?
Gold standard label available in the data (diagnosed by a clinician).
c. Human-labeled data:
How many labelers were considered?
Single labeler per data
What is the demographic of the labelers? (countries of residence, of origin, number of years of experience, age, gender, race, ethnicity, etc.)
Typically, the clinician at site of data collection, or external to (for prior diagnostic assessment)
What guidelines did they follow?
Per Bridge2AI Protocols and ICD-10 codes.
How many labelers provide a label per instance?
1
What is the human-level performance in the applications that the dataset is supposed to address?
It varies widely.
Is the software used to preprocess/clean/label the instances available? If so, please provide a link or other access point.
Yes.
https://github.com/sensein/b2aiprep
,
https://github.com/sensein/senselab
Is there any guideline that the future researchers are recommended to follow when creating new labels/defining new tasks?
The process for any new labels should be described alongside any release of a model or publication. This process should include exact variables used for this determination.
Collection Process
Were any REB/IRB approval (e.g., by an institutional review board or research ethics board) received? If so, please provide a description of these review processes, including the outcomes, as well as a link or other access point to any supporting documentation.
Yes
How was the data associated with each instance acquired? Was the data directly observable (e.g., medical images, labs, or vitals), reported by subjects (e.g., survey responses, pain levels, itching/burning sensations), or indirectly inferred/derived from other data (e.g., part-of-speech tags, model-based guesses for age or language)? If data was reported by subjects or indirectly inferred/derived from other data, was the data validated/verified? If so, please describe how.
The data was directly observable and reported by subjects. Clinical diagnoses were verified by clinicians reviewing audio and/or imaging data, looking at electronic health records, or medication prescriptions.
What mechanisms or procedures were used to collect the data (e.g., hardware apparatus or sensor, manual human curation, software program, software API)? How were these mechanisms or procedures validated? Provide the answer for all modalities and collected data. Has this information been changed through the process? If so, explain why.
The data were collected using an iPad app.
Who was involved in the data collection process (e.g., patients, clinicians, doctors, ML researchers, hospital staff, vendors, etc.) and how were they compensated (e.g., how much were contributors paid)?
Research teams, which may include medical, graduate, or undergraduate students, coordinated with clinicians/doctors to identify appropriate participants. These clinicians and doctors were listed under IRB as co-investigators, and were added to the consortium so that their names are included on consortium-level publications that emerge from the research. Hospital staff were not involved in scheduling but assisted in the logistics of coordinating data collection.
Participants were compensated for their time through electronic gift cards. Participants currently receive $40 for a data collection session that takes less than 90 minutes, and $80 for a session that takes over 90 minutes, for no more than a total of 3 sessions and maximum compensation of $120.
Over what timeframe was the data collected?
The data was collected over a period of 12 months.
Does the dataset relate to people?
Yes
Did you collect the data from the individuals in question directly, or obtain it via third parties or other sources (e.g., hospitals, app company)?
Directly
Were the individuals in question notified about the data collection?
Yes, participants went through an IRB-approved consent process.
Did the individuals in question consent to the collection and use of their data?
Yes, all participants have been duly informed and agreed to the collection and the use of their data via prospective informed consent.
If consent was obtained, were the consenting individuals provided with a mechanism to revoke their consent in the future or for certain uses?
Consenting participants are informed that they may withdraw from the study at any point. If a participant chooses to withdraw during or before the voice data collection, their data will not be included in the database. Participants are informed that research data (including voice recordings) cannot be removed from the database once the voice data collection process is completed.
In which countries was the data collected?
USA and Canada
Has an analysis of the potential impact of the dataset and its use on data subjects been conducted?
No
Inclusion Criteria-Accessibility in data collection
Is there any language-based communication with patients (e.g.: English, French)? If yes, describe the choices of language(s) for communication. (for example, if there is an app used for communication, what are the language options?)
English language was used for communication with study participants.
The only language option for v2.0.0 is English. Spanish versions of the protocol are under development.
What are the accessibility measurements and what aspects were considered when the study was designed and implemented?
The protocol asks about disabilities. Collection accessibility was facilitated through the normal means of the collection sites, including reading questions to participants when needed.
Uses
Has the dataset been used for any tasks already? If so, please provide a description.
A restricted version of the dataset containing raw audio has been used in the Bridge2AI Summer School and hackathon.
Does using the dataset require the citation of the paper or any other forms of acknowledgement? If yes, is it easily accessible through google scholar or other repositories
Bensoussan, Yael, et al. “Developing Multi-Disorder Voice Protocols: A team science approach involving clinical expertise, bioethics, standards, and DEI.” Proc. Interspeech 2024. 2024.
https://www.isca-archive.org/interspeech_2024/bensoussan24_interspeech.html
Is there a repository that links to any or all papers or systems that use the dataset? If so, please provide a link or other access point. (besides Google scholar)
No
Is there anything about the composition of the dataset or the way it was collected and preprocessed/cleaned/labeled that might impact future uses? For example, is there anything that a future user might need to know to avoid uses that could result in unfair treatment of individuals or groups (e.g., stereotyping, quality of service issues) or other undesirable harms (e.g., financial harms, legal risks) If so, please provide a description. Is there anything a future user could do to mitigate these undesirable harms?
Yes, this dataset has skews based on disorder category, site, and other demographic factors. Users should consider the multivariate distribution when assessing utility for different questions.
Are there tasks for which the dataset should not be used? If so, please provide a description.
Yes, there are certain applications that are discouraged from using this dataset. Specifically, the dataset should not be used for non-clinical applications such as hiring decisions, insurance premium adjustments, or any form of surveillance that could lead to discrimination or harm. These discouraged uses are intended to prevent unethical or biased outcomes that could negatively impact individuals based on their health conditions or voice characteristics. The dataset is intended strictly for research that prioritize patient safety, privacy, and ethical use.
Dataset Distribution
Will the dataset be distributed to third parties outside of the entity (e.g., company, institution, organization) on behalf of which the dataset was created?
The dataset will be distributed broadly to individuals outside of the entity who created the dataset.
How will the dataset be distributed (e.g., tarball on website, API, GitHub)? Does the dataset have a digital object identifier (DOI)?
The dataset will be distributed through a data publishing platform accessible at
https://healthdatanexus.ai/
This platform provides publicly accessible metadata regarding the dataset with a DOI for persistent resolution. The dataset itself requires registered access.
When was/will the dataset be distributed?
The data was published and made available at the end of November, 2024.
Assuming the dataset is available, will it be/is the dataset distributed under a copyright or other intellectual property (IP) license, and/or under applicable terms of use (ToU)? If so, please describe this license and/or ToU, and provide a link or other access point to, or otherwise reproduce, any relevant licensing terms or ToU, as well as any fees associated with these restrictions.
Users of the dataset must agree to terms laid out in the registered access agreement. The terms relating to intellectual property are repeated here for informational purposes only:
INTELLECTUAL PROPERTY RIGHTS. You understand and acknowledge that the Data may be protected by copyright and other rights, including other intellectual property rights. Duplication, as reasonably required to carry out Your Research Project with the Data, is nonetheless permitted. Sale of all or part of the Data on any media is not permitted. You recognize that nothing in this Agreement shall operate to transfer to You any intellectual property rights in or relating to the Data. You agree not to make intellectual property claims on the Data. You agree not to use intellectual property protection in ways that would prevent or block access to, or use of, any element of these Data, or conclusions drawn directly from the Data. You can elect to perform further research that would add intellectual and resource capital to the Data and decide to obtain intellectual property rights on these downstream discoveries. You agree to implement licensing policies that will not obstruct further research. You agree to respect the Fort Lauderdale Agreement.
There are no fees associated with these restrictions.
Have any third parties imposed IP-based or other restrictions on the data associated with the instances? If so, please describe these restrictions, and provide a link or other access point to, or otherwise reproduce, any relevant licensing terms, as well as any fees associated with these restrictions.
No IP-based restrictions have been imposed by third parties.
Do any export controls or other regulatory restrictions apply to the dataset or to individual instances? If so, please describe these restrictions, and provide a link or other access point to, or otherwise reproduce, any supporting documentation.
No export controls apply to the dataset.
Maintenance
Who is supporting/hosting/maintaining the dataset?
The dataset is supported by the NIH via the Bridge2AI project.
The dataset is hosted by the
Health Data Nexus
, a data publishing platform maintained by the Temerty Center for Artificial Intelligence Research and Education in Medicine (T-CAIREM) based at the University of Toronto. The Health Data Nexus maintains the technical infrastructure hosting dataset and provides continued access to interested researchers.
How can the owner/curator/manager of the dataset be contacted (e.g. email address)?
The platform team may be contacted through:
[email protected]
The curator of the data may be contacted through:
[email protected]
Is there an erratum? If so, please provide a link or other access point.
There is no erratum. A changelog for each dataset version is published online with the dataset metadata.
Will the dataset be updated (e.g., to correct labeling errors, add new instances, delete instances)? If so, please describe how often, by whom, and how updates will be communicated to users (e.g., mailing list, GitHub)?
Yes, further versions of the dataset will be released on a semi-annual (twice a year) basis. These updates will be distributed as new versions of the dataset on the Health Data Nexus platform. Users will be notified through news items on the platform as well as through standard communication channels.
If the dataset relates to people, are there applicable limits on the retention of the data associated with the instances (e.g., were individuals in question told that their data would be retained for a fixed period of time and then deleted)? If so, please describe these limits and explain how they will be enforced.
Once data is contributed, the data will be retained as long as it is useful for research purposes, possibly indefinitely.
Will older versions of the dataset continue to be supported/hosted/maintained? If so, please describe how and for how long. If not, please describe how its obsolescence will be communicated to users.
By default, older versions of the dataset will continue to be supported, hosted, and made available to researchers. Each version of the dataset has a unique DOI. The dataset publishers reserve the right to remove access to older versions.
If others want to extend/augment/build on/contribute to the dataset, is there a mechanism for them to do so?
For dataset extensions and augmentations, it is possible for others to publish a derivative dataset on the Health Data Nexus which references the original source. These derivative datasets may be made available under the same conditions as the source data.
For augmentations to the code used to produce the data, the open-source repositories have discussion forums and issue pages which allow for public discussion of data preprocessing. The repository also has a mechanism (“pull requests”) for contributing improvements to the data preprocessing code.
The raw audio files and the questionnaire data retrieved from ReproSchema-UI or exported from REDCap were converted to be compliant with the
Brain Imaging Data Structure v1.9.0
.
Pediatric data:
Pediatric data collected through ReproSchema-UI is extracted and transformed into REDCap format, and subsequently converted to the Brain Imaging Data Structure (BIDS).
The folder structure for the dataset is as follows:
b2ai-voice-audio
├── CHANGES.md
├── README.md
├── dataset_description.json
├── phenotype
├── confounders
│
├── confounders.json
│
└── confounders.tsv
├── demographics
│
├── demographics.json
│
└── demographics.tsv
├── diagnosis
│
├── adhd_adult.json
│
├── adhd_adult.tsv
│
├── airway_stenosis.json
│
├── airway_stenosis.tsv
│
├── amyotrophic_lateral_sclerosis.json
│
├── amyotrophic_lateral_sclerosis.tsv
│
├── anxiety.json
│
├── anxiety.tsv
│
├── benign_lesions.json
│
├── benign_lesions.tsv
│
├── bipolar_disorder.json
│
├── bipolar_disorder.tsv
│
├── cognitive_impairment.json
│
├── cognitive_impairment.tsv
│
├── control.json
│
├── control.tsv
│
├── copd_and_asthma.json
│
├── copd_and_asthma.tsv
│
├── depression.json
│
├── depression.tsv
│
├── glottic_insufficiency.json
│
├── glottic_insufficiency.tsv
│
├── laryngeal_cancer.json
│
├── laryngeal_cancer.tsv
│
├── laryngeal_dystonia.json
│
├── laryngeal_dystonia.tsv
│
├── laryngitis.json
│
├── laryngitis.tsv
│
├── muscle_tension_dysphonia.json
│
├── muscle_tension_dysphonia.tsv
│
├── parkinsons_disease.json
│
├── parkinsons_disease.tsv
│
├── precancerous_lesions.json
│
├── precancerous_lesions.tsv
│
├── psychiatric_history.json
│
├── psychiatric_history.tsv
│
├── ptsd_adult.json
│
├── ptsd_adult.tsv
│
├── unexplained_chronic_cough.json
│
├── unexplained_chronic_cough.tsv
│
├── unilateral_vocal_fold_paralysis.json
│
└── unilateral_vocal_fold_paralysis.tsv
├── enrollment
│
├── eligibility.json
│
├── eligibility.tsv
│
├── enrollment_form.json
│
├── enrollment_form.tsv
│
├── participant.json
│
└── participant.tsv
├── questionnaire
│
├── custom_affect_scale.json
│
├── custom_affect_scale.tsv
│
├── dsm5_adult.json
│
├── dsm5_adult.tsv
│
├── dyspnea_index.json
│
├── dyspnea_index.tsv
│
├── gad7_anxiety.json
│
├── gad7_anxiety.tsv
│
├── leicester_cough_questionnaire.json
│
├── leicester_cough_questionnaire.tsv
│
├── panas.json
│
├── panas.tsv
│
├── phq9.json
│
├── phq9.tsv
│
├── productive_vocabulary.json
│
├── productive_vocabulary.tsv
│
├── vhi10.json
│
├── vhi10.tsv
│
├── voice_perception.json
│
└── voice_perception.tsv
└── task
├── acoustic_task.json
├── acoustic_task.tsv
├── harvard_sentences.json
├── harvard_sentences.tsv
├── random_item_generation.json
├── random_item_generation.tsv
├── recording.json
├── recording.tsv
├── session.json
├── session.tsv
├── stroop.json
├── stroop.tsv
├── voice_perception.json
├── voice_perception.tsv
├── voice_problem_severity.json
├── voice_problem_severity.tsv
├── winograd.json
└── winograd.tsv
└── sub-<participant_id>
└── ses-<participant_id>
└── audio
├── sub<participant_id>_ses<participant_id>_task-<task_name>.wav
└── sub<participant_id>_ses<participant_id>_task-<task_name>.json
Speech tasks included
ABC’s
Animal fluency
Cape V sentences
Caterpillar Passage
Caterpillar Passage (Pediatrics)
Cinderella Story
Counting
Diadochokinesis
Favorite Foods
Favorite Show/Movies
Identifying Pictures
Months
Naming Animals
Naming Foods
Outside of School
Picture description
Picture Description (Pediatrics)
Productive Vocabulary
Prolonged vowel
Rainbow Passage
Random Item Generation
Ready For School
Repeat Words
Repeat Sentences
Role Naming
Story recall
Word-color Stroop
AI-Ready derived datasets
The feature-only dataset provides AI-ready derivations from the raw audio. Features extracted include:
OpenSmile eGeMaps features per audio file
Parselmouth/Praat speech features for any speech tasks
Speech intelligibility metrics for speech tasks Time-varying features:
Torchaudio-based pitch contour, spectrograms, mel spectrogram, and MFCCs
Speech Articulatory Coding (sparc)-based features including electromagnetic articulography (EMA) estimates, plus loudness, periodicity, and pitch measures
Phonetic posteriorgrams (PPGs)
The waveform-derived features are stored using two formats:
A fixed feature format that includes static features extracted from the entire waveform
A temporal format that varies for each audio file depending on the length of recording.
The questionnaire features are collected and distributed in the phenotype folder format shown above. These can be used for cohort selection.
Methods of De-identification for v3.0.0
All direct identifiers were removed, as these would reveal the identity of the research participant. These include name, civic address, and social security numbers. Indirect identifiers were removed where these created a significant risk of causing participant re-identification, for example through their combination with other public data available on social media, in government registries, or elsewhere. These include select geographic or demographic identifiers, as well as some information about household composition or cultural identity. Non-identifying elements of data that revealed highly sensitive information, such as information about household income, mental health status, traumatic life experiences, and the like, were also removed.
Raw audio transcripts were reviewed, and any audio recordings that contained potentially identifying information and external voices were removed from the release.
All sensitive fields were removed from the dataset at this stage. These correspond to data elements encoded as sensitive (column name: “Identifier?”) listed in the
REDCap data dictionary (CSV)
.
In addition, all spectrograms, MFCCs, Mel spectrograms, transcriptions, EMAs, and PPGs from open-response prompts are removed from the feature-only dataset.
Audit protocol
Generate missingness tables
Check distributions and outliers
For categorical responses, check against schema
For audio tasks, run quality control metrics
For waveforms:
Check amount of silence
Duration
For speech, check produced speech relative to intended passage
All processing is performed using these toolkits:
b2aiprep
: organizing and preprocessing the dataset
SenseLab
: processing audio files for research tasks
For detailed descriptions of each criterion, please see:
AI-readiness for Biomedical Data: Bridge2AI Recommendations
Table 4 – Precision Public Health (Voice) – Current Rating
Criterion
Criterion met? (Y=1; N=0)
Total Score for Criterion (%)
FAIRness (0)
Findable (0.a)
1
100
Accessible (0.b)
1
Interoperable (0.c)
1
Reusable (0.d)
1
Provenance (1)
Transparent (1.a)
1
100
Traceable (1.b)
1
Interpretable (1.c)
1
Key actors identified (1.d)
1
Characterization (2)
Semantics (2.a)
1
80
Statistics (2.b)
1
Standards (2.c)
1
Potential Sources of Bias (2.d)
1
Data Quality (2.e)
0
Pre-model explainability (3)
Data documentation templates (3.a)
1
100
Fit for purpose (3.c)
1
Verifiable (3.d)
1
Ethics (4)
Ethically acquired (4.a)
1
100
Ethically managed (4.b)
1
Ethically disseminated (4.c)
1
Secure (4.d)
1
Sustainability (5)
Persistent (5.a)
1
50
Domain-appropriate (5.b)
0
Well-governed (5.c)
1
Associated (5.d)
0
Computability (6)
Standardized (6.a)
1
75
Computational Accessibility (6.b)
1
Portable (6.c)
1
Contextualized (6.d)
0
Supporting Materials
B2Ai-Voice | REDCap
Description: Bridge2AI REDCap Data Dictionary and Metadata.
License: MIT
Navigate to Source Code
B2Ai-Voice | Prep Library
Description: The code used to preprocess the raw audio waveforms into the parquet file and to merge the source data into the phenotype files.
License: Apache-2.0
Navigate to Source Code
B2Ai-Voice | Docs
Description: Source code for the Docs and Dashboard for the Bridge2AI Voice Project at
https://docs.b2ai-voice.org/.
License: MIT
Navigate to Source Code
B2Ai-Voice | FHIR
Description: FHIR profiles for voice as a biomarker.
Navigate to Source Code
View our dashboard
and learn more about the data
Go to the Bridge2AI Voice Adult Dashboard
Go to the Bridge2AI Voice Pediatric Dashboard
Join our mission to shape the future of voice in health. Collaborate, contribute, and explore today.
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Copyright © 2025 B2AI Voice. All Rights Reserved.
Bridge2AI-Voice is part of the Bridge2AI Program, funded by the NIH Common Fund. Award #3Tf-OTOD03272001S2
This repository is under review for potential modification in compliance with Administration directives.
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================================================================================

FILE: gdrive_1gTFzAM-FoYlM_X9qF0s7fXoswmaz8IqN_row13.txt
PATH: data/preprocessed/individual/VOICE/gdrive_1gTFzAM-FoYlM_X9qF0s7fXoswmaz8IqN_row13.txt
SIZE: 77201 bytes
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SOURCE METADATA
Project: VOICE
Source ID: irb_protocol
Source type: IRB
Source URL: https://docs.google.com/document/d/1gTFzAM-FoYlM_X9qF0s7fXoswmaz8IqN/edit
Raw file: data/raw/VOICE/gdrive_1gTFzAM-FoYlM_X9qF0s7fXoswmaz8IqN_row13.docx
--------------------------------------------------------------------------------
PROTOCOL TITLE:
Bridge2AI Voice Data Acquisition
PRINCIPAL INVESTIGATOR:
Yael Bensoussan, MD MSc, FRCSC
Department of Otolaryngology- Head and Neck Surgery
(323) 509-6483
yaelbensoussan@usf.edu
Other USF Co-investigators
Yassmeen Abdel-Aty, MD – Deparment of Otolaryngology
Stephen Aradi, MD – Assistant Professor, Department of Neurology
Ruth Bahr, PhD CCC-SLP – Professor, Department of Communication Sciences & Disorders
Micah Boyer, PhD – Clinical Research Associate
Karim Hanna, MD – Assistant Professor, TCOP Department of Pharmacy Practice
Matthew Mifsud, MD – Associate Professor, College of Medicine Otolaryngology
Tempestt Neal PhD – Assistant Professor, Department of Engineering
Christopher Nickel, MD – Assistant Professor, College of Medicine Otolaryngology
Suketu Shah, MD – Assistant Professor, College of Medicine Otolaryngology
Ahmed Shawkat, MD – Internal Medicine, Morsani College of Medicine
John Templeton, PhD – Assistant Professor, Department of Computer Science and Engineering
Stephanie Watts, PhD, CCC-SLP – Assistant Professor, Department of OTOHNS
Theresa Zesiewicz, MD – Professor, Department of Neurology
Participating institutions and investigators outside USF under Single IRB:
Participating institutions and investigators outside USF under Separate REB (Canadian):
VERSION NUMBER/DATE:
V1. January 17th, 2023
REVISION HISTORY
*This table should only be used during submission of a Modification application to the IRB.
Table of Contents
1.0	Study Summary	4
2.0	Objectives	7
3.0	Background	7
4.0	Safety Endpoints	11
5.0	Study Intervention	11
6.0	Procedures Involved	11
7.0	Data and Specimen Storage for Future Research	15
8.0	Sharing of Results with Subjects	16
9.0	Study Timelines	16
10.0	Inclusion and Exclusion Criteria	17
11.0	Vulnerable Populations	18
12.0	Local Number of Subjects	18
13.0	Recruitment Methods	18
14.0	Withdrawal of Subjects	19
15.0	Risks to Subjects	19
16.0	Potential Benefits to Subjects or Others	19
17.0	Data Management and Confidentiality	20
18.0	Provisions to Monitor the Data to Ensure the Safety of Subjects	21
19.0	Provisions to Protect the Privacy Interests of Subjects	22
20.0	Compensation for Research-Related Injury	22
21.0	Subject Costs and Compensation	22
22.0	Consent Process	22
23.0	Setting	24
24.0	References	24
Study Summary
1.1 Brief Summary of study:
Objectives
2.1 Our group aims to integrate the use of voice as biomarker of health with clinical care by generating a substantial multi-institutional, ethically sourced, and diverse voice database linked to multimodal health biomarkers to fuel voice AI research. Data collection will be made possible by software through a smartphone application linked to other health biomarkers such as radiomics, and genomics, and supported by federated learning technology to protect data privacy.
 Primary: To create a database of human voices, speech and respiratory sounds linked to other health biomarkers such as imaging, demographic and clinical data.
Secondary:
To develop a software and cloud infrastructure to collect and store voice data safely and ethically (Weill Cornell Medicine)
To develop, support and integrate federated learning platforms at USF and Cornell to provide a HIPAA compliant way to train ML models without sharing data within institutions. Federated learning is a technology that allows sharing data for AI analysis without the data leaving the institution. Algorithms are run on data at each institution and model updates are shared to a central node. Therefore, researchers can benefit from other institutions' data without the need to share the actual data. In academia and medicine, this is the solution to the most important boundaries to collaborative research due to the heavy legal and administrative burden linked to data sharing
Background
3.1 The human voice is often referred to as a unique print for each individual and contains biomarkers that have been linked to various diseases ranging from Parkinson’s disease to dementia, mood disorders and cancers [1].  Voice contains complex acoustic markers that depend on the coordination between respiration, phonation, articulation, and prosody. Recent advances in acoustic analysis technology, in particular those linked to machine learning, have shed new insights into the detection of diseases. As a biomarker, voice is unique, cost-effective, easy and safe to collect in low resource settings. Moreover, the human voice not only contains speech, but also other acoustic biomarkers such as respiratory sounds, and cough.
The production of human voice involves the complex interaction among respiration, phonation, resonation, and articulation. The respiratory system provides the air flow and pressure to initiate and maintain vocal fold vibration. The vocal folds generate the sound source which is then modified within the vocal tract by the oral and nasal cavities and the articulators involved in speech production. Each of these processes is influenced by the speaker’s ability to adjust and shape these interacting systems.
Although many use the terms voice and speech interchangeably, it is important to understand the distinction between the different terms used to describe human sounds:
Voice: In the voice research field, refers to sound production and is the phonatory aspect of speech. In other words, it is the sound produced by the larynx and the resonators. For example, voice can be assessed by asking someone to produce a prolonged vowel sound like /e/.
Speech: Speech is the result of the voice being modified by the articulators and is produced with intonation and prosody. For example, a patient having a stroke can have abnormal speech production due to difficulty with articulating words but have a normal voice. For this project, the term Voice as a Biomarker of Health will include speech in its definition.
For voice to emerge as a biomarker of health, there is a pressing need for a large, high- quality, multi-institutional and diverse voice database linked to other health biomarkers from various data of different modality (demographics, imaging, genomics, risk factors, etc.) to fuel voice AI research and answer tangible clinical questions. Such endeavor is only achievable through multi-institutional collaborations between voice experts and AI engineers, supported by bioethicists and social scientists to ensure the creation of ethically sourced voice databases representing our populations.
Objective of the Grand Challenge:
              Our group aims to develop voice as a biomarker of health used in clinical care. To do so we will generate a large multi-institutional, ethically sourced, and diverse voice database linked to multimodal health biomarkers to fuel voice AI research. We will then build predictive models to assist in screening, diagnosis, and treatment of a broad range of diseases, including several diseases with unmet clinical needs. Data collection will be made possible via the development of cutting-edge software available as smartphone application. Data collection will be combined with other health biomarkers such as radiomics, and genomics. Importantly, this project will pioneer the use of federated learning technology to create multi-center machine learning models while strictly protecting data privacy. Rising ethical concerns regarding Voice AI such as legal implications of voice identification, voice AI hacking and voice data sharing and privacy, and impact of gender and racial diversity on Voice AI will be addressed.
             Based on the existing literature and ongoing research in different fields of voice research, our group has identified 5 disease cohort categories for which voice changes have been associated to specific diseases with well-recognized unmet needs. We will center our data acquisition efforts on the following disease categories:
Voice Disorders
Neurological and Neurodegenerative Disorders
Mood and Psychiatric Disorders
Respiratory disorders
Pediatric Voice and Speech Disorders
The voice data acquisition efforts will be facilitated by partnerships with High Volume Expert Clinics as well as Community Clinics representing underserved populations.
Data Sharing and Federated Learning
Federated Learning is an emerging method in deep learning where multiple collaborators train a machine learning model in parallel, without trespassing institutional firewalls. This “decentralized” learning approach allows data to be kept within each collaborative institution protected servers, while their deep learning model updates are transferred to a central server to be aggregated in a consensus model. In contrast, the conventional “centralized” deep learning approach requires data to be uploaded to central servers, which becomes problematic when dealing with health data and patient-identifiers. Outside of the medical world, federated learning is actively used by technologists to augment datasets feeding AI systems. It is currently used by Google to “build better AI products with on-device data and privacy by default”. This novel approach has the potential to revolutionize health informatics and applications of artificial intelligence in medicine. For the purpose of this study, 2 levels of privacy will be built.
1. A regular combined master de-identified database will be hosted through a HIPAA privacy preserving NIH Stride Partner (see definition above in section 1 table). Data Sharing and data use agreements will be put in place between participating institutions
2. Through Federated learning technology where each institution will host their data and models can be trained without the data leaving the institution.
3.2 Existing Pilot Data:
Voice biomarkers are increasingly being used in the Voice AI world including academia and tech. Pilot Studies have shown promising preliminary results in the 5 disease categories described:
1. Voice Disorders: Laryngeal disorders are the most studied pathologies linked to vocal changes. Benign and malignant lesions can affect the shape, mass, density, and tension of the vocal folds resulting in changes in vibratory function resulting in changes in phonation [2].
Acoustic and aerodynamic analysis of the voice is an established component of the clinical laryngeal assessment, currently collected by speech-language pathologist trained in voice therapy, with an associated billing code. These measures have been used to identify pathologic vocal qualities, determine optimal management strategies, and evaluate treatment outcomes. They also provide quantitative objective measurements for scholarship pursuits. Currently, these analyses are performed in sound-proof rooms on proprietary hardware and software, such as the Computer Speech Lab by Kay-Pentax, from which a waveform file (WAV) can be extracted. Current standard acoustic measures collected at voice centers include fundamental frequency (pitch), intensity (loudness), jitter (variations in pitch), shimmer (variations in loudness), noise-to-harmonic ratio, and cepstral peak prominence (extent of harmonic structure in connected speech). Aerodynamic assessment quantifies laryngeal airflow and subglottic pressures during voice production. The latter require specialized aerodynamic equipment and cannot be collected via acoustic recording. Classic acoustic analysis has uncovered patterns of change in some standard parameters. However, small sample size and voice variability intra-and inter-subjects have limited generalizability. There has been a growing body of research using AI/ML models to screen for various voice disorders based on voice recording, all plagued by small sample size leading, limited external validity and algorithmic overfitting. For instance, spectrogram analysis by convolutional neural networks (CNNs) has demonstrated high accuracy in the identification of laryngeal disorders such as adductor spasmodic dysphonia, unilateral vocal fold paralysis, vocal fold polyp, polypoid corditis, and recurrent respiratory papillomatosis, based on voice samples from 10 speakers per disease [3]. Detection of laryngeal cancer from voice sample using CNNs attained high accuracy based on data from a cohort of 50 patients [4]. In order to be clinically relevant, AI models will need to help screen for conditions by differentiating the conditions that need urgent or active management, such as laryngeal cancer or vocal fold paralysis, from benign laryngitis so that patients can be referred to the right specialist and in a timely manner. Building a tool that contains enough voice data to differentiate between the various voice conditions requires very large numbers with standardized data collection protocols and diverse speakers [5].
2. Respiratory disorders: Respiratory sounds, including breath, cough and voice have long been used for diagnostic purposes. For instance, pediatric croup can be suspected based on the presence of barking cough, stridor and dysphonia. With advances in acoustic recording and analysis in the second half on the twentieth century, increasing interest has emerged in the use of respiratory sounds for disease screening and therapeutic monitoring, especially with cough sounds. Though some of these efforts were promising, sample size remained low, which compounded with reliance on variable voluntary coughs, has limited generalizability of these attempts. More recently, the potential of using voice-related biomarkers for respiratory disorders screening has gained immense interest worldwide with the COVID-19 pandemic. As voice is a non-invasive, low-cost marker to connect, several academic teams, non-profit organizations and companies have investigated the value of voluntary cough sounds and voice recordings to detect COVID-19 using machine learning algorithms. Most of the data in these efforts was obtained via crowdsourcing efforts, with no standardized data acquisition protocol and no verification of data validity, with reliance of participants to designate their COVID-19 status. Furthermore, reproducibility studies are rare, even with existing open-sourced data [6]. For-profit enterprise is vastly invested in this space, although no FDA-approved or clinically useful algorithm has yet emerged. Sonde Health, an AI start-up whose mission is to unlock voice as a vital signal and a meaningful predictor of health” uses ML model to screen and manage progression of other respiratory diseases such as Chronic Obstructive Lung Disease or Cardiac Failure through longitudinal analysis of shortness of breath heard through voice data collection through smartphones [7]. As voice biomarkers continue to emerge, it will be crucial for the researcher community to have access to publicly available voice databases without reliance on the private sector.
3. Mental Health and psychiatric disorders: Changes in voice have been linked to depression and other mood disorders. Individuals with depression have been found to have decreased fundamental frequency (f0) as well as a monotonous speech [8], while individuals with anxiety disorders have a significant increase in F0. Much of the literature examining the intersection of voice and speech changes in psychiatric conditions is plagued by small datasets with limited demographic diversity reporting, lack of standardized data collection protocol precluding meta-analysis and possible confounders, all limiting external validity and clinical usability [9]. There have been calls for creating open ML ready datasets for reproducible and generalizable AI voice research [10]. Approaches to data acquisition have varied, with some studies relying on small samples of voice data to analyze acoustic features such as F0, jitter or shimmer, while others focus on longitudinal voice and speech data collection through smartphones or wearable devices to screen for changes in mental health, such as manic and hypomanic episodes in bipolar disorder [11]. Prior literature thus suggests that software and hardware tools to collect voice and speech data for mental health screening, diagnosis and monitoring require a combination cross-sectional as well as longitudinal data acquisition. Science in this field should be hypothesis-driven and open, to allow for validation via reproducibility studies.
4. Neurological and neurodegenerative disorders: Voice and speech are altered in many neurological and neurodegenerative conditions [12, 13, 14]. Acute strokes can present with slurred speech (Dysarthria) or expressive deficits speech (Aphasia). Voice and speech changes can be the presenting symptoms of many neurodegenerative conditions, such as Parkinson’s and ALS with changes such as slowed, low frequency, monotonous speech as well as vocal tremor [15]. A recent review by Bjorklund et al. reviewed the available voice and speech datasets for Parkinson’s and Alzheimer’s disease and concluded that although individual studies showed promising results, there was a need for collecting acoustic biomarkers in a minimally invasive, low-cost and standard way to create harmonized speech datasets [16]. Dr. Reza Hosseini Ghomi, chief medical officer at NeuroLex Laboratories, a startup focused on developing voice biomarker technology, recently stated “The field of digital biomarkers is still very fragmented because there are no standards for voice recording or an organizing force,” which is likely why there is still no FDA-approved technology in this space [17].
5. Pediatric Speech disorders: The literature is sparser in terms of pediatric voice and speech analysis partly due to ethical concerns and challenges in data acquisition for this cohort [18]. However, many studies have investigated the use of machine learning models for voice and speech analysis for detection of Autism and Speech Delays in the pediatric population. A recent study on voice and speech difference in a cohort of 90 patients with autism spectrum disorders and 28 typical development patients and found that machine learning models could help distinguish between these two categories with higher performance when analyzing prosodic measures compared to articulation measures of the speech [19]. Due to the important variations in voice and speech with development of a child, creating a voice database of “normal cohort” of different age groups will be key to help machine learning models diagnose age specific speech delays and disorders.
4.0 Safety Endpoints
4.1 N/A
5.0 Study Intervention
5.1 N/A - This project will involve data collection only as the primary objective is to build a large multi-institutional database and there will be no intervention involved.
5.2 N/A
6.0 Procedures Involved
6.1 This is a prospective cohort study over 4 years involving 11 different academic sites across the US with potential of adding extra data collection sites in phases 3-4 of the project. It involves collection of mainly acoustic data (voice, speech and respiratory sound) through smartphone applications as well as other clinical data (demographics, clinical information, imaging, validated questionnaires and genomic information for only 1 subset of the population (a separate IRB will be submitted for that sub-group). In the event where the caregiver's voice is recorded inadvertently, these clips can be discarded by 2 means: Rerecord manually during data collection or during data audit and postprocessing. Participants' cohorts will be identified based on known diagnosis from 5 different disease categories:
Voice Disorders
Respiratory Disorders
Neuro Disorders
Mood Disorders
Pediatric speech disorders
In addition to patient cohort data, participants will include individuals who do not have the conditions of interest to serve as controls in the dataset.
There will also be a Feasibility Assessment:
Throughout the data collection process, feasibility measures will be taken directly from the app. Measures such as time taken to complete each task, drop-out rates, and time taken in clinic will be captured. Qualitative data will also be captured to get feedback from participants on experience with using data collection tools and feasibility of protocols within clinical workflow (through questionnaires and voice recording).
Please see Annex A for full Scope of Work (SOW) and deliverables for phase 1
6.2 Please select the methods that will be employed in this study (select all that apply):

Data will be collected at USF and 11 other participating institutions.
High Volume Expert Clinics
We define High Volume Expert Clinics (HVEC) as clinics/programs within academic institutions that have multidisciplinary programs targeted to the specific diseases listed in the disease cohorts table and have a volume of over 1000 patients per year.
Community Outreach Clinics and underserved populations
We define Community Outreach Clinics (COC) as clinics within or outside academic institution’s whose main mission is to provide health services to underserved and under-represented populations.
Table 1
Phased approach of Data Acquisition over a 4-year period
Data types collected across all categories:
Voice, Speech, and other acoustic data:
Voice samples will be collected in a prospective manner, for all participants undergoing gold standard diagnostic evaluation in all the described disease categories. Voice, breath, cough, and speech data will be recorded. Data collected will include prolonged vowel sounds, free speech, and spontaneous speech, as well as snoring sounds, coughing sounds and breathing sounds.
Demographics: Detailed demographic data will be collected through the smartphone application including Age, Sex, Gender, Race and Ethnicity, Language).
Imaging: Different type of imaging modality will be collected from patients charts ONLY. For example, CXR will be collected for Asthma while Brain CT Scans and Brain MRIs will be collected for the Alzheimer's Cohort. NO ADDITIONAL IMAGING WILL BE PERFORMED IN THE CONTEXT OF THIS STUDY AND ONLY PREVIOUSLY COMPLETED IMAGING WILL BE REVIEWED AND COLLECTED.
Validated tools: Validated tools for each disease category will be integrated within the app for data collection. For example, the Voice-Handicap Index-10 (see Annex B) will be used for the Voice Disorders. Scores will be automatically uploaded to the corresponding database.
Genomic: No genetic information will be collected at the USF site. This will only be performed at MSH and UofT which are 2 Canadian participating institutions. Therefore, a separate REB will be submitted and obtained at these institutions for this portion of the study
Table 2. Type of Data Modality Collected per Disease Category:
*Imaging will be collected retrospectively. No additional imaging will be performed in this study.
Sample Sizes
For all participants mentioned above, complete data acquisition including multi-modal data will be performed for up to 5000 participants per category (disease category and controls). As this database is intended to fuel AI research and develop ML/AI models to assist diagnosis and treatment of health conditions, there is no sample size calculation applicable to our methodology. The sample sizes have been defined according to the existing literature. Currently published AI/ML models linking voice to these categories of diseases consist of datasets of small sizes ranging from 20-400 patients and reach high diagnostic accuracies.
6.3 Voice/speech data collection will be conducted in two ways: through HVEC, and remotely. This type of data collection is not normally performed in HVEC for disease categories other than the voice disorder categories. In terms of validated questionnaires, these are commonly performed during regular clinical visits and would be expected to be performed within or outside of this study.
HVEC and remote data collection occur through two platforms, the Bridge2AI Voice Web app and the Bridge2AI Voice iOS app. The iOS app is restricted to iOS-compatible devices, while the Web App can run on a computer, tablet, or phone device. No software is downloaded or installed. Both apps are themselves HIPAA-compliant, and only store data during the collection phase. The data are removed as soon as the page is closed. There is no login required. The data are sent similar to the in-clinic collection over a secure https protocol to a HIPAA-compliant storage server.
6.4 Risk to Participants from Study Intervention:
There are no direct significant risks due to the research conducted. The most important risk lies in protection of data privacy. To reduce that risk, all voice data will be collected through one of two client applications, the Bridge2AI Voice iOS app or the Bridge2AI Voice Web app. The applications will send data over a secure https protocol to one of the NIH Strides partners – these companies (Google, Microsoft, Amazon) have pre-negotiated contracts with the NIH to ensure data privacy and HIPAA protection of medical information.
6. 5 Accessing or collecting existing data through:
Charts of patients presenting at HVEC will be screened for inclusion and exclusion criteria before the clinic day. At USF, this will be conducted through the EPIC platform (through local site EHR for other participating institutions). Investigators, who are clinicians practicing in these clinics, have authority to screen through these existing patient lists.
Existing clinical data within the EHR will be reviewed and the following information will be collected:
Demographics: Detailed demographic data will be collected through the tool (See TDOM Module) including Age, Sex, Gender, Race and Ethnicity, Language).
Imaging: CXR for the Respiratory Disease Category, Brain CT Scans and Brain MRIs for the Neurology Category will be collected
Clinical data related to diagnosis: this includes disease type, severity, symptoms, management, validated questionnaires and scores
Data will be entered into a REDCap database as part of the Case Report Form.
6.6 Collecting biological specimens:
There will be no biological specimens collected at USF or for this portion of the study. There will be genomic data collection performed ONLY at the University of Toronto and Mount Sinai Hospital who are participating institutions but will submit a separate research ethics proposal to their respective institution. All the genomic information will be collected, managed and analyzed at their sites. The REB (Canadian IRB) approval from these 2 participating sites will be sent to USF IRB once approved.
6.7 Long-term follow-up beyond study period
The current study period is 4 years. Within the consent process, individuals will be asked if they agree to be contacted in the future for further voice data collection if long-term follow up is required as part of an eventual extension of this grant. If they chose that option, they will agree to provide contact information including email and phone number. This data will be stored within the institutional database ONLY and not part of the data that is shared with the other centers and uploaded to the cloud. At this time, there is no plan to collect data beyond the study period. But since this represent a large multi-institutional, nationally funded grant with many potential derivative studies, it is crucial to give the option to participant to provide contact and agree to possible follow-up beyond the study period.
6.8 N/A
7.0 Data and Specimen Storage for Future Research
7.1 The primary objective of this data generation project is to create an open-sourced multi-institutional human voice database. De-identified clinical and voice data will be shared between institutions on a cloud-based infrastructure hosted through an NIH STRIDES partner.  For the first 2 phases of the study, only authorized personnel who have been approved by the institution will have access to the data during the quality control and pilot period. By the end of phase 2, de-identified data will be made open-sourced and publicly available to other researchers through an NIH hosted platform.  There are many similar open-sourced databases hosted by the NIH such as the Clinical Genomic Database (https://research.nhgri.nih.gov/CGD/download/) and the (https://www.genome.gov/human-genome-project).  All data including voice and clinical data hosted on the open-sourced database will be de-identified.
Data transfer and data use agreements between collaborating universities will be drafted alongside the patent and innovations office for the safe sharing of data.
7.2 Type of Data shared:
The meta-database will include acoustic samples (voice, speech, respiratory sounds) linked to other data modality:
Demographic data (age, sex at birth, gender identity, race, ethnicity, languages spoken)
Clinical data related to diagnosis (Disease category, disease severity, past medical history, treatment or medication for disease)
Imaging
Genomic data (only for the Alzheimer’s cohort that will be performed at UofT and MSH and therefore will have a separate protocol and REB approval)
7.3 Local access to data will be available to approved study personal via the password protected database REDCap, authorized personal who have been approved by the institution and the PI will have access to it for the purpose of this study. Data Transfer agreements between collaborating universities will be drafted alongside the patent and innovations office for the safe sharing of data. The data is going to be hosted on server through NIH Stride partners. All institutions will collect data through a client application, the Bridge2AI Voice Web app. The application will send data over a secure https protocol to one of the Strides partners at NIH. The Strides partners are Amazon, Google, and Microsoft and they have agreements with the NIH already for data privacy and storage.
8.0 Sharing of Results with Participants
8.1 The primary objective of this study is to build a meta-database of human voices. Therefore, there will be no result of investigations or study intervention within the study period (4 years). This meta-database will become an open-source database for other researchers to use for future voice AI projects and therefore results from all future studies cannot be tracked. If any studies are performed by the study investigators during or after the study period using this database, the consortium name BRIDGE2AI- VOICE will be used for any journal publication. For future researchers, the open-source database will also be cited.  Due to this the participants will have access to this open-source data and potentially additional data based on potential contact with the ethics team. Any additional data participants will optionally have access to.
9.0 Study Timelines
9.1 This study is currently funded over 4 years. Individuals will be offered to enroll with the option to contribute with single time point data or longitudinal data (for certain disease cohorts).
Single Time Point Data Collection:
For single time point data collection, voice data collection will be performed in one of two ways.
1. Voice data collection will be performed in clinic during the regular office visits. Patients will be offered to enroll in the study and escorted to a study room before or after their regular appointment. In some cases, the consent and data collection will be performed on the same day with the research assistant. In other cases, enrollment and consent may occur remotely through an application, and/or may occur at an earlier point in time than data collection. In some cases remote consent will not require the signature of the researcher; in this instance the consent form will leave off the final section (Statement of Person Obtaining Informed Consent) but be otherwise identical. Consent may take place electronically, within the app, or on paper. Electronic consent is housed in REDCap and is HIPAA compliant. The whole process will take from 30 to 60 minutes depending on vocal tasks from various disease categories.
2. Data collection may occur remotely, through access to an application.
Longitudinal data collection: For specific disease cohorts that are progressive or degenerative (i.e Alzheimer's, Parkinson’s, longitudinal data collection may be collected.
The longitudinal data will be collected in 2 forms:
1: Longitudinal data in clinic during clinic visits:
This data collection will happen in clinic during REGULAR follow up visits. No additional visits outside of regular follow up plans for disease monitoring. The maximum follow-up time will not exceed the study period of 4 years.
2: Longitudinal data “at home”:
In between clinic visit, certain cohorts could be asked to perform voice data collection at home through a smartphone application that will be downloaded for them in clinic. This data collection will be asked at intervals of 1-6 months depending on disease cohorts and for a maximum of 4 years total duration. Participants can decide to leave the study at any point during that time.
Table 3. Overall study timeline per phase
10.0 Inclusion and Exclusion Criteria
10.1 Eligibility screening:
-Patients will be screened for eligibility prior to the consent process
10.2 Inclusion criteria:
For treatment population:
- Being diagnosed and/or treated for a voice, respiratory, pediatric, mood, or neurological disorder affecting voice, cough, breath and/or speech (see Section 3: Background) at one of USF Health’s or participating institutions’ HVEC (see Annex C table 4 for participating institutions)
- Consenting to provide a voice/speech sample for an open-sourced database
- Being 18 years old or older (exception of pediatric data collection)
- Speaking the English or Spanish language
- At home or ‘in the wild” data collection participants will need to have their own smartphone that can be used for data collection
For control population:
- Not being diagnosed and/or treated for a voice, respiratory, pediatric, mood, or neurological disorder affecting voice, cough, breath and/or speech (see Section 3: Background)
- Consenting to provide a voice/speech sample for an open-sourced database
- Being 18 years old or older (exception of pediatric data collection)
- Speaking the English or Spanish language
- At home or ‘in the wild” data collection participants will need to have their own smartphone that can be used for data collection
10.3 Exclusion criteria:
- Not speaking the English or Spanish Language
- Having had a surgical intervention significantly altering the symptoms of the disease studied
- Not consenting (or, in the case of pediatric participants, not receiving consent from a parent or guardian) to voice and clinical de-identified data being uploaded to an open-sourced database
10.4 N/A
10.5 Specific populations: We will not specifically include or exclude students, employees, or wards of the state. There is a specific plan in place to enroll participants from socially/ economically disadvantaged and underserved population so that the database is diverse and FAIR while truly representing our populations. This will be performed through the Plan for Enhancing Diverse Perspectives (PEDP).
11. Vulnerable Populations
Pediatric patients will be enrolled ONLY through the pediatric sites and not at USF. Checklist HRP-416 for children under 21 has been completed and is appended to this protocol.
12.  Local Number of Participants
12.1 Total number of research participants
- Through HEVC and COC within USF, we aim to enroll 5000 participants over the study period. The total number of 30 000 participants will be reached by collaboration with other participating institutions and existing cohorts
12.2 N/A
13. Recruitment Methods
13.1 Potential participants will be recruited through clinic appointments at the participating facilities in this study. Additionally, recruitment flyers will be placed in well-trafficked areas to recruit participants in HVEC and COC. Recruitment for remote data collection will include flyers posted at other locations outside of HVEC. At some participating facilities, recruitment flyers will link through a QR code to a secure REDCap survey, which will be used to screen participants and facilitate enrollment. At some facilities where approval by the facilities has been granted, approved research staff will recruit individuals in waiting rooms, including those accompanying patients for clinical appointments. All entries to the REDCap system will be deleted once patients are enrolled, or within 90 days of their submission, whichever comes first. Recruitment will also take place through social media platforms, where information may be presented in the form of posts, reels, stories, or links. Platforms for recruitment will include: LinkedIn, X, Facebook, Instagram, YouTube, the Bridge2AI-Voice website (www.b2ai-voice.org), and the Bridge2AI website (www.bridge2ai.org). Recruitment will also take place through FlowTrials, an online platform where studies passively publicize their requests for participation. Participating facilities will recruit through their own websites in coming years. If specific diagnoses and/or severities are under-represented in the dataset, participants will be identified by medical record review, based on voice-disorder related diagnostic codes.
The Bridge2AI Enrollment Web app is launched in a browser on the participant's computer, tablet, or phone device. No software is downloaded or installed. The app is itself HIPAA-compliant, and only stores data during the collection phase. The data are removed as soon as the page is closed. There is no login required. The data are sent similar to the in-clinic collection over a secure https protocol to a HIPAA-compliant storage server.
13.2 Potential participants will be recruited through clinic appointments at the participating facilities in this study. Additionally, recruitment flyers will be placed in well-trafficked areas to recruit participants in HVEC and COC. Recruitment for remote data collection will include flyers posted at other locations outside of HVEC. At some participating facilities, recruitment flyers will link through a QR code to a secure REDCap survey, which will be used to screen participants and facilitate enrollment. At some facilities where approval by the facilities has been granted, approved research staff will recruit individuals in waiting rooms, including those accompanying patients for clinical appointments.
Recruitment will also take place through social media platforms, where information may be presented in the form of posts, reels, stories, or links. Platforms for recruitment will include: LinkedIn, X, Facebook, Instagram, YouTube, the Bridge2AI-Voice website (www.b2ai-voice.org), and the Bridge2AI website (www.bridge2ai.org). Recruitment will also take place through FlowTrials, an online platform where studies passively publicize their requests for participation. Participating facilities will recruit through their own websites in coming years. If specific diagnoses and/or severities are under-represented in the dataset, participants will be identified by medical record review, based on voice-disorder related diagnostic codes.
The Bridge2AI Enrollment Web app is launched in a browser on the participant's computer, tablet, or phone device. No software is downloaded or installed. The app is itself HIPAA-compliant, and only stores data during the collection phase. The data are removed as soon as the page is closed. There is no login required. The data are sent similar to the in-clinic collection over a secure https protocol to a HIPAA-compliant storage server.
13.3 An informed consent process will take place where the participant and/or consenting parent/guardian is aware that this is fully voluntary and no undue influence or coercion will be possible since it is a voluntary participation that will take place during a typical patient care appointment.
14.0 Withdrawal of Research Participants
14.1 N/A
14.2 If participants withdraw from the study after they complete the voice data collection the data they have provided will be kept until completion of study. If participants withdraw during or before the voice data collection is completed their data will not be included in the database. For longitudinal data collection, if participants withdraw at any time during the study process, the data they have completed will be used in the database as the purpose of this data generation project is not to analyze the data studied but provide a platform and open-source database for future research. The minimal requirement for study completion is 1 data collection time-point. Participants can decide to withdraw from the longitudinal data collection at any point throughout the study period.
Participants that withdraw from the study will have the option to complete a satisfaction survey to better understand their reasons for withdrawal. This survey may also be provided to patients who decline participation, or to participants who express initial hesitation. Completion of the survey will be entirely optional and no PHI will be collected.
15.0 Risks to Research Participants
15.1 Risks associated with the study itself include the risk of personal information being mistakenly released. Voice collection is a safe non-invasive collection method with minimal risk to the participant. The confidentiality of records that could identify participants will be protected, respecting the privacy and confidentiality rules in accordance with the applicable regulatory requirement(s). Risks to the participants will be minimized by strict adherence to confidentiality rules. In addition, the study team will perform the study according to good clinical practices, and only the PI and the study team will have access to the medical records, REDCap records and identifiable clinical information. The only other risk is associated with questions asked for participants from certain disease cohorts such as mood disorders, depression, anxiety, etc. Answering some of these questions can lead to possible discomfort and could trigger negative emotions for the participants.
15.2 N/A
15.3 N/A
16.0 Potential Benefits to Participants or Others
16.1 There are no direct immediate benefits to participants during this study.  Overall, the study could provide more accessibility to underserved or marginal populations through AI advancements/machine learning and health care screenings normally only provided through a specialist that can take months with high monetary costs otherwise.
16.2 This study has potential benefits to society. The primary aim of this study is to create a large database of human voices linked to diseases for AI analysis. This database could provide very valuable datasets to train AI models to screen or diagnose diseases in early staged based on voice. Utilizing objective AI could result in a more accurate assessment of a patient’s treatment progress and outcomes that might otherwise be susceptible to human error. This could also provide more accessibility to underserved or marginal populations through AI advancements/machine learning and health care screenings normally only provided through a specialist that can take months with high monetary costs otherwise.
17.0 Data Management and Confidentiality
17.1 The PI and study team will conduct the study using Good Clinical Practice guidelines. All members of the study team will respect the confidentiality of the records being accessed and the data being input to REDCap. All users will have individual usernames and passwords to access REDCap and the HIPAA-secure cloud server. These databases have security measures in place to protect the data.
Participant’s personal information will remain confidential and will not be used if study information is published or presented at a scientific meeting.
All institutions (see List of Institutions Annex C) will collect voice data through one of two mobile client applications, the Bridge2AI Voice Web app or the Bridge2AI Voice iOS app. The application will send data over a secure https protocol to one of the NIH Strides partners. The types of data that will be shared will be Voice, demographic data and clinical data.
All clinical data will be hosted on cloud/servers through NIH STRIDE partners. NIH Stride partners have pre-determined agreements with the NIH to ensure HIPAA protection and participant safety. All information regarding STRIDE partners can be found at: https://datascience.nih.gov/strides.
This project is a data generation project with the aim of building a large database of voices. There is no plan for data analysis for this project as the aim is to generate data.
17.2  Levels of data privacy and study-related storage:
1) Study related material:
- All study related material at USF will be stored through the Florence platform in accordance with USF and NIH regulations. The USF IRB requires de-identified study data and original consent forms be stored for a minimum of 5 years after the completion of the study. Original paper consent documents cannot be destroyed until 5 years after study completion, even if they are uploaded into Florence. We will follow these procedures.
2) Participant-related data containing identifying information:
- Each institution will keep their identified data within their institution. Identifiable PHI will be kept on the REDCap databases with password protected access only for investigators at the institution where the data is collected
3) De-identified participant-related data and de-identified voice samples:
- De-identified data will be shared through the joint meta database hosted on the cloud infrastructure described above. Participant numbers and institution numbers will be created to allow institutions to track and have ownership of their institutional data
All investigators and project related personnel with data access will need to follow the appropriate certification through their local institution including HIPAA and privacy training.
17.3 Quality control of the data will be performed throughout the 4 years of the project.
The smartphone application for voice data collection will include models to ensure acoustic data quality including volume standardization and noise cancellation.
The team has hired 2 acoustic engineers and 3 AI data scientists who will analyze all samples from Year 1 exploratory data collection phase to ensure acoustic quality and ML readiness in terms of data preparations.
Each subsequent year of the study (2-4) 10% of the data will be selected at random every month for quality control and ML readiness.
17.4 Data Transfer agreements between collaborating universities will be drafted alongside the paten and innovations office for the safe sharing of data. The data is going to be hosted on server through NIH stride partners. All institutions will collect data through a client application, the Bridge2AI Voice Web app. The application will send data over a secure https protocol to one of the Strides partners at NIH. The Strides partners are Amazon, Google, and Microsoft and they have agreements with the NIH already for data privacy and storage. The types of data that will be shared will be VOICE, clinical history, age, sex, and DOB.
The study team will also be utilizing federated learning. Federated learning is a technology that allows sharing data for AI analysis without the data actually leaving the institution. Algorithms are run on data at each institution and model updates are shared to a central node. Therefore, researchers can benefit from other institutions' data without the need to share the actual data. In academia and medicine, this is the solution to the most important boundaries to collaborative research dure to the heavy legal and administrative burden linked to data sharing.
Identifiable data and datasheets linking participant numbers to participant information will be kept in each institution on local cloud storage for a maximum of 10 years after study completion. Identifiable data will then be destroyed. This will be the responsibility of each local PI.
17.5 If you will review/access and/or collect/obtain Protected Health Information (PHI) during recruitment or the main study, select all that apply:
We do not plan to share PHI (identifiers plus health information) with anyone outside the USF research staff.
A partial HIPAA waiver is being requested for recruitment purposes to allow the study team to review EHR information for incoming clinic patients ahead of their appointments. PHI will be collected at this time to screen potential participants for qualification into the research study; the PHI collected at this time will therefore be limited to the study’s inclusion criteria. This criterion includes:
 Inclusion criteria:
For treatment population:
- Being diagnosed and/or treated for a voice, respiratory, pediatric, mood, or neurological disorder affecting voice, cough, breath and/or speech (see Section 3: Background) at one of USF Health’s or participating institutions’ HVEC (see Annex C table 4 for participating institutions)
- Consenting to provide a voice/speech sample for an open-sourced database
- Being 18 years old or older (exception of pediatric data collection)
- Speaking the English or Spanish language
- At home or ‘in the wild” data collection participants will need to have their own smartphone that can be used for data collection
For control population:
- Not being diagnosed and/or treated for a voice, respiratory, pediatric, mood, or neurological disorder affecting voice, cough, breath and/or speech (see Section 3: Background)
- Consenting to provide a voice/speech sample for an open-sourced database
- Being 18 years old or older (exception of pediatric data collection)
- Speaking the English or Spanish language
- At home or ‘in the wild” data collection participants will need to have their own smartphone that can be used for data collection
Exclusion criteria:
- Not speaking the English or Spanish Language
- Having had a surgical intervention significantly altering the symptoms of the disease studied
- Not consenting (or, in the case of pediatric participants, not receiving consent from a parent or legal guardian) to voice and clinical de-identified data being uploaded to an open-sourced database
This will allow the clinician to have a study team member available to consent a participant ahead of time and allow for a more seamless transition during the clinic appointment should the patient express interest in participating in research. PHI will not be reused/disclosed to any other person or entity except as required by law, for authorized oversight of the research project, or for other research which use/disclosure of PHI would be permitted by the HIPAA privacy regulations.
We plan to protect identifiers collected under the waiver from improper use and/or disclosure by only allowing authorized study personnel to access it and storing any PHI in HIPAA protected data bases and cloud servers such as REDCap and through Federated Learning.
We will destroy the identifiers collected under the waiver at the earliest opportunity consistent with the conduct of the research.
It is not practicable to obtain signed HIPAA Authorizations from the participants before using or disclosing their PHI in our study because we want to be able to screen incoming clinic patients for qualifications to research before approaching them about the research. We do not want to offer someone to join research they do not qualify for and we need to be able to have research staff on hand the days that there are qualified interested potential participants to help with the consent process so this all requires planning and access prior to contact with the participant so consent before would not be possible.
Our study cannot be conducted without access to and use of participants’ PHI because we need to be able to know if they qualify for the study based on the inclusion and exclusion criteria needed from their medical records/history.
18.0 Provisions to Monitor the Data to Ensure the Safety of Research Participants
18.1 N/A
18.2  N/A
19.0 Provisions to Protect the Privacy Interests of Research Participants
19.1 The PI and the study team will be using participant voice data to build an ethically sourced data database, and throughout this 4-year project only approved study members will have access to the data. Throughout the study the team will be working on a way to attempt to provide participants with the ability to track the results of the research associated with their voice data and outcomes the database creates. If this is possible then the information on how to follow the data will be provided to the participants at that time. Voice data will be deidentified for the privacy protection of the participants but it is important to understand that even when removing all
HIPAA protected information associated with the participant who provides the voice sample each person's voice is unique to them and their health at that time in their life and it is always possible even if unlikely that at some point someone could recognize the participant’s voice since we can never full deidentify one’s voice.
19.2 The data set, collected by the data acquisition team will contain de-identified participant voice samples and analysis of same. Clinical team members with EPIC access will have initial access when necessary for recruiting and disease/cohort placement, by the PI to facilitate hand collection of additional clinicopathology or imaging data. The study data will be uploaded to the password protected network REDCap. which is behind the HIPAA firewall. Data transfer agreements will be approved and signed by each institution collecting or sharing data, and federated learning will be implemented once the application begins hosting/collecting data.
20.0 Compensation for Research-Related Injury
20.1  N/A - This study does not involve any physical risk to participants and therefore there is no risk of research-related injury.
21.0 Participant Costs and Compensation
21.1 Compensation will be provided to the adult population only.
21.2 If you will provide compensation to participants, select all that apply:
Participants will be compensated with gift cards. Participants will be given a $40 gift card for sessions lasting under 90 minutes, and an $80 gift card for sessions lasting over 90 minutes, for a maximum of three sessions and $120.
22.0 Consent Process
22.1 Select the consent options you will use during the course of the study. Each selection below must have a description in the subsequent section(s). Choose all that apply:
22.2 	This is a human subjects research project, so an informed consent should be required by the IRB. We have an ethical obligation to provide the participants with the information provided during the consent process and offer the same due diligences we would offer if it were a human subject research project. This is to ensure that the participants are aware and sure of their choice to participate voluntarily.
Please see Consent, Assent and Parental Permission Documents
In most cases, consent/assent process will take place in the clinic setting of the recruiting HVEC and will be conducted by the research assistants. Consent may also take place remotely through REDCap survey forms, accessed electronically.
Consent may also take place through video recording within an application. In this case, the standard consent document will be displayed on the app, and participants will be instructed to start recording, read a statement out loud, and send. The audio recording from the consent will be used to verify that the acoustic data submitted by the participant belongs to the consented individual. Video consent is meant to provide an additional level of security for participants, as it helps to verify identity and to distinguish participants from bots.
In most cases, there will not be any delay period as the study aims to collect the voice data for the study during the same clinic appointment. In some cases, participants may wish to consent but want to schedule their data collection separately. Participants who consent remotely may also schedule data collection after consent. Where delays occur, participants will be notified of any changes to the study and re-consented before data collection.
Participants may consent by signing a physical paper copy, by indicating electronic consent within the app or through REDCap surveys, or through video recording within the app. When used, paper consent documents will be uploaded and saved electronically in REDCap. All paper and electronic versions of consent will be stored for a minimum of 5 years after the completion of the study, following required USF procedure.
During the consent process, it will be explained to participants that they may be asked to provide longitudinal data for some diseases. In these cases, participants will be counselled on how to download the application on their personal cell phones or tablets for further data collection from home.  For ongoing consent, an electronic consent through the app will be administered before each subsequent data collection to ensure ongoing consent.
The participant and/or parent/guardian will be given any time they need to consider or ask questions before signing the consent and/or assent document in clinic. The participant will be made explicitly aware that this is optional and voluntary and deciding not to participate will not interfere with their normal standard of care in anyway. The participant will not be at risk of undue influence or coercion because it is strictly voluntary.
When consent occurs in clinics, the investigators (see list of co-investigators Page 1 and Annex C) will be responsible for introducing the study to the individuals coming to the appointment. If individuals are interested in participating, they will be escorted to a research room where the research assistant will explain the study in details and conduct the consent process. About 30 minutes will be spent explaining the study and consent in detail with time for any questions or comments. Potential participants will be asked to explain what they have understood to the research assistant to confirm their understanding.
Potential participants will be informed that participation in this study is voluntary and does not impact their medical care in any way.
22.3
Please see Consent, Assent and Parental Permission Documents
In most cases, consent/assent process will take place in the clinic setting of the recruiting HVEC and will be conducted by the research assistants. Consent may also take place remotely through REDCap survey forms, accessed electronically. Electronic consent contains the same text as paper consent.
In most cases, there will not be any delay period as the study aims to collect the voice data for the study during the same clinic appointment. In some cases, participants may wish to consent but want to schedule their data collection separately. Participants who consent remotely may also schedule data collection after consent. Where delays occur, participants will be notified of any changes to the study and re-consented before data collection.
Participants may consent by signing a physical paper copy, or by indicating electronic consent within the app or through REDCap surveys. When used, paper consent documents will be uploaded and saved electronically in REDCap. All paper and electronic versions of consent will be stored for a minimum of 5 years after the completion of the study, following required USF procedure.
In case of remote consent, participants will be able to contact researchers with any questions or concerns before signing.
22.4 N/A
22.5 N/A
-
22.6
- No pediatric data collection will be performed at USF. Data Collection for Pediatric cohorts will ONLY be performed at the pediatric participating institutions.
-When children will be participating in the research, parents or guardians will provide consent, and children will provide verbal assent where appropriate. Parental permission will be obtained from one or both parents; this research is minimal risk and does not require permission from both parents or legal guardians. Where young children cannot reasonably be asked to assent (due to limitations in understanding due to age and development) they will not be asked to do so. This typically holds for children under seven years of age. Assent will be documented (see form).
23.0 Setting
23.1 Part of the data collection will take place in High Volume Experts Clinics across the participating institutions. In this case, data collection will be performed during clinic visits by the research team including the investigator and research coordinators/research assistants. Morsani location is the central location for the USF-based team. Recruitment and data collection will also occur at three other USF specialty clinics: USF Health Byrd Institute; USF Health Park Clinic; and 17 Davis Medical Building; and at VUMC, the Shade Tree Clinic (part of VUMC) and the Academy Children’s Clinic. In some cases, data collection will occur remotely, through access to an application using a phone, tablet, or other device.
24.0 References
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4. Kim H, Jeon J, Han YJ, Joo Y, Lee J, Lee S, Im S. Convolutional neural network classifies pathological voice change in laryngeal cancer with high accuracy. Journal of Clinical Medicine. 2020 Oct 25;9(11):3415.
5. Arora S, Baghai-Ravary L, Tsanas A. Developing a large scale population screening tool for the assessment of Parkinson's disease using telephone-quality voice. The Journal of the Acoustical Society of America. 2019 May 9;145(5):2871-84.
6. Xia T, Han J, Mascolo C. Exploring machine learning for audio-based respiratory condition screening: A concise review of databases, methods, and open issues. Experimental Biology and Medicine. 2022 Nov;247(22):2053-61.
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8. Higuchi M, Tokuno SH, Nakamura M, Shinohara SH, Mitsuyoshi S, Omiya Y, Hagiwara NA, Takano TA, Toda HI, Saito TA, Terashi H. Classification of bipolar disorder, major depressive disorder, and healthy state using voice. Asian Journal of Pharmaceutical and Clinical Research. 2018 Oct;11(3):89-93.
9. Low DM, Bentley KH, Ghosh SS. Automated assessment of psychiatric disorders using speech: A systematic review. Laryngoscope Investig Otolaryngol. 2020;5(1):96-116. doi:10.1002/lio2.354
10. Costantini G, Cesarini V, Di Leo P, Amato F, Suppa A, Asci F, Pisani A, Calculli A, Saggio G. Artificial Intelligence-Based Voice Assessment of Patients with Parkinson’s Disease Off and On Treatment: Machine vs. Deep-Learning Comparison. Sensors. 2023 Feb 18;23(4):2293.
11. Faurholt-Jepsen M, Busk J, Frost M, et al. Voice analysis as an objective state marker in bipolar disorder. Transl Psychiatry. 2016;6(7):e856. doi:10.1038/tp.2016.123
12. Atkinson-Clement, C., Sadat, J., & Pinto, S. (2015). Behavioral treatments for speech in Parkinson's disease: meta-analyses and review of the literature. Neurodegenerative Disease Management, 5(3), 233-248.
13. Martínez-Nicolás, I., Llorente, T. E., Martínez-Sánchez, F., & Meilán, J. J. G. (2021). Ten years of research on automatic voice and speech analysis of people with Alzheimer's disease and mild cognitive impairment: a systematic review article. Frontiers in Psychology, 12, 620251.
14. Godoy, J. F., Brasolotto, A. G., Berretin-Félix, G., & Fernandes, A. Y. (2014). Neuroradiology and voice findings in stroke. In CoDAS (Vol. 26, pp. 168-174). Sociedade Brasileira de Fonoaudiologia.
15. Woodson, G. (2003). Neurological problems of the voice. Journal of Singing-The Official Journal of the National Association of Teachers of Singing, 59(4), 321-327.
16. Bjorklund NL, Fillit H, Malzbender K, Purushothama S, Kourtis L. The need for a harmonized speech dataset for Alzheimer’s disease biomarker development. Explor Med. 2020;1:359–63.
17. Wanucha G. Talk About a Revolution: The Future of Voice Biomarkers in the Neurology Clinic. Dimensions: UW Memory and Brain Wellness Center. 2019.
18. Patel D, Hall GL, Broadhurst D, Smith A, Schultz A, Foong RE. Does machine learning have a role in the prediction of asthma in children?. Paediatric Respiratory Reviews. 2022 Mar 1;41:51-60.
19. Asgari M, Chen L, Fombonne E. Quantifying voice characteristics for detecting autism. Frontiers in Psychology. 2021 Sep 7;12:665096.
ANNEX A – SCOPE OF WORK DATA ACQUISITION B2AI YEAR 1  (SEE ADDITIONAL DOCUMENTS)
Please note that not all deliverables from year 1 include patient studies and therefore these are not included in this IRB
ANNEX B: VOICE HANDICAP INDEX (VHI-10)
ANNEX C – PARTICIPATING INSTITUTIONS AND LEAD INVESTIGATORS PER SITE
Table 4 Participating Institutions and Lead investigators per site
Lead Investigator
Weill Cornell Medicine (WCM)	Alexandros Sigaras, PhD
Anais Rameau, MD, MPhil
Olivier Elemento, PhD
Massachusetts Institute of Technology (MIT)	Satrajit Ghosh, PhD
Vanderbilt University Medical Center (VUMC)	Maria Powell, PhD
Alexander Gelbard, MD
Massachusetts Eye and Ear (MEEI)	Phillip Song MD
Matthew Naunheim, MD
Emory University	Anthony Law, MD PhD
University of Toronto (UofT)	Frank Rudzicz, PhD
Hospital for Sick Children (HSC)	Alistair Johnson, DPhil
Mount Sinai Hospital (MSH)	Jordan Lerner-Ellis, PhD
Revision #	Version Date	Summary of Changes	Consent Change?
V2	May 3,2023	Modified to include pediatric cohort under single IRB	Yes
V3	August 15, 2023	Modified to include compensation for participating adults	No
V4	December 11, 2023	Modified to allow for electronic consent and change the options for consent levels. Added USF REDCap protocol language and social media recruitment.	Yes
V5	January 31, 2024	Modified to include data collection for controls. Updated personnel (USF co-investigators and outside investigators). Added new disease categories of interest.	Yes
V6	February 22, 2024	Removes Table: Disease Cohorts per Site of Data Collection	No
V7	July 19, 2024	Included e-consent and remote consent options. Changed compensation amount for participation.	Yes
V8	September 27, 2024	Included options for remote enrollment through an app used as a recruitment tool (Bridge2AI Enrollment Web app) and for remote data collection (Bridge2AI Voice Web app). Changed compensation amount for participation. Added USF co-investigator.	Yes
V9	November 15, 2024	Updated protocol to clarify the current state of research.	No
V10	January 17, 2025	Clarified that remote data collection will take place on two different platforms, the Bridge2AI Voice Web app and the Bridge2AI Voice iOS app. Added USF co-investigator.	No
V11	February 10, 2025	Updated to include data collection from Spanish language speakers. Added a new platform for patient recruitment. Added USF co-investigator. Added further sites for USF data collection. Added satisfaction survey for participants who decide not to complete data collection.	Yes
V12	May 6, 2025	Updated language about the platforms for remote consent and data collection.	No
V13	July 11, 2025	Added sentence regarding the use of flyers for recruitment. Added sentence regarding other sites for recruitment at Vanderbilt.	No
Study Title	Bridge2AI Voice Data Acquisition
Study Design	Prospective Cohort Study
Primary Objective/Purpose	To build a large multi-institutional database of human voices, speech and respiratory sounds that is ethically sourced, diverse, and linked to multimodal health biomarkers to fuel voice AI research
Secondary Objective(s)/Purposes	To build a data collection application and IT infrastructure for human voice, speech and respiratory sounds linked to other health biomarkers such as radiomics, and genomics, and supported by federated learning technology to protect data privacy. Data collected through this application will exist in the database.
Research Intervention(s)	N/A
ClinicalTrials.gov NCT #	N/A
Study Population	Participants with known diagnosed diseases from 5 disease categories (Respiratory disorders, Voice disorders, Neurological disorders, Mood disorders, Pediatric voice and speech disorders), as well as participants without the identified conditions from the above 5 disease categories who can serve as controls in the database.  Participants will be recruited primarily from individuals presenting at USF specialty clinics or at one of the participating institutions described below and included in the SINGLE IRB PROCESS.
Sample Size	30 000 participants
Study Duration for individual participants	Up to 4 years
Study Specific Abbreviations/ Definitions	Abbreviations:

Machine Learning (ML)
Artificial Intelligence (AI)
Smart Phones (SP)
CAPE-V (Consensus Auditory Perceptual Evaluation of Voice)
Rainbow Passage: a standard text that contains all the phonemes of the English language.
HVEC- High Volume Expert Clinics
COC- Community Outreach Clinics
PEDP- Plan for Enhancing Diverse Perspectives
(EHR)- Electronic Health Records

Definitions:

Participating institutions
University of South Florida, Tampa, Florida, US (USF)
Weill Cornell Medicine, New York, New York, US (WCM)
Vanderbilt University Medical Center, Nashville, Tennessee, US (VUMC)
University of Toronto, Toronto, Ontario, Canada (UofT)
Mount Sinai Hospital, Toronto, Ontario, Canada (MSH)
Massachusetts Institute of Technology (MIT), Boston, Massachusetts, US (MIT)
Hospital for Sick Children, Toronto, Ontario, Canada (HSC)
Massachusetts Eye and Ear Institute, Boston, Massachusetts, US (MEEI)
Emory University, Atlanta, Georgia, US (EU)

High Volume Expert Clinics:
These are clinics within participating institutions who see a high volume of patients with the specific disease studied (ex; The Alzheimer’s and Mild Cognitive impairment clinic at USF for Alzheimer’s).

All US-based institutions will abide to the SINGLE IRB Process. Once this protocol is approved at USF, each of the participating institution will submit review based on the SINGLE IRB at USF
The exceptions to the single IRB are the following:
Genomic data will only be collected and analyzed at the University of Toronto and Mount Sinai Hospital in Canada. Therefore, that team will have a separate protocol for genomic data collection and analysis. Once approved, a copy of the protocol and Research Ethics board approval will be submitted to USF IRB.
Canadian Institutions (MSH, SickKids and UofT) do not abide to the SINGLE IRB process and will apply for a separate REB (research ethics board application) to comply with the Canadian regulations. A copy of this approved IRB will also be submitted with their application

Longitudinal Data Collection:
For specific disease cohorts that are progressive or degenerative (i.e Alzheimer's, Parkinson’s), Longitudinal data collection may be collected.
The longitudinal data will be collected in 2 forms:
1: Longitudinal data in clinic during clinic visits:
This data collection will happen in clinic during REGULAR follow up visits. No additional visits outside of regular follow up plans for disease monitoring
2: Longitudinal data “at home”:
In between clinic visit, certain cohorts could be asked to perform voice data collection at home through participants’ own smartphone application that will be downloaded for them in clinic. This is just for longitudinal data cohort only. This data collection will be asked at intervals of 1-6 months depending on disease cohorts and for a maximum of 3 years total duration. Participants can decide to leave the study at any point during that time.
NIH Stride Partner:
  An initiative allows NIH to explore the use of cloud environments to streamline NIH data use by partnering with commercial providers. NIH’s STRIDES Initiative aims to modernize biomedical research by reducing economic and process barriers in utilizing commercial cloud services. These partnerships enable access to rich datasets and advanced computational infrastructure, tools, and services.
Audio/Video Recording	Psychophysiological Recording
Behavioral Interventions	Record Review - Educational
Behavioral Observations and Experimentations	Record Review - Employee
Deception	Record Review- Medical
Focus Groups	Record Review - Other
Interviews	Specimen collection and analysis
Investigational Device – Non-Significant Risk (e.g. Mobile Applications)	Surveys and/or questionnaires
Psychometric Testing	Other Social-Behavioral Procedures
Phase 1	Phase 2	Phase 3	Phase 4
Exploratory	Pilot	Expansion	Outreach
IT and cloud infrastructure
Software and app development
Data collection for up to 180 participants (30 participants from each disease category and controls, at main sites only
(Completed November 2023)	Data collection to cumulatively reach up to 600 participants (100 participants from each disease category and controls) at:
Main sites
HVEC in participating institutions
(Begun November 2023, ongoing in November 2024)	Expansion to COC, other HVEC and expansion phase of data acquisition to cumulatively reach data collection from up to 3000 participants (500 from each disease category and controls)	Expansion to normal cohorts though partnerships, expansion through HVEC and COC to cumulatively reach 5000 participants
Category	Acoustic	Imaging	Genomic	Descriptive/normative
Voice Disorders	Voice and Speech	Video-Laryngoscopy images	Demographic data
Validated Questionnaire Scores
Respiratory Disorders	Voice and Speech
Breathing Sounds	Chest X rays	Oxygen saturation
Demographic data
Validated Questionnaire Scores
Forced Expiratory Volumes
Mood Disorders	Voice and Speech	Demographic data
Validated Questionnaire Scores
Neurological Disorders	Voice and Speech	Brain CT Scan
Brain MRI	Whole Genome Sequencing	Demographic data
Validated Questionnaire Scores
Pediatric Disorders	Voice and Speech	Demographic data
Validated Questionnaire Scores
Controls	Voice and Speech	Demographic data
Validated Questionnaire Scores
Phase 1: Exploratory	Phase 2
Pilot	Phase 3
Expansion	Phase 4
Outreach
Single IRB process @ USF
Integration of participating institutions to Single IRB
App Development
Protocol and app refinement
IT infrastructure and data storage platform development
Preliminary data collection
Pilot data collection (began November 2023)
Expansion phase of data collection
Outreach phase of Data collection
Data sharing and access management
Transfer of data sharing to open-source access
Email	Online/Social Media Advertisement
Flyer	Record Review
Letter	SONA
News Advertisement	Other
Obtaining Signed Authorization	Waiver of HIPAA Authorization for Recruitment/Screening Purposes Only
Obtaining Online or Verbal Authorization (Alteration of HIPAA Authorization)	Waiver of HIPAA Authorization for Entire Study
Data Use Agreement	Business Associate Agreement
No Compensation	Tokens (pens, food items, etc.)
Financial Compensation (cash, gift cards)	Other
Course Credit (i.e. extra credit, SONA points)
Obtaining Signed Consent (Subject or Legally Authorized Representative)	Obtaining Consent Online (Waiver of Written Documentation of Consent )
Obtaining Signed Parental Permission	Obtaining Verbal Consent (Waiver of Written Documentation of Consent)
Obtaining Signed Assent for Children or Adults Unable to Consent	Waiving Consent and/or Parental Permission (Waiver of Consent Process)
Obtaining Verbal Assent for Children or Adults Unable to Consent	Waiving Assent/Assent is Not Appropriate
Lead Investigator	Role
USF	Yael Bensoussan MD, MSc	Co-Lead of Data Acquisition
WCM	Anais Rameau, MD, MPhil	Co-Lead of Data Acquisition
WCM	Alexandros Sigaras, PhD	Co-Lead – Tools – Software and IT infrastructure
WCM	Olivier Elemento, PhD	Co-Lead – Tools – Software and IT infrastructure
USF	Stephanie Watts, PhD, CCC-SLP	Lead Respiratory Disorders
USF	Ruth Bahr PhD, CCC-SLP	Lead Voice Disorders
MIT	Satrajit Ghosh, PhD	Lead Mood Disorders
UofT	Frank Rudzicz, PhD	Lead Neuro Disorders
USF	Tempestt Neal, PhD	Investigator – Machine Learning Readiness
USF	Karim Hanna, PhD	Investigator – Control data
USF	Stephen Aradi, MD	Investigator - Neurology
VUMC	Maria Powell, PhD	Investigator – Voice Disorders
EU	Anthony Law, MD PhD	Investigator – Voice Disorders
MEEI	Phillip Song MD	Investigator – Voice Disorders
MEEI	Matthew Naunheim, MD	Investigator – Voice Disorders
MSH	Jordan Lerner-Ellis, PhD	Lead – Genomic data (separate IRB)
HSC	Alistair Johnson	Lead – data integration


================================================================================

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SOURCE METADATA
Project: VOICE
Source ID: data_transfer_use_agreement
Source type: DUA
Source URL: https://drive.google.com/file/d/1z4zZ_Z_Jb017IoVZn5btJnSLKdEOHZPA/view?usp=sharing
Raw file: data/raw/VOICE/gdrive_1z4zZ_Z_Jb017IoVZn5btJnSLKdEOHZPA_row14.pdf
--------------------------------------------------------------------------------
Data Transfer and Use Agreement (“Agreement”)

Provider Institution: University of South Florida Board of Trustees pursuant to Agreement
concerning: Bridge2AI: Voice as a Biomarker of Health – Building an ethically sourced,
bioaccoustic database to understand disease like never before.

Recipient Institution / Company:

Recipient Scientist:

Recipient Authorized Institutional Offical:

Project Title:

Agreement Term:

Start Date:

End Date:  Two years after the Start Date, upon completion of the project, upon expiration of
the applicable ethics approval, or termination by Provider Institution, whichever occurs first.

1.  Reimbursement of Costs:

Terms and Conditions:

If applicable, Recipient shall reimburse Provider for any costs associated with the
preparation, compilation, and transfer of the Data to the Recipient.  Costs shall not
include payments for research effort by the Provider.

A.  This Agreement is in support of Agreement # _______________, which shall

cover reimbursement of costs.
B.  Costs as set forth in Attachement I.

2.  Provider shall provide the data set described in Attachment 1 (the “Data”) to Recipient

for the research purpose set forth in Attachment 1 (the “Project”).

3.   Recipient shall not use the Data except as authorized under this Agreement. The Data

will be used solely to conduct the Project and solely by Recipient Scientist and
Recipient’s faculty, employees, fellows, students, and agents (“Recipient Personnel”) (as
approved and listed in Attachment 3) that have a need to use, or provide a service in
respect of, the Data in connection with the Project and whose obligations of use are
consistent with the terms of this Agreement (collectively, “Authorized Persons”).
Collaborators at other research organizations, and other research teams at the same
organization, must apply independently for access to the Data and sign a Data Transfer
and Use Agreement (DTUA) with the Provider, before accessing the data.

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4.  Except as authorized under this Agreement or otherwise required by law, Recipient

agrees to retain control over the Data and shall not disclose, release, sell, rent, lease, loan,
or otherwise grant access to the Data to any third party, except Authorized Persons,
without the prior written consent of Provider. Recipient agrees to establish appropriate
administrative, technical, and physical safeguards to prevent unauthorized use of or
access to the Data and comply with any other special requirements relating to
safeguarding of the Data as may be set forth in Attachment 2. Recipient must also bind
Authorized Persons to hold the Data according to standards of confidentiality and
security that are equivalent to those described in this Agreement.

5.  Recipient agrees to use the Data in compliance with all applicable laws, rules, and
regulations, as well as all professional standards applicable to such research.

6.  Recipient is encouraged to make publicly available the results of the Project, in open-

access journals or pre-print servers where possible.

7.  Recipient agrees to recognize the contribution of the Provider as the source of the Data in
all written, visual, or oral public disclosures of recipient’s research using the Data, as
appropriate in accordance with scholarly standards and any specific format that has been
indicated in Attachment 1.

8.  Unless terminated earlier in accordance with this section or extended via a modification
in accordance with Section 13, this Agreement shall expire as of the End Date set forth
above. Either party may terminate this Agreement with thirty (30) days written notice to
the other party’s Authorized Official as set forth below. Upon expiration or early
termination of this Agreement, Recipient shall follow the disposition instructions
provided in Attachment 1, provided, however, that Recipient may retain one (1) copy of
the Data to the extent necessary to comply with the records retention requirements:

I. under any law, regulation, or Recipient institutional policy, and

II. for the purposes of research integrity and verification.

The restrictions set forth in this Agreement (as applicable) shall survive and apply to such
archival copy so long as Recipient holds the Data.

9.  Except as provided below or prohibited by law, any Data delivered pursuant to this

Agreement is understood to be provided “AS IS.” PROVIDER MAKES NO
REPRESENTATIONS AND EXTENDS NO WARRANTIES OF ANY KIND, EITHER
EXPRESSED OR IMPLIED. THERE ARE NO EXPRESS OR IMPLIED
WARRANTIES OF MERCHANTABILITY OR FITNESS FOR A PARTICULAR
PURPOSE, OR THAT THE USE OF THE DATA WILL NOT INFRINGE ANY
PATENT, COPYRIGHT, TRADEMARK, OR OTHER PROPRIETARY RIGHTS.
Notwithstanding, Provider, to the best of its knowledge and belief, has the right and
authority to provide the Data to Recipient for use in the Project.

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10. The Data Provider provides no guarantees that the Data is free of third-party intellectual
property rights, database rights, and other related rights. Nothing in this Agreement shall
operate to transfer to the Recipient any intellectual property rights in or relating to the
Data. Recipient agrees not to use intellectual property protection, database rights, or
related rights in a way that could prevent or limit access to, or use of, any element of the
Data or research conclusion derived from it. Recipient can elect to perform further
research that would add intellectual and resource capital to the Data and decide to obtain
intellectual property rights on these downstream discoveries.

11. Except to the extent prohibited by law, the Recipient assumes all liability for damages
which may arise from its use, storage, disclosure, or disposal of the Data. The Provider
will not be liable to the Recipient for any loss, claim, or demand made by the Recipient,
or made against the Recipient by any other party, due to or arising from the use of the
Data by the Recipient.  No indemnification for any loss, claim, damage, or liability is
intended or provided by either party under this Agreement.

12. Neither party shall use the other party’s name, trademarks, or other logos in any publicity,

advertising, or news release without the prior written approval of an authorized
representative of that party. The parties agree that each party may disclose factual
information regarding the existence and purpose of the relationship that is the subject of
this Agreement for other purposes without written permission from the other party
provided that any such statement shall accurately and appropriately describe the
relationship of the parties and shall not in any manner imply endorsement by the other
party whose name is being used.

13. Unless otherwise specified, this Agreement and the below listed Attachments embody the
entire understanding between Provider and Recipient regarding the transfer of the Data to
Recipient for the Project:

I. Attachment 1: Project Specific Information.

II. Attachment 2: Data-specific Terms and Conditions.

III. Attachment 3: Identification of Permitted Collaborators (if any).

14. No modification or waiver of this Agreement shall be valid unless in writing and

executed by duly-authorized representatives of both parties.  This Agreement shall only
be effective upon the review and subject ot the approval of the Data Access Compliance
Office (“DACO”) requiring a distinct application.

15. The undersigned Authorized Officials of Provider and Recipient expressly represent and
affirm that the contents of any statements made herein are truthful and accurate and that
they are duly authorized to sign this Agreement on behalf of their institution. This
Agreement may be executed in counterparts, including both counterparts that are
executed on paper and counterparts that are in the form of electronic records and are

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executed electronically. All executed counterparts shall constitute one agreement, and
each counterpart shall be deemed an original. The parties hereby acknowledge and agree
that electronic records and electronic signatures may be used in connection with the
execution of this Agreement and electronic signatures or signatures transmitted by
electronic mail in so-called pdf format shall be legal and binding and shall have the same
full force and effect as if a paper original of this Agreement had been delivered and
signed using a handwritten signature.

Signatures:

Provider Institutional Offical:

Provider Scientist:

_______________________

____________________

Print:

Recipient Institutional Offical:

______________________

Print:

Print:

Recipient Scientist:

___________________

Print:

Notice Address:

Notice Address:

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1.  Description of the Data:

Attachment 1:

The Bridge2AI-Voice dataset contains samples from conventional acoustic tasks including
respiratory sounds, cough sounds, and free speech prompts, capturing voice, speech and
language data relating to health and other health information. Participants who consent are
asked  to  perform  speaking  tasks  and  complete  self-reported  demographic  and  medical
history  questionnaires,  as  well  as  disease-specific  validated  questionnaires.  Participants
who  consent  also  permit  investigators  to  access  medical  information  through  EHR
platforms in order to perform gold standard validation of diagnoses and symptoms.

2.  Description of Project:

[Instructions to Drafter – Delete after completion.]

This section of this attachment should provide sufficient information such that each party
understands the project that the Recipient will perform using the Data. Content of this
section will be very similar to the Statement of Work used in other types of Agreements.
Examples of information that should be provided include:

* Objective or purpose of the Recipient’s work

* A general description of the actions to be performed by the Recipient using the Data
and possibly the anticipated results

* Include whether or not the Recipient is permitted to link the Data with other data sets
(If yes, be sure to include any special disposition requirements related to the linked data
sets in Section 4 of this attachment)

* Include application of costs, if any.

3.  Provider Support and Data Transmission:

Provider shall transmit the Data to Recipient:

Upon execution of this Agreement, Provider shall send any specific instructions
necessary to complete the transfer of the Data to the contact person listed above, if not
already included below in this section of Attachment 1.

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4.  Disposition Requirements upon the termination or expiration of the Agreement:

Two years after the Start Date, upon completion of the project, upon termination, or upon
expiration of the applicable ethics approval, whichever occurs first.  Data shall be
destroyed in accordance with instructions of Provider.  Recipient shall submit to Provider
a written certification of such data destruction within thirty (30) days after termination or
expiration signed by an appropriate representative of Recipient.

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Additional Terms and Conditions:

Attachment 2:

1.  The Data is Personally Identifiable Information, as that is defined in OMB Memorandum

M-07-16, and not covered under HIPAA, FERPA, or similar laws or regulations
governing personal information that require the addition of special terms beyond those
included in this Attachment.
☐ If checked, the Data is subject to the Federal Privacy Act of 1974, as amended, at 5
U.S.C. § 552a.

x If checked, the Data is covered under a Certificate of Confidentiality, which must be
asserted against compulsory legal demands, such as court orders and subpoenas for
identifying information or characteristics of a research participant. See Certificates of
Confidentiality (CoC) | Grants & Funding for further information.

2.  Notwithstanding any statement herein to the contrary, Provider represents that it has full
authority to share the Data it has collected with the Recipient and has confirmed that the
Project is consistent with such consents as Provider may have obtained from individuals
who are the subjects of the Data.

3.  Unless otherwise required by law or legal process, Recipient shall not use or further

disclose the Data other than as permitted by this Agreement. If Recipient believes it is
required by law or legal process to use or disclose the Data, it will promptly notify
Provider, to the extent allowed by law, prior to such use or disclosure and will disclose
the least possible amount of Data necessary to fulfill its legal obligations.

4.  In the event Recipient becomes aware of any use or disclosure of the Data not provided
for by this Agreement, Recipient shall take any appropriate steps to minimize the impact
of such unauthorized use or disclosure as soon as practicable and shall notify Provider of
such use or disclosure as soon as possible, but no later than 5 business days after
discovery of the unauthorized use or disclosure. Recipient shall cooperate with Provider
to investigate, correct, and/or mitigate such unauthorized use or disclosure. Recipient
acknowledges that Provider may have an obligation to make further notifications under
applicable state law and shall cooperate with the Provider to the extent necessary to
enable Provider to meet all such obligations.

5.  Recipient will not use the Data, either alone or in concert with any other information, to

make any effort to contact individuals who are the subjects of the Data without
appropriate Institutional Review Board (IRB) approval, specific written approval from
Provider, and informed consent from the individual, if required.

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6.  Recipient agrees to store Data with security controls adequate to protect Personally

Identifiable Information, to ensure that only Authorized Persons have access to the Data,
and to maintain appropriate control over the Data at all times. The controls shall include
administrative, physical, and technical safeguards that covered entities and business
associates must put in place to secure individuals’ electronic protected health information.
Recipient further agrees to remove and securely destroy or return, as directed by the
Provider in Attachment 1, the Data at the earliest time at which removal and destruction
or return can be accomplished, consistent with the purpose of the Project.

7.  By signing this Agreement, Recipient provides assurance that its relevant institutional
policies and applicable federal, state, or local laws and regulations (if any) have been
followed, including the completion of any IRB review or approval that may be required
prior to Recipient’s use of the Data. Upon Provider’s written request to the Recipient’s
Contact for Formal Notices identified in the signature block, Recipient shall provide
documentation of its IRB-Approved Protocol.

8.  Recipient futher agrees to adhere to the specific requirements of PhysioNet.Org managed
by the MIT Laboratory for Computational Physiology and supported by the National
Institute of Biomedical Imaging and Bioengineering (NIBIB) under NIH grant number
R01EB030362 or other data distribution as may be utilized by Provider.

9.  Provider may unilaterally amend this Agreement should the Federal sponsor require

revision.  Should recipient object to any amendment, this Agreement shall immediately
terminate and Resipient shall immediately return or destroy all Data.

10. The Authorized Representative of Recipient signing this agreement below warrants and
declares, to the best of their knowledge and belief, that Recipeint does not use coercion
for labor or services as defined in §787.06, F.S. This Agreement shall immediately
terminate upon a breach of this section by Recipient.

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Attachment 3:

To be replaced with a list of approved recipient personnel.  [any changes to the list require
amendment of the Agreement]

Name, Title and Signature of each individual

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================================================================================

FILE: physionet_b2ai-voice_1.1_row17.txt
PATH: data/preprocessed/individual/VOICE/physionet_b2ai-voice_1.1_row17.txt
SIZE: 23237 bytes
--------------------------------------------------------------------------------

SOURCE METADATA
Project: VOICE
Source ID: physionet_1_1
Source type: data resource
Source URL: https://physionet.org/content/b2ai-voice/1.1/
Raw file: data/raw/VOICE/physionet_b2ai-voice_1.1_row17.html
--------------------------------------------------------------------------------
Bridge2AI-Voice: An ethically-sourced, diverse voice dataset linked to health information v1.1
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Bridge2AI-Voice: An ethically-sourced, diverse voice dataset linked to health information
Alistair Johnson
,
Jean-Christophe Bélisle-Pipon
,
David Dorr
,
Satrajit Ghosh
,
Philip Payne
,
Maria Powell
,
Anais Rameau
,
Vardit Ravitsky
,
Alexandros Sigaras
,
Olivier Elemento
,
Yael Bensoussan
Published: Jan. 17, 2025. Version:
1.1
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Raw Audio Data Access for Bridge2AI Voice Adult Cohort is via Synapse
(March 9, 2026, 10:11 a.m.)
The published Bridge2AI-Voice Adult Dataset contains derived features from the audio waveforms. This PhysioNet project does not contain raw audios.
Accessing raw audio is a more involved process and requires institutional sign off. Please reach out to the access committee if you are interested in access: DACO@b2ai-voice.org
Data will be made available via Synapse:
https://www.synapse.org/Synapse:syn72370534/
For questions regarding the dataset itself, please contact the corresponding author, listed on the sidebar.
Note that the Bridge2AI-Voice Pediatric Dataset is also available on PhysioNet:
https://physionet.org/content/b2ai-voice-pediatric/
When using this resource, please cite:
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Johnson, A., Bélisle-Pipon, J., Dorr, D., Ghosh, S., Payne, P., Powell, M., Rameau, A., Ravitsky, V., Sigaras, A., Elemento, O., & Bensoussan, Y. (2025). Bridge2AI-Voice: An ethically-sourced, diverse voice dataset linked to health information (version 1.1).
PhysioNet
. RRID:SCR_007345.
https://doi.org/10.13026/249v-w155
@article{PhysioNet-b2ai-voice-1.1,
author = {Johnson, Alistair and Bélisle-Pipon, Jean-Christophe and Dorr, David and Ghosh, Satrajit and Payne, Philip and Powell, Maria and Rameau, Anais and Ravitsky, Vardit and Sigaras, Alexandros and Elemento, Olivier and Bensoussan, Yael},
title = {{Bridge2AI-Voice: An ethically-sourced, diverse voice dataset linked to health information}},
journal = {{PhysioNet}},
year = {2025},
month = jan,
note = {Version 1.1},
doi = {10.13026/249v-w155},
url = {https://doi.org/10.13026/249v-w155}
}
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MLA
Johnson, Alistair, et al. "Bridge2AI-Voice: An ethically-sourced, diverse voice dataset linked to health information" (version 1.1).
PhysioNet
(2025). RRID:SCR_007345.
https://doi.org/10.13026/249v-w155
APA
Johnson, A., Bélisle-Pipon, J., Dorr, D., Ghosh, S., Payne, P., Powell, M., Rameau, A., Ravitsky, V., Sigaras, A., Elemento, O., & Bensoussan, Y. (2025). Bridge2AI-Voice: An ethically-sourced, diverse voice dataset linked to health information (version 1.1).
PhysioNet
. RRID:SCR_007345.
https://doi.org/10.13026/249v-w155
Chicago
Johnson, Alistair, Bélisle-Pipon, Jean-Christophe, Dorr, David, Ghosh, Satrajit, Payne, Philip, Powell, Maria, Rameau, Anais, Ravitsky, Vardit, Sigaras, Alexandros, Elemento, Olivier, and Yael Bensoussan. "Bridge2AI-Voice: An ethically-sourced, diverse voice dataset linked to health information" (version 1.1).
PhysioNet
(2025). RRID:SCR_007345.
https://doi.org/10.13026/249v-w155
Harvard
Johnson, A., Bélisle-Pipon, J., Dorr, D., Ghosh, S., Payne, P., Powell, M., Rameau, A., Ravitsky, V., Sigaras, A., Elemento, O., and Bensoussan, Y. (2025) 'Bridge2AI-Voice: An ethically-sourced, diverse voice dataset linked to health information' (version 1.1),
PhysioNet
. RRID:SCR_007345. Available at:
https://doi.org/10.13026/249v-w155
Vancouver
Johnson A, Bélisle-Pipon J, Dorr D, Ghosh S, Payne P, Powell M, Rameau A, Ravitsky V, Sigaras A, Elemento O, Bensoussan Y. Bridge2AI-Voice: An ethically-sourced, diverse voice dataset linked to health information (version 1.1). PhysioNet. 2025. RRID:SCR_007345. Available from:
https://doi.org/10.13026/249v-w155
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@article{PhysioNet-b2ai-voice-1.1,
author = {Johnson, Alistair and Bélisle-Pipon, Jean-Christophe and Dorr, David and Ghosh, Satrajit and Payne, Philip and Powell, Maria and Rameau, Anais and Ravitsky, Vardit and Sigaras, Alexandros and Elemento, Olivier and Bensoussan, Yael},
title = {{Bridge2AI-Voice: An ethically-sourced, diverse voice dataset linked to health information}},
journal = {{PhysioNet}},
year = {2025},
month = jan,
note = {Version 1.1},
doi = {10.13026/249v-w155},
url = {https://doi.org/10.13026/249v-w155}
}
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Pollard, T., Moody, B. E., Lehman, L., Gow, B., Fernandes, C., Xie, C., Johnson, A., Mark, R. G., & Heldt, T. (2026). PhysioNet as a global platform for biomedical research. Nature Health. https://doi.org/10.1038/s44360-026-00096-z. Available from: https://rdcu.be/faatM
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APA
Pollard, T., Moody, B. E., Lehman, L., Gow, B., Fernandes, C., Xie, C., Johnson, A., Mark, R. G., & Heldt, T. (2026). PhysioNet as a global platform for biomedical research. Nature Health. https://doi.org/10.1038/s44360-026-00096-z. Available from: https://rdcu.be/faatM
MLA
Pollard, Tom, et al. “PhysioNet as a Global Platform for Biomedical Research.” Nature Health, 2026, https://doi.org/10.1038/s44360-026-00096-z. Available from: https://rdcu.be/faatM
CHICAGO
Pollard, Tom, Benjamin E. Moody, Li-wei Lehman, Brian Gow, Chrystinne Fernandes, Chen Xie, Alistair Johnson, Roger G. Mark, and Thomas Heldt. “PhysioNet as a Global Platform for Biomedical Research.” Nature Health (2026). https://doi.org/10.1038/s44360-026-00096-z.i Available from: https://rdcu.be/faatM
HARVARD
Pollard, T., Moody, B.E., Lehman, L., Gow, B., Fernandes, C., Xie, C., Johnson, A., Mark, R.G. and Heldt, T., 2026. PhysioNet as a global platform for biomedical research. Nature Health. Available at: https://doi.org/10.1038/s44360-026-00096-z. Available from: https://rdcu.be/faatM
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Pollard T, Moody BE, Lehman L, Gow B, Fernandes C, Xie C, et al. PhysioNet as a global platform for biomedical research. Nature Health. 2026. doi:10.1038/s44360-026-00096-z. Available from: https://rdcu.be/faatM
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Abstract
The human voice contains complex acoustic markers which have been linked to important health conditions including dementia, mood disorders, and cancer. When viewed as a biomarker, voice is a promising characteristic to measure as it is simple to collect, cost-effective, and has broad clinical utility. Recent advances in artificial intelligence have provided techniques to extract previously unknown prognostically useful information from dense data elements such as images. The Bridge2AI-Voice project seeks to create an ethically sourced flagship dataset to enable future research in artificial intelligence and support critical insights into the use of voice as a biomarker of health. Here we present Bridge2AI-Voice, a comprehensive collection of data derived from voice recordings with corresponding clinical information. Bridge2AI-Voice v1.0, the initial release, provides 12,523 recordings for 306 participants collected across five sites in North America. Participants were selected based on known conditions which manifest within the voice waveform including voice disorders, neurological disorders, mood disorders, and respiratory disorders. The initial release contains data considered low risk, including derivations such as spectrograms but not the original voice recordings. Detailed demographic, clinical, and validated questionnaire data are also made available.
Background
The production of human voice involves the complex interaction among respiration, phonation, resonation, and articulation. The respiratory system provides the air flow and pressure to initiate and maintain vocal fold vibration. The vocal folds generate the sound source which is then modified within the vocal tract by the oral and nasal cavities and the articulators involved in speech production. Each of these processes is influenced by the speaker’s ability to adjust and shape these interacting systems.
Although many use the terms voice and speech interchangeably, it is important to understand the distinction between the different terms used to describe human sounds:
Voice:
In the voice research field, refers to sound production and is the phonatory aspect of speech. In other words, it is the sound produced by the larynx and the resonators. For example, voice can be assessed by asking someone to do a prolonged vowel sound like /e/.
Speech:
Speech is the result of the voice being modified by the articulators and is produced with intonation and prosody. For example, a patient having a stroke can have abnormal speech production due to difficulty with articulating words but have a normal voice. For this project, the term Voice as a Biomarker of Health will include speech in its definition.
For voice to emerge as a
biomarker of health
, there is a pressing need for large, high quality, multi-institutional and diverse voice database linked to other health biomarkers from various data of different modality (demographics, imaging, genomics, risk factors, etc.) to fuel voice AI research and answer tangible clinical questions. Such an endeavor is only achievable through multi-institutional collaborations between voice experts and AI engineers, supported by bioethicists and social scientists to ensure the creation of ethically sourced voice databases representing our populations.
Based on the existing literature and ongoing research in different fields of voice research, our group identified
5 disease cohort categories
for which voice changes have been associated to specific diseases with well-recognized unmet needs. These categories were:
Voice Disorders:
Laryngeal disorders are the most studied pathologies linked to vocal changes. Benign and malignant lesions can affect the shape, mass, density, and tension of the vocal folds resulting in changes in vibratory function resulting in changes in phonation.
Neurological and Neurodegenerative Disorders:
Changes in voice have been linked to depression, and other mood disorders. Individuals with depression have been found to have decreased fundamental frequency (f0) as well as a monotonous speech, while individuals with anxiety disorders have a significant increase in F0. Regrettably, much of the literature examining the intersection of voice and speech changes in psychiatric conditions have used small datasets with limited demographic diversity reporting, lack of standardized data collection protocol precluding meta-analysis and possible confounders, all limiting external validity and clinical usability.
Mood and Psychiatric Disorders:
Voice and speech are altered in many neurological and neurodegenerative conditions. Acute strokes can present with slurred speech (Dysarthria) or expressive deficits speech (Aphasia). Voice and speech changes can be the presenting symptoms of many neurodegenerative conditions, such as Parkinson’s and ALS with changes such as slowed, low frequency, monotonous speech as well as vocal tremor.
Respiratory disorders:
Respiratory sounds, including breath, cough and voice have long been used for diagnostic purposes. For instance, pediatric croup can be suspected based on the presence of barking cough, stridor and dysphonia. With advances in acoustic recording and analysis in the second half on the twentieth century, increasing interest has emerged in the use of respiratory sounds for disease screening and therapeutic monitoring, especially with cough sounds.
Pediatric Voice and Speech Disorders:
The literature is sparser in terms of pediatric voice and speech analysis partly due to ethical concerns and challenges in data acquisition for this cohort. However, many studies have investigated the use of machine learning models for voice and speech analysis for detection of Autism and Speech Delays in the pediatric population.
The protocols used for data collection in this study have been extensively described [1].
Methods
Patients presenting at specialty clinics and institutions were considered for enrolment. Patients were selected based on membership to five predetermined groups (Respiratory disorders, Voice disorders, Neurological disorders, Mood disorders, Pediatric). Patients presenting at the given clinic were screened for inclusion and exclusion criteria prior to their visit by the project investigators. If eligible for enrolment, patient consent was sought for the data collection initiative and to share the acquired research data. Once consented, a standardized protocol for data collection was adopted. This protocol involved the collection of demographic information, health questionnaires, targeted questionnaires inquiring about known confounders for voice, disease specific information, and voice recording tasks such as sustained phonation of a vowel sound. Data collection was conducted using a custom application on a tablet with a headset used for data collection when possible. For most participants a single session was sufficient to collect all relevant data. However, a subset of participants required multiple sessions to complete the data collection. As a result, there may be more than one session per participant in the current dataset. Data were exported and converted from RedCap using an open source library developed by our team [2].
Raw audio was preprocessed by converting to monaural and resampling to 16 kHz with a Butterworth anti-aliasing filter applied. From this standardized audio, we extracted five types of derived data:
Spectrograms - Time-frequency representations were computed using the short-time Fast Fourier Transform (FFT) with a 25ms window size, 10ms hop length, and a 512-point FFT.
Mel-frequency cepstral coefficients (MFCC) - 60 MFCCs were extracted using the above spectrograms.
Acoustic features were extracted using OpenSMILE, capturing temporal dynamics and acoustic characteristics.
Phonetic and prosodic features were computed using Parselmouth and Praat, providing measures of fundamental frequency, formants, and voice quality.
Transcriptions were generated using OpenAI's Whisper Large model.
The following de-identification steps were taken in the process of preparing the dataset:
HIPAA Safe Harbor identifiers were removed.
While not all relevant to this dataset, these identifiers include: names, geographic locators, date information (at resolution finer than years), phone/fax numbers, email addresses, IP addresses, Social Security Numbers, medical record numbers, health plan beneficiary numbers, device identifiers, license numbers, account numbers, vehicle identifiers, website URLs, full face photos, biometric identifiers, and any unique identifiers.
State and province were removed. Country of data collection was retained.
Transcripts of free speech audio were removed.
In this release, audio waveforms were omitted, and only spectrograph data and other derived features are made available.
We aim to include voice data on future releases with additional precautions taken to ensure data security.
Data Description
As of v1.1, only data from the adult cohort is available.
The dataset has been made available in three files:
spectrograms.parquet - a Parquet file storing dense data derived from voice waveforms.
mfcc.parquet - a Parquet file storing MFCC data derived from the above spectrograms
phenotype.tsv - Information collected during the visit including demographics, acoustic confounders, and responses to validated questionnaires.
phenotype.json - A data dictionary for the phenotype data.
static_features.tsv - Features derived from the raw audio, with one feature per audio recording.
static_features.json - A data dictionary for the features data.
The above data dictionaries have the same overall structure: a dictionary where keys are the column names matching the associated data file, and values are dictionaries with further detail. The description value in the data dictionary provides a one sentence summary of the respective column.
The spectrograms.parquet file contains the majority of the data derived from the raw audio. Each element of the parquet formatted dataset contains a unique identifier for the participant (participant_id), a unique identifier for the recording session (session_id), the task performed (task_name), and the a 513xN dimension spectrogram of the raw audio waveform. The mfcc.parquet file contains MFCCs derived from the spectrograms, and is of size 60xN, where N is proportional to the length of the audio recording.
Features derived from the open-source Speech and Music Interpretation by Large-space Extraction (openSMILE [3]), Praat [4], parselmouth [5], and torchaudio [6, 7] are provided. Each feature is present in the static_features.tsv file, with the data dictionary providing a description of each feature, and one row per unique recording. The phenotype.tsv file is similarly a tab delimited file with one row per unique participant. Each column is the response to a question asked during clinical data collection within the custom data collection app. The phenotype.json file provides a description of each column of data.
The code used to preprocess the raw audio waveforms into the parquet file and to merge the source data into the phenotype files has been made open source in the
b2aiprep library
[8].
Usage Notes
If using Python, the parquet dataset can be loaded in with the following code:
from datasets import Dataset
ds = Dataset.from_parquet("spectrograms.parquet")
A spectrogram can be plotted in decibels by converting it from its original power representation:
import librosa
spectrogram = librosa.power_to_db(ds[0]['spectrogram'])
plt.figure(figsize=(10, 4))
plt.imshow(spectrogram, aspect='auto', origin='lower')
plt.title('Spectrogram')
plt.xlabel('Time')
plt.ylabel('Frequency')
plt.colorbar()
The phenotype file can be loaded with any statistical analysis tool. For example, the pandas library in Python can read the data:
import pandas as pd
df = pd.read_csv("phenotype.tsv", sep="\t", header=0)
Release Notes
b2ai-voice v1.1:
This release added Mel-frequency cepstral coefficients (MFCCs).
b2ai-voice v1.0:
This was the first release of the Bridge2AI voice as a biomarker of health dataset [9].
Ethics
Data collection and sharing was approved by the University of South Florida Institutional Review Board.
Acknowledgements
This project was funded by NIH project number 3OT2OD032720-01S1: Bridge2AI: Voice as a Biomarker of Health - Building an ethically sourced, bioaccoustic database to understand disease like never before. We would like to acknowledge that this release would not be possible without the graceful contribution of data from all the participants of the study. We would also like to thank the NIH for their continued support of the project.
Conflicts of Interest
None to declare.
References
Rameau, A., Ghosh, S., Sigaras, A., Elemento, O., Belisle-Pipon, J.-C., Ravitsky, V., Powell, M., Johnson, A., Dorr, D., Payne, P., Boyer, M., Watts, S., Bahr, R., Rudzicz, F., Lerner-Ellis, J., Awan, S., Bolser, D., Bensoussan, Y. (2024) Developing Multi-Disorder Voice Protocols: A team science approach involving clinical expertise, bioethics, standards, and DEI.. Proc. Interspeech 2024, 1445-1449, doi: 10.21437/Interspeech.2024-1926
Bensoussan, Y., Ghosh, S. S., Rameau, A., Boyer, M., Bahr, R., Watts, S., Rudzicz, F., Bolser, D., Lerner-Ellis, J., Awan, S., Powell, M. E., Belisle-Pipon, J.-C., Ravitsky, V., Johnson, A., Zisimopoulos, P., Tang, J., Sigaras, A., Elemento, O., Dorr, D., … Bridge2AI-Voice. (2024). Bridge2AI Voice REDCap (v3.20.0). Zenodo.
https://doi.org/10.5281/zenodo.14148755
Florian Eyben, Martin Wöllmer, Björn Schuller: "openSMILE - The Munich Versatile and Fast Open-Source Audio Feature Extractor", Proc. ACM Multimedia (MM), ACM, Florence, Italy, ISBN 978-1-60558-933-6, pp. 1459-1462, 25.-29.10.2010.
Boersma P, Van Heuven V. Speak and unSpeak with PRAAT. Glot International. 2001 Nov;5(9/10):341-7.
Jadoul Y, Thompson B, De Boer B. Introducing parselmouth: A python interface to praat. Journal of Phonetics. 2018 Nov 1;71:1-5.
Hwang, J., Hira, M., Chen, C., Zhang, X., Ni, Z., Sun, G., Ma, P., Huang, R., Pratap, V., Zhang, Y., Kumar, A., Yu, C.-Y., Zhu, C., Liu, C., Kahn, J., Ravanelli, M., Sun, P., Watanabe, S., Shi, Y., Tao, T., Scheibler, R., Cornell, S., Kim, S., & Petridis, S. (2023). TorchAudio 2.1: Advancing speech recognition, self-supervised learning, and audio processing components for PyTorch. arXiv preprint arXiv:2310.17864
Yang, Y.-Y., Hira, M., Ni, Z., Chourdia, A., Astafurov, A., Chen, C., Yeh, C.-F., Puhrsch, C., Pollack, D., Genzel, D., Greenberg, D., Yang, E. Z., Lian, J., Mahadeokar, J., Hwang, J., Chen, J., Goldsborough, P., Roy, P., Narenthiran, S., Watanabe, S., Chintala, S., Quenneville-Bélair, V, & Shi, Y. (2021). TorchAudio: Building Blocks for Audio and Speech Processing. arXiv preprint arXiv:2110.15018.
Bevers, I., Ghosh, S., Johnson, A., Brito, R., Bedrick, S., Catania, F., & Ng, E. (2017). My Research Software (Version 0.21.0) [Computer software].
https://github.com/sensein/b2aiprep
Johnson, A., Bélisle-Pipon, J., Dorr, D., Ghosh, S., Payne, P., Powell, M., Rameau, A., Ravitsky, V., Sigaras, A., Elemento, O., & Bensoussan, Y. (2024). Bridge2AI-Voice: An ethically-sourced, diverse voice dataset linked to health information (version 1.0). Health Data Nexus.
https://doi.org/10.57764/qb6h-em84
Contents
Abstract
Background
Methods
Data Description
Usage Notes
Release Notes
Ethics
Acknowledgements
Conflicts of Interest
References
Files
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Access Policy:
Only registered users who sign the specified data use agreement can access the files.
License (for files):
Bridge2AI Voice Registered Access License
Data Use Agreement:
Bridge2AI Voice Registered Access Agreement
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DOI (version 1.1):
https://doi.org/10.13026/249v-w155
DOI (latest version):
https://doi.org/10.13026/37yb-1t42
Topics:
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2.0.1
Aug. 18, 2025
3.0.0
Dec. 16, 2025
3.1.0
May 1, 2026
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Supported by the National Institute of Biomedical Imaging and Bioengineering (NIBIB), National Heart Lung and Blood Institute (NHLBI), and NIH Office of the Director under NIH grant numbers U24EB037545 and R01EB030362
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SOURCE METADATA
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Source ID: physionet_3_0_0
Source type: data resource
Source URL: https://physionet.org/content/b2ai-voice/3.0.0/
Raw file: data/raw/VOICE/physionet_b2ai-voice_3.0.0_row18.html
--------------------------------------------------------------------------------
Bridge2AI-Voice: An ethically-sourced, diverse voice dataset linked to health information v3.0.0
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Bridge2AI-Voice: An ethically-sourced, diverse voice dataset linked to health information
Yael Bensoussan
,
Alexandros Sigaras
,
Anais Rameau
,
Olivier Elemento
,
Maria Powell
,
David Dorr
,
Philip Payne
,
Vardit Ravitsky
,
Jean-Christophe Bélisle-Pipon
,
Ruth Bahr
,
Stephanie Watts
,
Donald Bolser
,
Jennifer Siu
,
Jordan Lerner-Ellis
,
Frank Rudzicz
,
Micah Boyer
,
Yassmeen Abdel-Aty
,
Toufeeq Ahmed Syed
,
James Anibal
,
Dona Amraei
,
Stephen Aradi
,
Kirollos Armosh
,
Ana Sophia Martinez
,
Shaheen Awan
,
Steven Bedrick
,
Helena Beltran
,
Alexander Bernier
,
Moroni Berrios
,
Isaac Bevers
,
Alden Blatter
,
Rahul Brito
,
Amy Brown
,
Johnathan Brown
,
Léo Cadillac
,
Selina Casalino
,
John Costello
,
Abhijeet Dalal
,
Iris De Santiago
,
Enrique Diaz-Ocampo
,
Amanda Doherty-Kirby
,
Mohamed Ebraheem
,
Ellie Eiseman
,
Mahmoud Elmahdy
,
Renee English
,
Emily Evangelista
,
Kenneth Fletcher
,
Hortense Gallois
,
Gaelyn Garrett
,
Alexander Gelbard
,
Anna Goldenberg
,
Karim Hanna
,
William Hersh
,
Jennifer Jain
,
Lochana Jayachandran
,
Kaley Jenney
,
Kathy Jenkins
,
Stacy Jo
,
Alistair Johnson
,
Ayush Kalia
,
Megha Kalia
,
Zoha Khawa
,
Cindy Kostelnik
,
Alisa Krause
,
Andrea Krussel
,
Elisa Lapadula
,
Genelle Leo
,
Justin Levinsky
,
Chloe Loewith
,
Radhika Mahajan
,
Vrishni Maharaj
,
Siyu Miao
,
LeAnn Michaels
,
Matthew Mifsud
,
Marian Mikhael
,
Elijah Moothedan
,
Yosef Nafii
,
Tempestt Neal
,
Karlee Newberry
,
Evan Ng
,
Christopher Nickel
,
Amanda Peltier
,
Trevor Pharr
,
Michaela Pnacekova
,
Matthew Pontell
,
Claire Premi-Bortolotto
,
Parnaz Rafatjou
,
JM Rahman
,
John Ramos
,
Sarah Rohde
,
Michael de Riesthal
,
Jillian Rossi
,
Laurie Russell
,
Samantha Salvi Cruz
,
Joyce Samuel
,
Suketu Shah
,
Ahmed Shawkat
,
Elizabeth Silberholz
,
John Stark
,
Lala Su
,
Shrramana Ganesh Sudhakar
,
Duncan Sutherland
,
Venkata Swarna Mukhi
,
Jeffrey Tang
,
Luka Taylor
,
Jamie Toghranegar
,
Julie Tu
,
Megan Urbano
,
Gavin Victor
,
Kimberly Vinson
,
Jordan Wilke
,
Claire Wilson
,
Madeleine Zanin
,
Xijie Zeng
,
Theresa Zesiewicz
,
Robin Zhao
,
Pantelis Zisimopoulos
,
Satrajit Ghosh
Published: Dec. 16, 2025. Version:
3.0.0
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Raw Audio Data Access for Bridge2AI Voice Adult Cohort is via Synapse
(March 9, 2026, 10:11 a.m.)
The published Bridge2AI-Voice Adult Dataset contains derived features from the audio waveforms. This PhysioNet project does not contain raw audios.
Accessing raw audio is a more involved process and requires institutional sign off. Please reach out to the access committee if you are interested in access: DACO@b2ai-voice.org
Data will be made available via Synapse:
https://www.synapse.org/Synapse:syn72370534/
For questions regarding the dataset itself, please contact the corresponding author, listed on the sidebar.
Note that the Bridge2AI-Voice Pediatric Dataset is also available on PhysioNet:
https://physionet.org/content/b2ai-voice-pediatric/
When using this resource, please cite:
Cite
Copy BibTeX
Bensoussan, Y., Sigaras, A., Rameau, A., Elemento, O., Powell, M., Dorr, D., Payne, P., Ravitsky, V., Bélisle-Pipon, J., Bahr, R., Watts, S., Bolser, D., Siu, J., Lerner-Ellis, J., Rudzicz, F., Boyer, M., Abdel-Aty, Y., Ahmed Syed, T., Anibal, J., ... Ghosh, S. (2025). Bridge2AI-Voice: An ethically-sourced, diverse voice dataset linked to health information (version 3.0.0).
PhysioNet
. RRID:SCR_007345.
https://doi.org/10.13026/k81f-qr68
@article{PhysioNet-b2ai-voice-3.0.0,
author = {Bensoussan, Yael and Sigaras, Alexandros and Rameau, Anais and Elemento, Olivier and Powell, Maria and Dorr, David and Payne, Philip and Ravitsky, Vardit and Bélisle-Pipon, Jean-Christophe and Bahr, Ruth and Watts, Stephanie and Bolser, Donald and Siu, Jennifer and Lerner-Ellis, Jordan and Rudzicz, Frank and Boyer, Micah and Abdel-Aty, Yassmeen and {Ahmed Syed}, Toufeeq and Anibal, James and Amraei, Dona and Aradi, Stephen and Armosh, Kirollos and Martinez, Ana Sophia and Awan, Shaheen and Bedrick, Steven and Beltran, Helena and Bernier, Alexander and Berrios, Moroni and Bevers, Isaac and Blatter, Alden and Brito, Rahul and Brown, Amy and Brown, Johnathan and Cadillac, Léo and Casalino, Selina and Costello, John and Dalal, Abhijeet and {De Santiago}, Iris and Diaz-Ocampo, Enrique and Doherty-Kirby, Amanda and Ebraheem, Mohamed and Eiseman, Ellie and Elmahdy, Mahmoud and English, Renee and Evangelista, Emily and Fletcher, Kenneth and Gallois, Hortense and Garrett, Gaelyn and Gelbard, Alexander and Goldenberg, Anna and Hanna, Karim and Hersh, William and Jain, Jennifer and Jayachandran, Lochana and Jenney, Kaley and Jenkins, Kathy and Jo, Stacy and Johnson, Alistair and Kalia, Ayush and Kalia, Megha and Khawa, Zoha and Kostelnik, Cindy and Krause, Alisa and Krussel, Andrea and Lapadula, Elisa and Leo, Genelle and Levinsky, Justin and Loewith, Chloe and Mahajan, Radhika and Maharaj, Vrishni and Miao, Siyu and Michaels, LeAnn and Mifsud, Matthew and Mikhael, Marian and Moothedan, Elijah and Nafii, Yosef and Neal, Tempestt and Newberry, Karlee and Ng, Evan and Nickel, Christopher and Peltier, Amanda and Pharr, Trevor and Pnacekova, Michaela and Pontell, Matthew and Premi-Bortolotto, Claire and Rafatjou, Parnaz and Rahman, JM and Ramos, John and Rohde, Sarah and {de Riesthal}, Michael and Rossi, Jillian and Russell, Laurie and {Salvi Cruz}, Samantha and Samuel, Joyce and Shah, Suketu and Shawkat, Ahmed and Silberholz, Elizabeth and Stark, John and Su, Lala and Sudhakar, Shrramana Ganesh and Sutherland, Duncan and {Swarna Mukhi}, Venkata and Tang, Jeffrey and Taylor, Luka and Toghranegar, Jamie and Tu, Julie and Urbano, Megan and Victor, Gavin and Vinson, Kimberly and Wilke, Jordan and Wilson, Claire and Zanin, Madeleine and Zeng, Xijie and Zesiewicz, Theresa and Zhao, Robin and Zisimopoulos, Pantelis and Ghosh, Satrajit},
title = {{Bridge2AI-Voice: An ethically-sourced, diverse voice dataset linked to health information}},
journal = {{PhysioNet}},
year = {2025},
month = dec,
note = {Version 3.0.0},
doi = {10.13026/k81f-qr68},
url = {https://doi.org/10.13026/k81f-qr68}
}
Cite
×
MLA
Bensoussan, Yael, et al. "Bridge2AI-Voice: An ethically-sourced, diverse voice dataset linked to health information" (version 3.0.0).
PhysioNet
(2025). RRID:SCR_007345.
https://doi.org/10.13026/k81f-qr68
APA
Bensoussan, Y., Sigaras, A., Rameau, A., Elemento, O., Powell, M., Dorr, D., Payne, P., Ravitsky, V., Bélisle-Pipon, J., Bahr, R., Watts, S., Bolser, D., Siu, J., Lerner-Ellis, J., Rudzicz, F., Boyer, M., Abdel-Aty, Y., Ahmed Syed, T., Anibal, J., ... Ghosh, S. (2025). Bridge2AI-Voice: An ethically-sourced, diverse voice dataset linked to health information (version 3.0.0).
PhysioNet
. RRID:SCR_007345.
https://doi.org/10.13026/k81f-qr68
Chicago
Bensoussan, Yael, Sigaras, Alexandros, Rameau, Anais, Elemento, Olivier, Powell, Maria, Dorr, David, Payne, Philip, Ravitsky, Vardit, Bélisle-Pipon, Jean-Christophe, Bahr, Ruth, Watts, Stephanie, Bolser, Donald, Siu, Jennifer, Lerner-Ellis, Jordan, Rudzicz, Frank, Boyer, Micah, Abdel-Aty, Yassmeen, Ahmed Syed, Toufeeq, Anibal, James, Amraei, Dona, Aradi, Stephen, Armosh, Kirollos, Martinez, Ana Sophia, Awan, Shaheen, Bedrick, Steven, Beltran, Helena, Bernier, Alexander, Berrios, Moroni, Bevers, Isaac, Blatter, Alden, Brito, Rahul, Brown, Amy, Brown, Johnathan, Cadillac, Léo, Casalino, Selina, Costello, John, Dalal, Abhijeet, De Santiago, Iris, Diaz-Ocampo, Enrique, Doherty-Kirby, Amanda, Ebraheem, Mohamed, Eiseman, Ellie, Elmahdy, Mahmoud, English, Renee, Evangelista, Emily, Fletcher, Kenneth, Gallois, Hortense, Garrett, Gaelyn, Gelbard, Alexander, Goldenberg, Anna, Hanna, Karim, Hersh, William, Jain, Jennifer, Jayachandran, Lochana, Jenney, Kaley, Jenkins, Kathy, Jo, Stacy, Johnson, Alistair, Kalia, Ayush, Kalia, Megha, Khawa, Zoha, Kostelnik, Cindy, Krause, Alisa, Krussel, Andrea, Lapadula, Elisa, Leo, Genelle, Levinsky, Justin, Loewith, Chloe, Mahajan, Radhika, Maharaj, Vrishni, Miao, Siyu, Michaels, LeAnn, Mifsud, Matthew, Mikhael, Marian, Moothedan, Elijah, Nafii, Yosef, Neal, Tempestt, Newberry, Karlee, Ng, Evan, Nickel, Christopher, Peltier, Amanda, Pharr, Trevor, Pnacekova, Michaela, Pontell, Matthew, Premi-Bortolotto, Claire, Rafatjou, Parnaz, Rahman, JM, Ramos, John, Rohde, Sarah, de Riesthal, Michael, Rossi, Jillian, Russell, Laurie, Salvi Cruz, Samantha, Samuel, Joyce, Shah, Suketu, Shawkat, Ahmed, Silberholz, Elizabeth, Stark, John, Su, Lala, Sudhakar, Shrramana Ganesh, Sutherland, Duncan, Swarna Mukhi, Venkata, Tang, Jeffrey, Taylor, Luka, Toghranegar, Jamie, Tu, Julie, Urbano, Megan, Victor, Gavin, Vinson, Kimberly, Wilke, Jordan, Wilson, Claire, Zanin, Madeleine, Zeng, Xijie, Zesiewicz, Theresa, Zhao, Robin, Zisimopoulos, Pantelis, and Satrajit Ghosh. "Bridge2AI-Voice: An ethically-sourced, diverse voice dataset linked to health information" (version 3.0.0).
PhysioNet
(2025). RRID:SCR_007345.
https://doi.org/10.13026/k81f-qr68
Harvard
Bensoussan, Y., Sigaras, A., Rameau, A., Elemento, O., Powell, M., Dorr, D., Payne, P., Ravitsky, V., Bélisle-Pipon, J., Bahr, R., Watts, S., Bolser, D., Siu, J., Lerner-Ellis, J., Rudzicz, F., Boyer, M., Abdel-Aty, Y., Ahmed Syed, T., Anibal, J., Amraei, D., Aradi, S., Armosh, K., Martinez, A. S., Awan, S., Bedrick, S., Beltran, H., Bernier, A., Berrios, M., Bevers, I., Blatter, A., Brito, R., Brown, A., Brown, J., Cadillac, L., Casalino, S., Costello, J., Dalal, A., De Santiago, I., Diaz-Ocampo, E., Doherty-Kirby, A., Ebraheem, M., Eiseman, E., Elmahdy, M., English, R., Evangelista, E., Fletcher, K., Gallois, H., Garrett, G., Gelbard, A., Goldenberg, A., Hanna, K., Hersh, W., Jain, J., Jayachandran, L., Jenney, K., Jenkins, K., Jo, S., Johnson, A., Kalia, A., Kalia, M., Khawa, Z., Kostelnik, C., Krause, A., Krussel, A., Lapadula, E., Leo, G., Levinsky, J., Loewith, C., Mahajan, R., Maharaj, V., Miao, S., Michaels, L., Mifsud, M., Mikhael, M., Moothedan, E., Nafii, Y., Neal, T., Newberry, K., Ng, E., Nickel, C., Peltier, A., Pharr, T., Pnacekova, M., Pontell, M., Premi-Bortolotto, C., Rafatjou, P., Rahman, J., Ramos, J., Rohde, S., de Riesthal, M., Rossi, J., Russell, L., Salvi Cruz, S., Samuel, J., Shah, S., Shawkat, A., Silberholz, E., Stark, J., Su, L., Sudhakar, S. G., Sutherland, D., Swarna Mukhi, V., Tang, J., Taylor, L., Toghranegar, J., Tu, J., Urbano, M., Victor, G., Vinson, K., Wilke, J., Wilson, C., Zanin, M., Zeng, X., Zesiewicz, T., Zhao, R., Zisimopoulos, P., and Ghosh, S. (2025) 'Bridge2AI-Voice: An ethically-sourced, diverse voice dataset linked to health information' (version 3.0.0),
PhysioNet
. RRID:SCR_007345. Available at:
https://doi.org/10.13026/k81f-qr68
Vancouver
Bensoussan Y, Sigaras A, Rameau A, Elemento O, Powell M, Dorr D, Payne P, Ravitsky V, Bélisle-Pipon J, Bahr R, Watts S, Bolser D, Siu J, Lerner-Ellis J, Rudzicz F, Boyer M, Abdel-Aty Y, Ahmed Syed T, Anibal J, Amraei D, Aradi S, Armosh K, Martinez A S, Awan S, Bedrick S, Beltran H, Bernier A, Berrios M, Bevers I, Blatter A, Brito R, Brown A, Brown J, Cadillac L, Casalino S, Costello J, Dalal A, De Santiago I, Diaz-Ocampo E, Doherty-Kirby A, Ebraheem M, Eiseman E, Elmahdy M, English R, Evangelista E, Fletcher K, Gallois H, Garrett G, Gelbard A, Goldenberg A, Hanna K, Hersh W, Jain J, Jayachandran L, Jenney K, Jenkins K, Jo S, Johnson A, Kalia A, Kalia M, Khawa Z, Kostelnik C, Krause A, Krussel A, Lapadula E, Leo G, Levinsky J, Loewith C, Mahajan R, Maharaj V, Miao S, Michaels L, Mifsud M, Mikhael M, Moothedan E, Nafii Y, Neal T, Newberry K, Ng E, Nickel C, Peltier A, Pharr T, Pnacekova M, Pontell M, Premi-Bortolotto C, Rafatjou P, Rahman J, Ramos J, Rohde S, de Riesthal M, Rossi J, Russell L, Salvi Cruz S, Samuel J, Shah S, Shawkat A, Silberholz E, Stark J, Su L, Sudhakar S G, Sutherland D, Swarna Mukhi V, Tang J, Taylor L, Toghranegar J, Tu J, Urbano M, Victor G, Vinson K, Wilke J, Wilson C, Zanin M, Zeng X, Zesiewicz T, Zhao R, Zisimopoulos P, Ghosh S. Bridge2AI-Voice: An ethically-sourced, diverse voice dataset linked to health information (version 3.0.0). PhysioNet. 2025. RRID:SCR_007345. Available from:
https://doi.org/10.13026/k81f-qr68
BibTeX
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@article{PhysioNet-b2ai-voice-3.0.0,
author = {Bensoussan, Yael and Sigaras, Alexandros and Rameau, Anais and Elemento, Olivier and Powell, Maria and Dorr, David and Payne, Philip and Ravitsky, Vardit and Bélisle-Pipon, Jean-Christophe and Bahr, Ruth and Watts, Stephanie and Bolser, Donald and Siu, Jennifer and Lerner-Ellis, Jordan and Rudzicz, Frank and Boyer, Micah and Abdel-Aty, Yassmeen and {Ahmed Syed}, Toufeeq and Anibal, James and Amraei, Dona and Aradi, Stephen and Armosh, Kirollos and Martinez, Ana Sophia and Awan, Shaheen and Bedrick, Steven and Beltran, Helena and Bernier, Alexander and Berrios, Moroni and Bevers, Isaac and Blatter, Alden and Brito, Rahul and Brown, Amy and Brown, Johnathan and Cadillac, Léo and Casalino, Selina and Costello, John and Dalal, Abhijeet and {De Santiago}, Iris and Diaz-Ocampo, Enrique and Doherty-Kirby, Amanda and Ebraheem, Mohamed and Eiseman, Ellie and Elmahdy, Mahmoud and English, Renee and Evangelista, Emily and Fletcher, Kenneth and Gallois, Hortense and Garrett, Gaelyn and Gelbard, Alexander and Goldenberg, Anna and Hanna, Karim and Hersh, William and Jain, Jennifer and Jayachandran, Lochana and Jenney, Kaley and Jenkins, Kathy and Jo, Stacy and Johnson, Alistair and Kalia, Ayush and Kalia, Megha and Khawa, Zoha and Kostelnik, Cindy and Krause, Alisa and Krussel, Andrea and Lapadula, Elisa and Leo, Genelle and Levinsky, Justin and Loewith, Chloe and Mahajan, Radhika and Maharaj, Vrishni and Miao, Siyu and Michaels, LeAnn and Mifsud, Matthew and Mikhael, Marian and Moothedan, Elijah and Nafii, Yosef and Neal, Tempestt and Newberry, Karlee and Ng, Evan and Nickel, Christopher and Peltier, Amanda and Pharr, Trevor and Pnacekova, Michaela and Pontell, Matthew and Premi-Bortolotto, Claire and Rafatjou, Parnaz and Rahman, JM and Ramos, John and Rohde, Sarah and {de Riesthal}, Michael and Rossi, Jillian and Russell, Laurie and {Salvi Cruz}, Samantha and Samuel, Joyce and Shah, Suketu and Shawkat, Ahmed and Silberholz, Elizabeth and Stark, John and Su, Lala and Sudhakar, Shrramana Ganesh and Sutherland, Duncan and {Swarna Mukhi}, Venkata and Tang, Jeffrey and Taylor, Luka and Toghranegar, Jamie and Tu, Julie and Urbano, Megan and Victor, Gavin and Vinson, Kimberly and Wilke, Jordan and Wilson, Claire and Zanin, Madeleine and Zeng, Xijie and Zesiewicz, Theresa and Zhao, Robin and Zisimopoulos, Pantelis and Ghosh, Satrajit},
title = {{Bridge2AI-Voice: An ethically-sourced, diverse voice dataset linked to health information}},
journal = {{PhysioNet}},
year = {2025},
month = dec,
note = {Version 3.0.0},
doi = {10.13026/k81f-qr68},
url = {https://doi.org/10.13026/k81f-qr68}
}
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Pollard, T., Moody, B. E., Lehman, L., Gow, B., Fernandes, C., Xie, C., Johnson, A., Mark, R. G., & Heldt, T. (2026). PhysioNet as a global platform for biomedical research. Nature Health. https://doi.org/10.1038/s44360-026-00096-z. Available from: https://rdcu.be/faatM
Cite
×
APA
Pollard, T., Moody, B. E., Lehman, L., Gow, B., Fernandes, C., Xie, C., Johnson, A., Mark, R. G., & Heldt, T. (2026). PhysioNet as a global platform for biomedical research. Nature Health. https://doi.org/10.1038/s44360-026-00096-z. Available from: https://rdcu.be/faatM
MLA
Pollard, Tom, et al. “PhysioNet as a Global Platform for Biomedical Research.” Nature Health, 2026, https://doi.org/10.1038/s44360-026-00096-z. Available from: https://rdcu.be/faatM
CHICAGO
Pollard, Tom, Benjamin E. Moody, Li-wei Lehman, Brian Gow, Chrystinne Fernandes, Chen Xie, Alistair Johnson, Roger G. Mark, and Thomas Heldt. “PhysioNet as a Global Platform for Biomedical Research.” Nature Health (2026). https://doi.org/10.1038/s44360-026-00096-z.i Available from: https://rdcu.be/faatM
HARVARD
Pollard, T., Moody, B.E., Lehman, L., Gow, B., Fernandes, C., Xie, C., Johnson, A., Mark, R.G. and Heldt, T., 2026. PhysioNet as a global platform for biomedical research. Nature Health. Available at: https://doi.org/10.1038/s44360-026-00096-z. Available from: https://rdcu.be/faatM
VANCOUVER
Pollard T, Moody BE, Lehman L, Gow B, Fernandes C, Xie C, et al. PhysioNet as a global platform for biomedical research. Nature Health. 2026. doi:10.1038/s44360-026-00096-z. Available from: https://rdcu.be/faatM
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Abstract
The human voice contains complex acoustic markers which have been linked to important health conditions including dementia, mood disorders, and cancer. When viewed as a biomarker, voice is a promising characteristic to measure as it is simple to collect, cost-effective, and has broad clinical utility. Recent advances in artificial intelligence have provided techniques to extract previously unknown prognostically useful information from dense data elements such as images. The Bridge2AI-Voice project seeks to create an ethically sourced flagship dataset to enable future research in artificial intelligence and support critical insights into the use of voice as a biomarker of health. Here we present Bridge2AI-Voice, a comprehensive collection of data derived from voice recordings with corresponding clinical information.
Bridge2AI-Voice v3.0 contains data for 833 participants across five sites in North America. Participants were selected based on known conditions which manifest within the voice waveform including voice disorders, neurological disorders, mood disorders, and respiratory disorders. The release contains data considered low risk, including derivations such as spectrograms but not the original voice recordings. Detailed demographic, clinical, and validated questionnaire data are also made available.
Background
The production of human voice involves the complex interaction among respiration, phonation, resonation, and articulation. The respiratory system provides the air flow and pressure to initiate and maintain vocal fold vibration. The vocal folds generate the sound source which is then modified within the vocal tract by the oral and nasal cavities and the articulators involved in speech production. Each of these processes is influenced by the speaker’s ability to adjust and shape these interacting systems.
Although many use the terms voice and speech interchangeably, it is important to understand the distinction between the different terms used to describe human sounds:
Voice:
In the voice research field, refers to sound production and is the phonatory aspect of speech. In other words, it is the sound produced by the larynx and the resonators. For example, voice can be assessed by asking someone to do a prolonged vowel sound like /e/.
Speech:
Speech is the result of the voice being modified by the articulators and is produced with intonation and prosody. For example, a patient having a stroke can have abnormal speech production due to difficulty with articulating words but have a normal voice. For this project, the term Voice as a Biomarker of Health will include speech in its definition.
For voice to emerge as a
biomarker of health
, there is a pressing need for large, high quality, multi-institutional and diverse voice database linked to other health biomarkers from various data of different modality (demographics, imaging, genomics, risk factors, etc.) to fuel voice AI research and answer tangible clinical questions. Such an endeavor is only achievable through multi-institutional collaborations between voice experts and AI engineers, supported by bioethicists and social scientists to ensure the creation of ethically sourced voice databases representing our populations.
Based on the existing literature and ongoing research in different fields of voice research, our group identified
5 disease cohort categories
for which voice changes have been associated to specific diseases with well-recognized unmet needs. These categories were:
Voice Disorders:
Laryngeal disorders are the most studied pathologies linked to vocal changes. Benign and malignant lesions can affect the shape, mass, density, and tension of the vocal folds resulting in changes in vibratory function resulting in changes in phonation.
Neurological and Neurodegenerative Disorders:
Changes in voice have been linked to depression, and other mood disorders. Individuals with depression have been found to have decreased fundamental frequency (f0) as well as a monotonous speech, while individuals with anxiety disorders have a significant increase in F0. Regrettably, much of the literature examining the intersection of voice and speech changes in psychiatric conditions have used small datasets with limited demographic diversity reporting, lack of standardized data collection protocol precluding meta-analysis and possible confounders, all limiting external validity and clinical usability.
Mood and Psychiatric Disorders:
Voice and speech are altered in many neurological and neurodegenerative conditions. Acute strokes can present with slurred speech (Dysarthria) or expressive deficits speech (Aphasia). Voice and speech changes can be the presenting symptoms of many neurodegenerative conditions, such as Parkinson’s and ALS with changes such as slowed, low frequency, monotonous speech as well as vocal tremor.
Respiratory disorders:
Respiratory sounds, including breath, cough and voice have long been used for diagnostic purposes. For instance, pediatric croup can be suspected based on the presence of barking cough, stridor and dysphonia. With advances in acoustic recording and analysis in the second half on the twentieth century, increasing interest has emerged in the use of respiratory sounds for disease screening and therapeutic monitoring, especially with cough sounds.
Pediatric Voice and Speech Disorders:
The literature is sparser in terms of pediatric voice and speech analysis partly due to ethical concerns and challenges in data acquisition for this cohort. However, many studies have investigated the use of machine learning models for voice and speech analysis for detection of Autism and Speech Delays in the pediatric population.
The protocols used for data collection in this study have been extensively described [1].
Methods
Patients presenting at specialty clinics and institutions were considered for enrollment. Patients were selected based on membership to five predetermined groups (Respiratory disorders, Voice disorders, Neurological disorders, Mood disorders, Pediatric). Patients presenting at the given clinic were screened for inclusion and exclusion criteria prior to their visit by the project investigators. If eligible for enrollment, patient consent was sought for the data collection initiative and to share the acquired research data. Once consented, a standardized protocol for data collection was adopted. This protocol involved the collection of demographic information, health questionnaires, targeted questionnaires inquiring about known confounders for voice, disease specific information, and voice recording tasks such as sustained phonation of a vowel sound. Data collection was conducted using a custom application on a tablet with a headset used for data collection when possible. For most participants a single session was sufficient to collect all relevant data. However, a subset of participants required multiple sessions to complete the data collection. As a result, there may be more than one session per participant in the current dataset. Data were exported and converted from RedCap using an open source library developed by our team [2].
Raw audio was preprocessed by converting to monaural and resampling to 16 kHz with a Butterworth anti-aliasing filter applied. From this standardized audio, we extracted five types of derived data:
Spectrograms - Time-frequency representations were computed using the short-time Fast Fourier Transform (FFT) with a 25ms window size, 10ms hop length, and a 400-point FFT. Spectrograms were further downsampled by a factor of two in the time domain after derivation.
Mel-frequency cepstral coefficients (MFCC) - 60 MFCCs were extracted using the above spectrograms.
Mel Spectrogram - a combination of the above two computed with the same parameters (25ms window size, 10ms hop length, a 400-point FFT, and 60 Mels).
Articulatory features - using the Speech Articulatory Coding (sparc) package, we generate the kinematic traces of vocal tract articulators and source features as well as measures of loudness, periodicity, and pitch. All features were gathered at 50Hz.
Acoustic features were extracted using OpenSMILE, capturing temporal dynamics and acoustic characteristics.
Phonetic and prosodic features were computed using Parselmouth and Praat, providing measures of fundamental frequency, formants, and voice quality.
Phonetic Posteriorgrams (ppgs) - time-varying categorical distribution over acoustic units of speech (e.g., phonemes) at 100Hz were generated via the ppgs package [10].
Transcriptions were generated using OpenAI's Whisper Large model.
The following de-identification steps were taken in the process of preparing the dataset:
HIPAA Safe Harbor identifiers were removed.
While not all relevant to this dataset, these identifiers include: names, geographic locators, date information (at resolution finer than years), phone/fax numbers, email addresses, IP addresses, Social Security Numbers, medical record numbers, health plan beneficiary numbers, device identifiers, license numbers, account numbers, vehicle identifiers, website URLs, full face photos, biometric identifiers, and any unique identifiers.
State and province were removed. Country of data collection was retained.
Spectrograms and similar features were excluded if the audio contained free speech. Static and other features which do not encode the sensitive information were retained.
Data Description
The dataset contains both derived audio data features (under features) and phenotypic information acquired during data collection.
Features
Binary files are made available as Parquet, an open-source
column-oriented data file format.
The following dense binary files are available in the features subfolder:
ppgs.parquet
sparc_ema.parquet
sparc_loudness.parquet
sparc_periodicity.parquet
sparc_pitch.parquet
torchaudio_spectrogram.parquet
torchaudio_mfcc.parquet
torchaudio_pitch.parquet
torchaudio_mel_spectrogram.parquet
In addition to these files, the features folder contains the following plain-text files:
static_features.tsv - Features derived from the raw audio, with one feature per audio recording.
All of the above files are associated with a data dictionary file which has the same file stem and a JSON suffixes (e.g. torch_spectrogram.json). These data dictionary files contain a description of the feature and detail on the processing done to prepare the feature.
Each of the parquet files is formatted similarly. Each element of the parquet formatted dataset contains a unique identifier for the participant (participant_id), a unique identifier for the recording session (session_id), the task performed (task_name), the number of time frames associated with that feature (n_frames) and the tensor data for that associated feature which are described in more detail below where the feature is after the software used to extract it:
torchaudio_spectrograms.parquet (n=29020) contains spectrograms of dimension 201xT generated using the short-time Fast Fourier Transform (FFT) with a 25ms window size, 10ms hop length, and a 400-point FFT.
torchaudio_mel_spectrograms.parquet (n=29020) contains Mel spectrograms of dimension 60xT generated with a 25ms window size, 10ms hop length, a 400-point FFT and 60 Mel bins.
torchaudio_mfcc.parquet (n=29020) contains Mel-frequency cepstrum coefficients of dimension 60xT using the same parameters as the mel spectrograms.
torchaudio_pitch.parquet (n=32236) contains the detected pitch (fundamental frequency) over time and is of dimension T with a min and max pitch of 80 and 500 respectively.
sparc_ema.parquet (n=31616) contains the estimated electromagnetic articulography (EMA) using a deep learning model with dimensions Tx12 where the 12 correspond to X/Y positions of six articulators: tongue dorsum (TD), tongue body (TB), tongue tip (TT), lower incisor (LI), upper lip (UL), lower lip (LL), respectively.
sparc_loudness.parquet (n=31616) contains the estimated loudness based on the average absolute amplitude of the audio waveform of size T, using 20ms windows.
sparc_periodicity.parquet (n=31633) contains the estimated periodicity (confidence of pitch presence) derived from the audio using 20ms windows of dimension T.
sparc_pitch.parquet (n=31633) contains the estimated fundamental frequency (F0) of the audio signal using a different algorithm than before with a range of 50-550Hz and dimension T.
ppgs.parquet (n=29031) contains the phonetic posteriorgram probabilities across 40 phoneme categories giving a dimension of 40xT with a frame rate of 100Hz.
Spectrograms, Mel Spectrograms, MFC coefficients, PPGs, and EMAs for sensitive records and audio checks have been removed from v3.0. Additionally, some files, whether due to length or other issues, could not generate certain features and so are not included in the bundled data.
Features derived from the open-source Speech and Music Interpretation by Large-space Extraction (openSMILE [3]), Praat [4], parselmouth [5], and torchaudio [6, 7] are provided. Each feature is present in the static_features.tsv file, with the data dictionary providing a description of each feature, and one row per unique recording.
Phenotype
The phenotype subfolder contains organized information collected from the participant or other individual during their encounter:
.
├── confounders
│   ├── confounders.json
│   └── confounders.tsv
├── demographics
│   ├── demographics.json
│   └── demographics.tsv
├── diagnosis
│   ├── adhd_adult.json
│   ├── adhd_adult.tsv
│   ├── airway_stenosis.json
│   ├── airway_stenosis.tsv
│   ├── amyotrophic_lateral_sclerosis.json
│   ├── amyotrophic_lateral_sclerosis.tsv
│   ├── anxiety.json
│   ├── anxiety.tsv
│   ├── benign_lesions.json
│   ├── benign_lesions.tsv
│   ├── bipolar_disorder.json
│   ├── bipolar_disorder.tsv
│   ├── cognitive_impairment.json
│   ├── cognitive_impairment.tsv
│   ├── control.json
│   ├── control.tsv
│   ├── copd_and_asthma.json
│   ├── copd_and_asthma.tsv
│   ├── depression.json
│   ├── depression.tsv
│   ├── glottic_insufficiency.json
│   ├── glottic_insufficiency.tsv
│   ├── laryngeal_cancer.json
│   ├── laryngeal_cancer.tsv
│   ├── laryngeal_dystonia.json
│   ├── laryngeal_dystonia.tsv
│   ├── laryngitis.json
│   ├── laryngitis.tsv
│   ├── muscle_tension_dysphonia.json
│   ├── muscle_tension_dysphonia.tsv
│   ├── parkinsons_disease.json
│   ├── parkinsons_disease.tsv
│   ├── precancerous_lesions.json
│   ├── precancerous_lesions.tsv
│   ├── psychiatric_history.json
│   ├── psychiatric_history.tsv
│   ├── ptsd_adult.json
│   ├── ptsd_adult.tsv
│   ├── unexplained_chronic_cough.json
│   ├── unexplained_chronic_cough.tsv
│   ├── unilateral_vocal_fold_paralysis.json
│   └── unilateral_vocal_fold_paralysis.tsv
├── enrollment
│   ├── eligibility.json
│   ├── eligibility.tsv
│   ├── enrollment_form.json
│   ├── enrollment_form.tsv
│   ├── participant.json
│   └── participant.tsv
├── questionnaire
│   ├── custom_affect_scale.json
│   ├── custom_affect_scale.tsv
│   ├── dsm5_adult.json
│   ├── dsm5_adult.tsv
│   ├── dyspnea_index.json
│   ├── dyspnea_index.tsv
│   ├── gad7_anxiety.json
│   ├── gad7_anxiety.tsv
│   ├── leicester_cough_questionnaire.json
│   ├── leicester_cough_questionnaire.tsv
│   ├── panas.json
│   ├── panas.tsv
│   ├── phq9.json
│   ├── phq9.tsv
│   ├── productive_vocabulary.json
│   ├── productive_vocabulary.tsv
│   ├── vhi10.json
│   ├── vhi10.tsv
│   ├── voice_perception.json
│   └── voice_perception.tsv
└── task
├── acoustic_task.json
├── acoustic_task.tsv
├── harvard_sentences.json
├── harvard_sentences.tsv
├── random_item_generation.json
├── random_item_generation.tsv
├── recording.json
├── recording.tsv
├── session.json
├── session.tsv
├── stroop.json
├── stroop.tsv
├── voice_perception.json
├── voice_perception.tsv
├── voice_problem_severity.json
├── voice_problem_severity.tsv
├── winograd.json
└── winograd.tsv
Phenotype data files only contain rows for a participant if at least one column is not missing. As visible, all of the TSV data files have a data dictionary available with the data file. The data dictionary has the same file stem but a distinct suffix: json. For phenotype data, dictionary files have keys with the same name as the column names in the associated data file. The values for each element provide detail of the column, including a description field which provides a one sentence summary of the respective column, the question (if any) that was asked the participant to prompt the answer, and the data type of the response.
Note that as participants may have repeated visits in order to collect data, there may be more than one row per participant in the data files. Furthermore, there is no requirement the participant provide the same response for each visit. As a result, participant information for the same data element may vary across the data file.
The code used to process the raw audio into the above features and to merge the source data into the phenotype files has been made open source in the
b2aiprep library
[8]. This release was generated with b2aiprep v3.0.0.
Usage Notes
If using Python, the parquet dataset can be loaded in with any library that supports parquet. For example, the HuggingFace Datasets library can be used to load in the spectrograms:
from datasets import Dataset
ds = Dataset.from_parquet("torchaudio_spectrogram.parquet")
A spectrogram can be plotted in decibels by converting it from its original power representation:
import librosa
spectrogram = librosa.power_to_db(np.asarray(ds[0]['spectrogram']))
plt.figure(figsize=(10, 4))
plt.imshow(spectrogram, aspect='auto', origin='lower')
plt.title('Spectrogram')
plt.xlabel('Time')
plt.ylabel('Frequency')
plt.colorbar()
The phenotype file can be loaded with any statistical analysis tool. For example, the pandas library in Python can read the data:
import pandas as pd
df = pd.read_csv("demographics.tsv", sep="\t", header=0)
Release Notes
b2ai-voice v3.0.0:
A major update with new data for an additional 391 participants. The single phenotype data file has been separated into more user-friendly and intuitive individual files. Additional features were provided from the Speech Articulatory Coding (sparc) package as well as Phonetic Posteriorgrams from the ppgs package. The files have been reorganized.
b2ai-voice v2.0.1:
Corrections in the authorship list.
b2ai-voice v2.0:
This release provides data for an additional 136 new participants. Spectrograms were reprocessed to fix some minor issues identified in the previous release. All spectograms and Mel-frequency cepstral coefficients from free speech related files have been removed.
b2ai-voice v1.1:
This release added Mel-frequency cepstral coefficients (MFCCs).
b2ai-voice v1.0:
This was the first release of the Bridge2AI voice as a biomarker of health dataset [9].
Ethics
Data collection and sharing was approved by the University of South Florida Institutional Review Board.
Acknowledgements
This release would not be possible without the graceful contribution of data from all the participants of the study.
This project was funded by NIH project number 3OT2OD032720-01S1: Bridge2AI: Voice as a Biomarker of Health - Building an ethically sourced, bioaccoustic database to understand disease like never before. We would also like to thank the NIH for their continued support of the project.
Conflicts of Interest
None to declare.
References
Rameau, A., Ghosh, S., Sigaras, A., Elemento, O., Belisle-Pipon, J.-C., Ravitsky, V., Powell, M., Johnson, A., Dorr, D., Payne, P., Boyer, M., Watts, S., Bahr, R., Rudzicz, F., Lerner-Ellis, J., Awan, S., Bolser, D., Bensoussan, Y. (2024) Developing Multi-Disorder Voice Protocols: A team science approach involving clinical expertise, bioethics, standards, and DEI.. Proc. Interspeech 2024, 1445-1449, doi: 10.21437/Interspeech.2024-1926
Bensoussan, Y., Ghosh, S. S., Rameau, A., Boyer, M., Bahr, R., Watts, S., Rudzicz, F., Bolser, D., Lerner-Ellis, J., Awan, S., Powell, M. E., Belisle-Pipon, J.-C., Ravitsky, V., Johnson, A., Zisimopoulos, P., Tang, J., Sigaras, A., Elemento, O., Dorr, D., … Bridge2AIVoice. (2024). Bridge2AI Voice REDCap (v3.23.0). Zenodo.
https://zenodo.org/records/14989503
Florian Eyben, Martin Wöllmer, Björn Schuller: "openSMILE - The Munich Versatile and Fast Open-Source Audio Feature Extractor", Proc. ACM Multimedia (MM), ACM, Florence, Italy, ISBN 978-1-60558-933-6, pp. 1459-1462, 25.-29.10.2010.
Boersma P, Van Heuven V. Speak and unSpeak with PRAAT. Glot International. 2001 Nov;5(9/10):341-7.
Jadoul Y, Thompson B, De Boer B. Introducing parselmouth: A python interface to praat. Journal of Phonetics. 2018 Nov 1;71:1-5.
Hwang, J., Hira, M., Chen, C., Zhang, X., Ni, Z., Sun, G., Ma, P., Huang, R., Pratap, V., Zhang, Y., Kumar, A., Yu, C.-Y., Zhu, C., Liu, C., Kahn, J., Ravanelli, M., Sun, P., Watanabe, S., Shi, Y., Tao, T., Scheibler, R., Cornell, S., Kim, S., & Petridis, S. (2023). TorchAudio 2.1: Advancing speech recognition, self-supervised learning, and audio processing components for PyTorch. arXiv preprint arXiv:2310.17864
Yang, Y.-Y., Hira, M., Ni, Z., Chourdia, A., Astafurov, A., Chen, C., Yeh, C.-F., Puhrsch, C., Pollack, D., Genzel, D., Greenberg, D., Yang, E. Z., Lian, J., Mahadeokar, J., Hwang, J., Chen, J., Goldsborough, P., Roy, P., Narenthiran, S., Watanabe, S., Chintala, S., Quenneville-Bélair, V, & Shi, Y. (2021). TorchAudio: Building Blocks for Audio and Speech Processing. arXiv preprint arXiv:2110.15018.
Bevers, I., Ghosh, S., Johnson, A., Brito, R., Bedrick, S., Catania, F., & Ng, E. (2017). b2aiprep library (Version 3.0.0) [Computer software].
https://github.com/sensein/b2aiprep
Johnson, A., Bélisle-Pipon, J., Dorr, D., Ghosh, S., Payne, P., Powell, M., Rameau, A., Ravitsky, V., Sigaras, A., Elemento, O., & Bensoussan, Y. (2024). Bridge2AI-Voice: An ethically-sourced, diverse voice dataset linked to health information (version 1.0). Health Data Nexus.
https://doi.org/10.57764/qb6h-em84
C. Churchwell, M. Morrison, and B. Pardo, "High-Fidelity Neural Phonetic Posteriorgrams," ICASSP 2024 Workshop on Explainable Machine Learning for Speech and Audio, April 2024.
Contents
Abstract
Background
Methods
Data Description
Usage Notes
Release Notes
Ethics
Acknowledgements
Conflicts of Interest
References
Files
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Access Policy:
Only credentialed users who sign the DUA can access the files.
License (for files):
Bridge2AI Voice Registered Access License
Data Use Agreement:
Bridge2AI Voice Registered Access Agreement
Required training:
No training required
Discovery
DOI (version 3.0.0):
https://doi.org/10.13026/k81f-qr68
DOI (latest version):
https://doi.org/10.13026/37yb-1t42
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Project Website:
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================================================================================

FILE: physionet_b2ai-voice_3.1.0_2026-07-24.txt
PATH: data/preprocessed/individual/VOICE/physionet_b2ai-voice_3.1.0_2026-07-24.txt
SIZE: 42778 bytes
--------------------------------------------------------------------------------

SOURCE METADATA
Project: VOICE
Source ID: physionet_3_1_0
Source type: data resource
Source URL: https://physionet.org/content/b2ai-voice/3.1.0/
Raw file: data/raw/VOICE/physionet_b2ai-voice_3.1.0_2026-07-24.html
--------------------------------------------------------------------------------
Bridge2AI-Voice: An ethically-sourced, diverse voice dataset linked to health information v3.1.0
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Credentialed Access
Bridge2AI-Voice: An ethically-sourced, diverse voice dataset linked to health information
Yael Bensoussan
,
Alexandros Sigaras
,
Anais Rameau
,
Olivier Elemento
,
Maria Powell
,
David Dorr
,
Philip Payne
,
Vardit Ravitsky
,
Jean-Christophe Bélisle-Pipon
,
Ruth Bahr
,
Stephanie Watts
,
Donald Bolser
,
Jennifer Siu
,
Jordan Lerner-Ellis
,
Frank Rudzicz
,
Micah Boyer
,
Yassmeen Abdel-Aty
,
Toufeeq Ahmed Syed
,
James Anibal
,
Dona Amraei
,
Stephen Aradi
,
Kirollos Armosh
,
Ana Sophia Martinez
,
Shaheen Awan
,
Steven Bedrick
,
Helena Beltran
,
Alexander Bernier
,
Moroni Berrios
,
Isaac Bevers
,
Alden Blatter
,
Rahul Brito
,
Amy Brown
,
Johnathan Brown
,
Léo Cadillac
,
Selina Casalino
,
John Costello
,
Abhijeet Dalal
,
Iris De Santiago
,
Enrique Diaz-Ocampo
,
Amanda Doherty-Kirby
,
Mohamed Ebraheem
,
Ellie Eiseman
,
Mahmoud Elmahdy
,
Renee English
,
Emily Evangelista
,
Kenneth Fletcher
,
Hortense Gallois
,
Gaelyn Garrett
,
Alexander Gelbard
,
Omar Ghaffar
,
Amer Ghavanini
,
Anna Goldenberg
,
Karim Hanna
,
William Hersh
,
Jennifer Jain
,
Lochana Jayachandran
,
Kaley Jenney
,
Kathy Jenkins
,
Stacy Jo
,
Alistair Johnson
,
Ayush Kalia
,
Megha Kalia
,
Zoha Khawa
,
Kenji Kobayashi
,
Cindy Kostelnik
,
Alisa Krause
,
Andrea Krussel
,
Elisa Lapadula
,
Genelle Leo
,
Justin Levinsky
,
Chloe Loewith
,
Radhika Mahajan
,
Vrishni Maharaj
,
Siyu Miao
,
LeAnn Michaels
,
Matthew Mifsud
,
Marian Mikhael
,
Elijah Moothedan
,
Yosef Nafii
,
Tempestt Neal
,
Karlee Newberry
,
Evan Ng
,
Christopher Nickel
,
Amanda Peltier
,
Trevor Pharr
,
Michaela Pnacekova
,
Matthew Pontell
,
Jaiden Potter
,
Claire Premi-Bortolotto
,
Parnaz Rafatjou
,
JM Rahman
,
Gayathiri Rajkumar
,
John Ramos
,
Michael de Riesthal
,
Sarah Rohde
,
Jillian Rossi
,
Laurie Russell
,
Samantha Salvi Cruz
,
Joyce Samuel
,
Suketu Shah
,
Ahmed Shawkat
,
Elizabeth Silberholz
,
John Stark
,
Lala Su
,
Shrramana Ganesh Sudhakar
,
Duncan Sutherland
,
Venkata Swarna Mukhi
,
Jeffrey Tang
,
Luka Taylor
,
Jamie Toghranegar
,
Julie Tu
,
Megan Urbano
,
Gavin Victor
,
Kimberly Vinson
,
Jordan Wilke
,
Claire Wilson
,
Madeleine Zanin
,
Xijie Zeng
,
Theresa Zesiewicz
,
Robin Zhao
,
Pantelis Zisimopoulos
,
Satrajit Ghosh
Published: May 1, 2026. Version:
3.1.0
Raw Audio Data Access for Bridge2AI Voice Adult Cohort is via Synapse
(March 9, 2026, 10:11 a.m.)
The published Bridge2AI-Voice Adult Dataset contains derived features from the audio waveforms. This PhysioNet project does not contain raw audios.
Accessing raw audio is a more involved process and requires institutional sign off. Please reach out to the access committee if you are interested in access: DACO@b2ai-voice.org
Data will be made available via Synapse:
https://www.synapse.org/Synapse:syn72370534/
For questions regarding the dataset itself, please contact the corresponding author, listed on the sidebar.
Note that the Bridge2AI-Voice Pediatric Dataset is also available on PhysioNet:
https://physionet.org/content/b2ai-voice-pediatric/
When using this resource, please cite:
Cite
Copy BibTeX
Bensoussan, Y., Sigaras, A., Rameau, A., Elemento, O., Powell, M., Dorr, D., Payne, P., Ravitsky, V., Bélisle-Pipon, J., Bahr, R., Watts, S., Bolser, D., Siu, J., Lerner-Ellis, J., Rudzicz, F., Boyer, M., Abdel-Aty, Y., Ahmed Syed, T., Anibal, J., ... Ghosh, S. (2026). Bridge2AI-Voice: An ethically-sourced, diverse voice dataset linked to health information (version 3.1.0).
PhysioNet
. RRID:SCR_007345.
https://doi.org/10.13026/8xbn-nq66
@article{PhysioNet-b2ai-voice-3.1.0,
author = {Bensoussan, Yael and Sigaras, Alexandros and Rameau, Anais and Elemento, Olivier and Powell, Maria and Dorr, David and Payne, Philip and Ravitsky, Vardit and Bélisle-Pipon, Jean-Christophe and Bahr, Ruth and Watts, Stephanie and Bolser, Donald and Siu, Jennifer and Lerner-Ellis, Jordan and Rudzicz, Frank and Boyer, Micah and Abdel-Aty, Yassmeen and {Ahmed Syed}, Toufeeq and Anibal, James and Amraei, Dona and Aradi, Stephen and Armosh, Kirollos and Martinez, Ana Sophia and Awan, Shaheen and Bedrick, Steven and Beltran, Helena and Bernier, Alexander and Berrios, Moroni and Bevers, Isaac and Blatter, Alden and Brito, Rahul and Brown, Amy and Brown, Johnathan and Cadillac, Léo and Casalino, Selina and Costello, John and Dalal, Abhijeet and {De Santiago}, Iris and Diaz-Ocampo, Enrique and Doherty-Kirby, Amanda and Ebraheem, Mohamed and Eiseman, Ellie and Elmahdy, Mahmoud and English, Renee and Evangelista, Emily and Fletcher, Kenneth and Gallois, Hortense and Garrett, Gaelyn and Gelbard, Alexander and Ghaffar, Omar and Ghavanini, Amer and Goldenberg, Anna and Hanna, Karim and Hersh, William and Jain, Jennifer and Jayachandran, Lochana and Jenney, Kaley and Jenkins, Kathy and Jo, Stacy and Johnson, Alistair and Kalia, Ayush and Kalia, Megha and Khawa, Zoha and Kobayashi, Kenji and Kostelnik, Cindy and Krause, Alisa and Krussel, Andrea and Lapadula, Elisa and Leo, Genelle and Levinsky, Justin and Loewith, Chloe and Mahajan, Radhika and Maharaj, Vrishni and Miao, Siyu and Michaels, LeAnn and Mifsud, Matthew and Mikhael, Marian and Moothedan, Elijah and Nafii, Yosef and Neal, Tempestt and Newberry, Karlee and Ng, Evan and Nickel, Christopher and Peltier, Amanda and Pharr, Trevor and Pnacekova, Michaela and Pontell, Matthew and Potter, Jaiden and Premi-Bortolotto, Claire and Rafatjou, Parnaz and Rahman, JM and Rajkumar, Gayathiri and Ramos, John and {de Riesthal}, Michael and Rohde, Sarah and Rossi, Jillian and Russell, Laurie and {Salvi Cruz}, Samantha and Samuel, Joyce and Shah, Suketu and Shawkat, Ahmed and Silberholz, Elizabeth and Stark, John and Su, Lala and Sudhakar, Shrramana Ganesh and Sutherland, Duncan and {Swarna Mukhi}, Venkata and Tang, Jeffrey and Taylor, Luka and Toghranegar, Jamie and Tu, Julie and Urbano, Megan and Victor, Gavin and Vinson, Kimberly and Wilke, Jordan and Wilson, Claire and Zanin, Madeleine and Zeng, Xijie and Zesiewicz, Theresa and Zhao, Robin and Zisimopoulos, Pantelis and Ghosh, Satrajit},
title = {{Bridge2AI-Voice: An ethically-sourced, diverse voice dataset linked to health information}},
journal = {{PhysioNet}},
year = {2026},
month = may,
note = {Version 3.1.0},
doi = {10.13026/8xbn-nq66},
url = {https://doi.org/10.13026/8xbn-nq66}
}
Cite
×
MLA
Bensoussan, Yael, et al. "Bridge2AI-Voice: An ethically-sourced, diverse voice dataset linked to health information" (version 3.1.0).
PhysioNet
(2026). RRID:SCR_007345.
https://doi.org/10.13026/8xbn-nq66
APA
Bensoussan, Y., Sigaras, A., Rameau, A., Elemento, O., Powell, M., Dorr, D., Payne, P., Ravitsky, V., Bélisle-Pipon, J., Bahr, R., Watts, S., Bolser, D., Siu, J., Lerner-Ellis, J., Rudzicz, F., Boyer, M., Abdel-Aty, Y., Ahmed Syed, T., Anibal, J., ... Ghosh, S. (2026). Bridge2AI-Voice: An ethically-sourced, diverse voice dataset linked to health information (version 3.1.0).
PhysioNet
. RRID:SCR_007345.
https://doi.org/10.13026/8xbn-nq66
Chicago
Bensoussan, Yael, Sigaras, Alexandros, Rameau, Anais, Elemento, Olivier, Powell, Maria, Dorr, David, Payne, Philip, Ravitsky, Vardit, Bélisle-Pipon, Jean-Christophe, Bahr, Ruth, Watts, Stephanie, Bolser, Donald, Siu, Jennifer, Lerner-Ellis, Jordan, Rudzicz, Frank, Boyer, Micah, Abdel-Aty, Yassmeen, Ahmed Syed, Toufeeq, Anibal, James, Amraei, Dona, Aradi, Stephen, Armosh, Kirollos, Martinez, Ana Sophia, Awan, Shaheen, Bedrick, Steven, Beltran, Helena, Bernier, Alexander, Berrios, Moroni, Bevers, Isaac, Blatter, Alden, Brito, Rahul, Brown, Amy, Brown, Johnathan, Cadillac, Léo, Casalino, Selina, Costello, John, Dalal, Abhijeet, De Santiago, Iris, Diaz-Ocampo, Enrique, Doherty-Kirby, Amanda, Ebraheem, Mohamed, Eiseman, Ellie, Elmahdy, Mahmoud, English, Renee, Evangelista, Emily, Fletcher, Kenneth, Gallois, Hortense, Garrett, Gaelyn, Gelbard, Alexander, Ghaffar, Omar, Ghavanini, Amer, Goldenberg, Anna, Hanna, Karim, Hersh, William, Jain, Jennifer, Jayachandran, Lochana, Jenney, Kaley, Jenkins, Kathy, Jo, Stacy, Johnson, Alistair, Kalia, Ayush, Kalia, Megha, Khawa, Zoha, Kobayashi, Kenji, Kostelnik, Cindy, Krause, Alisa, Krussel, Andrea, Lapadula, Elisa, Leo, Genelle, Levinsky, Justin, Loewith, Chloe, Mahajan, Radhika, Maharaj, Vrishni, Miao, Siyu, Michaels, LeAnn, Mifsud, Matthew, Mikhael, Marian, Moothedan, Elijah, Nafii, Yosef, Neal, Tempestt, Newberry, Karlee, Ng, Evan, Nickel, Christopher, Peltier, Amanda, Pharr, Trevor, Pnacekova, Michaela, Pontell, Matthew, Potter, Jaiden, Premi-Bortolotto, Claire, Rafatjou, Parnaz, Rahman, JM, Rajkumar, Gayathiri, Ramos, John, de Riesthal, Michael, Rohde, Sarah, Rossi, Jillian, Russell, Laurie, Salvi Cruz, Samantha, Samuel, Joyce, Shah, Suketu, Shawkat, Ahmed, Silberholz, Elizabeth, Stark, John, Su, Lala, Sudhakar, Shrramana Ganesh, Sutherland, Duncan, Swarna Mukhi, Venkata, Tang, Jeffrey, Taylor, Luka, Toghranegar, Jamie, Tu, Julie, Urbano, Megan, Victor, Gavin, Vinson, Kimberly, Wilke, Jordan, Wilson, Claire, Zanin, Madeleine, Zeng, Xijie, Zesiewicz, Theresa, Zhao, Robin, Zisimopoulos, Pantelis, and Satrajit Ghosh. "Bridge2AI-Voice: An ethically-sourced, diverse voice dataset linked to health information" (version 3.1.0).
PhysioNet
(2026). RRID:SCR_007345.
https://doi.org/10.13026/8xbn-nq66
Harvard
Bensoussan, Y., Sigaras, A., Rameau, A., Elemento, O., Powell, M., Dorr, D., Payne, P., Ravitsky, V., Bélisle-Pipon, J., Bahr, R., Watts, S., Bolser, D., Siu, J., Lerner-Ellis, J., Rudzicz, F., Boyer, M., Abdel-Aty, Y., Ahmed Syed, T., Anibal, J., Amraei, D., Aradi, S., Armosh, K., Martinez, A. S., Awan, S., Bedrick, S., Beltran, H., Bernier, A., Berrios, M., Bevers, I., Blatter, A., Brito, R., Brown, A., Brown, J., Cadillac, L., Casalino, S., Costello, J., Dalal, A., De Santiago, I., Diaz-Ocampo, E., Doherty-Kirby, A., Ebraheem, M., Eiseman, E., Elmahdy, M., English, R., Evangelista, E., Fletcher, K., Gallois, H., Garrett, G., Gelbard, A., Ghaffar, O., Ghavanini, A., Goldenberg, A., Hanna, K., Hersh, W., Jain, J., Jayachandran, L., Jenney, K., Jenkins, K., Jo, S., Johnson, A., Kalia, A., Kalia, M., Khawa, Z., Kobayashi, K., Kostelnik, C., Krause, A., Krussel, A., Lapadula, E., Leo, G., Levinsky, J., Loewith, C., Mahajan, R., Maharaj, V., Miao, S., Michaels, L., Mifsud, M., Mikhael, M., Moothedan, E., Nafii, Y., Neal, T., Newberry, K., Ng, E., Nickel, C., Peltier, A., Pharr, T., Pnacekova, M., Pontell, M., Potter, J., Premi-Bortolotto, C., Rafatjou, P., Rahman, J., Rajkumar, G., Ramos, J., de Riesthal, M., Rohde, S., Rossi, J., Russell, L., Salvi Cruz, S., Samuel, J., Shah, S., Shawkat, A., Silberholz, E., Stark, J., Su, L., Sudhakar, S. G., Sutherland, D., Swarna Mukhi, V., Tang, J., Taylor, L., Toghranegar, J., Tu, J., Urbano, M., Victor, G., Vinson, K., Wilke, J., Wilson, C., Zanin, M., Zeng, X., Zesiewicz, T., Zhao, R., Zisimopoulos, P., and Ghosh, S. (2026) 'Bridge2AI-Voice: An ethically-sourced, diverse voice dataset linked to health information' (version 3.1.0),
PhysioNet
. RRID:SCR_007345. Available at:
https://doi.org/10.13026/8xbn-nq66
Vancouver
Bensoussan Y, Sigaras A, Rameau A, Elemento O, Powell M, Dorr D, Payne P, Ravitsky V, Bélisle-Pipon J, Bahr R, Watts S, Bolser D, Siu J, Lerner-Ellis J, Rudzicz F, Boyer M, Abdel-Aty Y, Ahmed Syed T, Anibal J, Amraei D, Aradi S, Armosh K, Martinez A S, Awan S, Bedrick S, Beltran H, Bernier A, Berrios M, Bevers I, Blatter A, Brito R, Brown A, Brown J, Cadillac L, Casalino S, Costello J, Dalal A, De Santiago I, Diaz-Ocampo E, Doherty-Kirby A, Ebraheem M, Eiseman E, Elmahdy M, English R, Evangelista E, Fletcher K, Gallois H, Garrett G, Gelbard A, Ghaffar O, Ghavanini A, Goldenberg A, Hanna K, Hersh W, Jain J, Jayachandran L, Jenney K, Jenkins K, Jo S, Johnson A, Kalia A, Kalia M, Khawa Z, Kobayashi K, Kostelnik C, Krause A, Krussel A, Lapadula E, Leo G, Levinsky J, Loewith C, Mahajan R, Maharaj V, Miao S, Michaels L, Mifsud M, Mikhael M, Moothedan E, Nafii Y, Neal T, Newberry K, Ng E, Nickel C, Peltier A, Pharr T, Pnacekova M, Pontell M, Potter J, Premi-Bortolotto C, Rafatjou P, Rahman J, Rajkumar G, Ramos J, de Riesthal M, Rohde S, Rossi J, Russell L, Salvi Cruz S, Samuel J, Shah S, Shawkat A, Silberholz E, Stark J, Su L, Sudhakar S G, Sutherland D, Swarna Mukhi V, Tang J, Taylor L, Toghranegar J, Tu J, Urbano M, Victor G, Vinson K, Wilke J, Wilson C, Zanin M, Zeng X, Zesiewicz T, Zhao R, Zisimopoulos P, Ghosh S. Bridge2AI-Voice: An ethically-sourced, diverse voice dataset linked to health information (version 3.1.0). PhysioNet. 2026. RRID:SCR_007345. Available from:
https://doi.org/10.13026/8xbn-nq66
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@article{PhysioNet-b2ai-voice-3.1.0,
author = {Bensoussan, Yael and Sigaras, Alexandros and Rameau, Anais and Elemento, Olivier and Powell, Maria and Dorr, David and Payne, Philip and Ravitsky, Vardit and Bélisle-Pipon, Jean-Christophe and Bahr, Ruth and Watts, Stephanie and Bolser, Donald and Siu, Jennifer and Lerner-Ellis, Jordan and Rudzicz, Frank and Boyer, Micah and Abdel-Aty, Yassmeen and {Ahmed Syed}, Toufeeq and Anibal, James and Amraei, Dona and Aradi, Stephen and Armosh, Kirollos and Martinez, Ana Sophia and Awan, Shaheen and Bedrick, Steven and Beltran, Helena and Bernier, Alexander and Berrios, Moroni and Bevers, Isaac and Blatter, Alden and Brito, Rahul and Brown, Amy and Brown, Johnathan and Cadillac, Léo and Casalino, Selina and Costello, John and Dalal, Abhijeet and {De Santiago}, Iris and Diaz-Ocampo, Enrique and Doherty-Kirby, Amanda and Ebraheem, Mohamed and Eiseman, Ellie and Elmahdy, Mahmoud and English, Renee and Evangelista, Emily and Fletcher, Kenneth and Gallois, Hortense and Garrett, Gaelyn and Gelbard, Alexander and Ghaffar, Omar and Ghavanini, Amer and Goldenberg, Anna and Hanna, Karim and Hersh, William and Jain, Jennifer and Jayachandran, Lochana and Jenney, Kaley and Jenkins, Kathy and Jo, Stacy and Johnson, Alistair and Kalia, Ayush and Kalia, Megha and Khawa, Zoha and Kobayashi, Kenji and Kostelnik, Cindy and Krause, Alisa and Krussel, Andrea and Lapadula, Elisa and Leo, Genelle and Levinsky, Justin and Loewith, Chloe and Mahajan, Radhika and Maharaj, Vrishni and Miao, Siyu and Michaels, LeAnn and Mifsud, Matthew and Mikhael, Marian and Moothedan, Elijah and Nafii, Yosef and Neal, Tempestt and Newberry, Karlee and Ng, Evan and Nickel, Christopher and Peltier, Amanda and Pharr, Trevor and Pnacekova, Michaela and Pontell, Matthew and Potter, Jaiden and Premi-Bortolotto, Claire and Rafatjou, Parnaz and Rahman, JM and Rajkumar, Gayathiri and Ramos, John and {de Riesthal}, Michael and Rohde, Sarah and Rossi, Jillian and Russell, Laurie and {Salvi Cruz}, Samantha and Samuel, Joyce and Shah, Suketu and Shawkat, Ahmed and Silberholz, Elizabeth and Stark, John and Su, Lala and Sudhakar, Shrramana Ganesh and Sutherland, Duncan and {Swarna Mukhi}, Venkata and Tang, Jeffrey and Taylor, Luka and Toghranegar, Jamie and Tu, Julie and Urbano, Megan and Victor, Gavin and Vinson, Kimberly and Wilke, Jordan and Wilson, Claire and Zanin, Madeleine and Zeng, Xijie and Zesiewicz, Theresa and Zhao, Robin and Zisimopoulos, Pantelis and Ghosh, Satrajit},
title = {{Bridge2AI-Voice: An ethically-sourced, diverse voice dataset linked to health information}},
journal = {{PhysioNet}},
year = {2026},
month = may,
note = {Version 3.1.0},
doi = {10.13026/8xbn-nq66},
url = {https://doi.org/10.13026/8xbn-nq66}
}
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Pollard, T., Moody, B. E., Lehman, L., Gow, B., Fernandes, C., Xie, C., Johnson, A., Mark, R. G., & Heldt, T. (2026). PhysioNet as a global platform for biomedical research. Nature Health. https://doi.org/10.1038/s44360-026-00096-z. Available from: https://rdcu.be/faatM
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APA
Pollard, T., Moody, B. E., Lehman, L., Gow, B., Fernandes, C., Xie, C., Johnson, A., Mark, R. G., & Heldt, T. (2026). PhysioNet as a global platform for biomedical research. Nature Health. https://doi.org/10.1038/s44360-026-00096-z. Available from: https://rdcu.be/faatM
MLA
Pollard, Tom, et al. “PhysioNet as a Global Platform for Biomedical Research.” Nature Health, 2026, https://doi.org/10.1038/s44360-026-00096-z. Available from: https://rdcu.be/faatM
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Pollard, Tom, Benjamin E. Moody, Li-wei Lehman, Brian Gow, Chrystinne Fernandes, Chen Xie, Alistair Johnson, Roger G. Mark, and Thomas Heldt. “PhysioNet as a Global Platform for Biomedical Research.” Nature Health (2026). https://doi.org/10.1038/s44360-026-00096-z.i Available from: https://rdcu.be/faatM
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Pollard, T., Moody, B.E., Lehman, L., Gow, B., Fernandes, C., Xie, C., Johnson, A., Mark, R.G. and Heldt, T., 2026. PhysioNet as a global platform for biomedical research. Nature Health. Available at: https://doi.org/10.1038/s44360-026-00096-z. Available from: https://rdcu.be/faatM
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Pollard T, Moody BE, Lehman L, Gow B, Fernandes C, Xie C, et al. PhysioNet as a global platform for biomedical research. Nature Health. 2026. doi:10.1038/s44360-026-00096-z. Available from: https://rdcu.be/faatM
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Abstract
The human voice contains complex acoustic markers which have been linked to important health conditions including dementia, mood disorders, and cancer. When viewed as a biomarker, voice is a promising characteristic to measure as it is simple to collect, cost-effective, and has broad clinical utility. Recent advances in artificial intelligence have provided techniques to extract previously unknown prognostically useful information from dense data elements such as images. The Bridge2AI-Voice project seeks to create an ethically sourced flagship dataset to enable future research in artificial intelligence and support critical insights into the use of voice as a biomarker of health. Here we present Bridge2AI-Voice, a comprehensive collection of data derived from voice recordings with corresponding clinical information.
Bridge2AI-Voice v3.1 contains data for 833 participants across five sites in North America. Participants were selected based on known conditions which manifest within the voice waveform including voice disorders, neurological disorders, mood disorders, and respiratory disorders. The release contains data considered low risk, including derivations such as spectrograms but not the original voice recordings. Detailed demographic, clinical, and validated questionnaire data are also made available.
Background
The production of human voice involves the complex interaction among respiration, phonation, resonation, and articulation. The respiratory system provides the air flow and pressure to initiate and maintain vocal fold vibration. The vocal folds generate the sound source which is then modified within the vocal tract by the oral and nasal cavities and the articulators involved in speech production. Each of these processes is influenced by the speaker’s ability to adjust and shape these interacting systems.
Although many use the terms voice and speech interchangeably, it is important to understand the distinction between the different terms used to describe human sounds:
Voice:
In the voice research field, refers to sound production and is the phonatory aspect of speech. In other words, it is the sound produced by the larynx and the resonators. For example, voice can be assessed by asking someone to do a prolonged vowel sound like /e/.
Speech:
Speech is the result of the voice being modified by the articulators and is produced with intonation and prosody. For example, a patient having a stroke can have abnormal speech production due to difficulty with articulating words but have a normal voice. For this project, the term Voice as a Biomarker of Health will include speech in its definition.
For voice to emerge as a
biomarker of health
, there is a pressing need for large, high quality, multi-institutional and diverse voice database linked to other health biomarkers from various data of different modality (demographics, imaging, genomics, risk factors, etc.) to fuel voice AI research and answer tangible clinical questions. Such an endeavor is only achievable through multi-institutional collaborations between voice experts and AI engineers, supported by bioethicists and social scientists to ensure the creation of ethically sourced voice databases representing our populations.
Based on the existing literature and ongoing research in different fields of voice research, our group identified
5 disease cohort categories
for which voice changes have been associated to specific diseases with well-recognized unmet needs. These categories were:
Voice Disorders:
Laryngeal disorders are the most studied pathologies linked to vocal changes. Benign and malignant lesions can affect the shape, mass, density, and tension of the vocal folds resulting in changes in vibratory function resulting in changes in phonation.
Neurological and Neurodegenerative Disorders:
Changes in voice have been linked to depression, and other mood disorders. Individuals with depression have been found to have decreased fundamental frequency (f0) as well as a monotonous speech, while individuals with anxiety disorders have a significant increase in F0. Regrettably, much of the literature examining the intersection of voice and speech changes in psychiatric conditions have used small datasets with limited demographic diversity reporting, lack of standardized data collection protocol precluding meta-analysis and possible confounders, all limiting external validity and clinical usability.
Mood and Psychiatric Disorders:
Voice and speech are altered in many neurological and neurodegenerative conditions. Acute strokes can present with slurred speech (Dysarthria) or expressive deficits speech (Aphasia). Voice and speech changes can be the presenting symptoms of many neurodegenerative conditions, such as Parkinson’s and ALS with changes such as slowed, low frequency, monotonous speech as well as vocal tremor.
Respiratory disorders:
Respiratory sounds, including breath, cough and voice have long been used for diagnostic purposes. For instance, pediatric croup can be suspected based on the presence of barking cough, stridor and dysphonia. With advances in acoustic recording and analysis in the second half on the twentieth century, increasing interest has emerged in the use of respiratory sounds for disease screening and therapeutic monitoring, especially with cough sounds.
Pediatric Voice and Speech Disorders:
The literature is sparser in terms of pediatric voice and speech analysis partly due to ethical concerns and challenges in data acquisition for this cohort. However, many studies have investigated the use of machine learning models for voice and speech analysis for detection of Autism and Speech Delays in the pediatric population.
The protocols used for data collection in this study have been extensively described [1].
Methods
Patients presenting at specialty clinics and institutions were considered for enrollment. Patients were selected based on membership to five predetermined groups (Respiratory disorders, Voice disorders, Neurological disorders, Mood disorders, Pediatric). Patients presenting at the given clinic were screened for inclusion and exclusion criteria prior to their visit by the project investigators. If eligible for enrollment, patient consent was sought for the data collection initiative and to share the acquired research data. Once consented, a standardized protocol for data collection was adopted. This protocol involved the collection of demographic information, health questionnaires, targeted questionnaires inquiring about known confounders for voice, disease specific information, and voice recording tasks such as sustained phonation of a vowel sound. Data collection was conducted using a custom application on a tablet with a headset used for data collection when possible. For most participants a single session was sufficient to collect all relevant data. However, a subset of participants required multiple sessions to complete the data collection. As a result, there may be more than one session per participant in the current dataset. Data were exported and converted from RedCap using an open source library developed by our team [2].
Raw audio was preprocessed by converting to monaural and resampling to 16 kHz with a Butterworth anti-aliasing filter applied. From this standardized audio, we extracted five types of derived data:
Spectrograms - Time-frequency representations were computed using the short-time Fast Fourier Transform (FFT) with a 25ms window size, 10ms hop length, and a 400-point FFT. Spectrograms were further downsampled by a factor of two in the time domain after derivation.
Mel-frequency cepstral coefficients (MFCC) - 60 MFCCs were extracted using the above spectrograms.
Mel Spectrogram - a combination of the above two computed with the same parameters (25ms window size, 10ms hop length, a 400-point FFT, and 60 Mels).
Articulatory features - using the Speech Articulatory Coding (sparc) package, we generate the kinematic traces of vocal tract articulators and source features as well as measures of loudness, periodicity, and pitch. All features were gathered at 50Hz.
Acoustic features were extracted using OpenSMILE, capturing temporal dynamics and acoustic characteristics.
Phonetic and prosodic features were computed using Parselmouth and Praat, providing measures of fundamental frequency, formants, and voice quality.
Phonetic Posteriorgrams (ppgs) - time-varying categorical distribution over acoustic units of speech (e.g., phonemes) at 100Hz were generated via the ppgs package [10].
Transcriptions were generated using OpenAI's Whisper Large model.
The following de-identification steps were taken in the process of preparing the dataset:
HIPAA Safe Harbor identifiers were removed.
While not all relevant to this dataset, these identifiers include: names, geographic locators, date information (at resolution finer than years), phone/fax numbers, email addresses, IP addresses, Social Security Numbers, medical record numbers, health plan beneficiary numbers, device identifiers, license numbers, account numbers, vehicle identifiers, website URLs, full face photos, biometric identifiers, and any unique identifiers.
State and province were removed. Country of data collection was retained.
Spectrograms and similar features were excluded if the audio contained free speech. Static and other features which do not encode the sensitive information were retained.
Data Description
The dataset contains both derived audio data features (under features) and phenotypic information acquired during data collection, as well as metadata related to the specific audio task and recording.
Features
Binary files are made available as Parquet, an open-source
column-oriented data file format.
The following dense binary files are available in the features subfolder:
ppgs.parquet
sparc_ema.parquet
sparc_loudness.parquet
sparc_periodicity.parquet
sparc_pitch.parquet
torchaudio_spectrogram.parquet
torchaudio_mfcc.parquet
torchaudio_pitch.parquet
torchaudio_mel_spectrogram.parquet
In addition to these files, the features folder contains the following plain-text files:
audio_quality_metrics.tsv - One feature per audio recording derived from the raw audio related to quality control metrics.
static_features.tsv - Features derived from the raw audio, with one feature per audio recording.
All of the above files are associated with a data dictionary file which has the same file stem and a JSON suffixes (e.g. torch_spectrogram.json). These data dictionary files contain a description of the feature and detail on the processing done to prepare the feature.
Each of the parquet files is formatted similarly. Each element of the parquet formatted dataset contains a unique identifier for the participant (participant_id), a unique identifier for the recording session (session_id), the task performed (task_name), the number of time frames associated with that feature (n_frames) and the tensor data for that associated feature which are described in more detail below where the feature is after the software used to extract it:
torchaudio_spectrograms.parquet (n=29278) contains spectrograms of dimension 201xT generated using the short-time Fast Fourier Transform (FFT) with a 25ms window size, 10ms hop length, and a 400-point FFT.
torchaudio_mel_spectrograms.parquet (n=29278) contains Mel spectrograms of dimension 60xT generated with a 25ms window size, 10ms hop length, a 400-point FFT and 60 Mel bins.
torchaudio_mfcc.parquet (n=29278) contains Mel-frequency cepstrum coefficients of dimension 60xT using the same parameters as the mel spectrograms.
torchaudio_pitch.parquet (n=32522) contains the detected pitch (fundamental frequency) over time and is of dimension T with a min and max pitch of 80 and 500 respectively.
sparc_ema.parquet (n=28640) contains the estimated electromagnetic articulography (EMA) using a deep learning model with dimensions Tx12 where the 12 correspond to X/Y positions of six articulators: tongue dorsum (TD), tongue body (TB), tongue tip (TT), lower incisor (LI), upper lip (UL), lower lip (LL), respectively.
sparc_loudness.parquet (n=31855) contains the estimated loudness based on the average absolute amplitude of the audio waveform of size T, using 20ms windows.
sparc_periodicity.parquet (n=31872) contains the estimated periodicity (confidence of pitch presence) derived from the audio using 20ms windows of dimension T.
sparc_pitch.parquet (n=31872) contains the estimated fundamental frequency (F0) of the audio signal using a different algorithm than before with a range of 50-550Hz and dimension T.
ppgs.parquet (n=29289) contains the phonetic posteriorgram probabilities across 40 phoneme categories giving a dimension of 40xT with a frame rate of 100Hz.
Spectrograms, Mel Spectrograms, MFC coefficients, PPGs, and EMAs for sensitive records and audio checks have been removed from v3.1. Additionally, some files, whether due to length or other issues, could not generate certain features and so are not included in the bundled data.
Features derived from the open-source Speech and Music Interpretation by Large-space Extraction (openSMILE [3]), Praat [4], parselmouth [5], and torchaudio [6, 7] are provided. Each feature is present in the static_features.tsv file, with the data dictionary providing a description of each feature, and one row per unique recording.
Metadata
The metadata folder contains a binary file made available as Parquet and its corresponding data dictionary. This includes specific information about the recording, including task instructions and prompts, as well as information about the microphone used and its gain.
Phenotype
The phenotype subfolder contains organized information collected from the participant or other individual during their encounter:
.
├── confounders
│   ├── confounders.json
│   └── confounders.tsv
├── demographics
│   ├── demographics.json
│   └── demographics.tsv
├── diagnosis
│   ├── airway_stenosis.json
│   ├── airway_stenosis.tsv
│   ├── amyotrophic_lateral_sclerosis.json
│   ├── amyotrophic_lateral_sclerosis.tsv
│   ├── anxiety.json
│   ├── anxiety.tsv
│   ├── benign_lesions.json
│   ├── benign_lesions.tsv
│   ├── bipolar_disorder.json
│   ├── bipolar_disorder.tsv
│   ├── cognitive_impairment.json
│   ├── cognitive_impairment.tsv
│   ├── control.json
│   ├── control.tsv
│   ├── copd_and_asthma.json
│   ├── copd_and_asthma.tsv
│   ├── depression.json
│   ├── depression.tsv
│   ├── glottic_insufficiency.json
│   ├── glottic_insufficiency.tsv
│   ├── laryngeal_cancer.json
│   ├── laryngeal_cancer.tsv
│   ├── laryngeal_dystonia.json
│   ├── laryngeal_dystonia.tsv
│   ├── laryngitis.json
│   ├── laryngitis.tsv
│   ├── muscle_tension_dysphonia.json
│   ├── muscle_tension_dysphonia.tsv
│   ├── parkinsons_disease.json
│   ├── parkinsons_disease.tsv
│   ├── precancerous_lesions.json
│   ├── precancerous_lesions.tsv
│   ├── unexplained_chronic_cough.json
│   ├── unexplained_chronic_cough.tsv
│   ├── unilateral_vocal_fold_paralysis.json
│   └── unilateral_vocal_fold_paralysis.tsv
├── enrollment
│   ├── eligibility.json
│   ├── eligibility.tsv
│   ├── enrollment_form.json
│   ├── enrollment_form.tsv
│   ├── participant.json
│   └── participant.tsv
├── questionnaire
│   ├── adhd_adult.json
│   ├── adhd_adult.tsv
│   ├── custom_affect_scale.json
│   ├── custom_affect_scale.tsv
│   ├── dsm5_adult.json
│   ├── dsm5_adult.tsv
│   ├── dyspnea_index.json
│   ├── dyspnea_index.tsv
│   ├── gad7_anxiety.json
│   ├── gad7_anxiety.tsv
│   ├── leicester_cough_questionnaire.json
│   ├── leicester_cough_questionnaire.tsv
│   ├── panas.json
│   ├── panas.tsv
│   ├── phq9.json
│   ├── phq9.tsv
│   ├── productive_vocabulary.json
│   ├── productive_vocabulary.tsv
│   ├── psychiatric_history.json
│   ├── psychiatric_history.tsv
│   ├── ptsd_adult.json
│   ├── ptsd_adult.tsv
│   ├── vhi10.json
│   ├── vhi10.tsv
│   ├── voice_perception.json
│   └── voice_perception.tsv
└── task
├── acoustic_task.json
├── acoustic_task.tsv
├── harvard_sentences.json
├── harvard_sentences.tsv
├── random_item_generation.json
├── random_item_generation.tsv
├── recording.json
├── recording.tsv
├── session.json
├── session.tsv
├── stroop.json
├── stroop.tsv
├── winograd.json
└── winograd.tsv
Phenotype data files only contain rows for a participant if at least one column is not missing. As visible, all of the TSV data files have a data dictionary available with the data file. The data dictionary has the same file stem but a distinct suffix: json. For phenotype data, dictionary files have keys with the same name as the column names in the associated data file. The values for each element provide detail of the column, including a description field which provides a one sentence summary of the respective column, the question (if any) that was asked the participant to prompt the answer, and the data type of the response.
Note that as participants may have repeated visits in order to collect data, there may be more than one row per participant in the data files. Furthermore, there is no requirement the participant provide the same response for each visit. As a result, participant information for the same data element may vary across the data file.
The code used to process the raw audio into the above features and to merge the source data into the phenotype files has been made open source in the
b2aiprep library
[8]. This release was generated with b2aiprep v3.0.0.
Usage Notes
If using Python, the parquet dataset can be loaded in with any library that supports parquet. For example, the HuggingFace Datasets library can be used to load in the spectrograms:
from datasets import Dataset
ds = Dataset.from_parquet("torchaudio_spectrogram.parquet")
A spectrogram can be plotted in decibels by converting it from its original power representation:
import librosa
spectrogram = librosa.power_to_db(np.asarray(ds[0]['spectrogram']))
plt.figure(figsize=(10, 4))
plt.imshow(spectrogram, aspect='auto', origin='lower')
plt.title('Spectrogram')
plt.xlabel('Time')
plt.ylabel('Frequency')
plt.colorbar()
The phenotype file can be loaded with any statistical analysis tool. For example, the pandas library in Python can read the data:
import pandas as pd
df = pd.read_csv("demographics.tsv", sep="\t", header=0)
Release Notes
b2ai-voice v3.1.0:
Minor update with no new participants, but some additional data released for certain participants. Fixed broken parquet files from previous release. Adds files related to audio quality metrics and releases per audio metadata. Updated validated diagnosis information across conditions with back-filled information. Some pehnotypic files rearranged and certain gold standard variable names were renamed.
b2ai-voice v3.0.0:
A major update with new data for an additional 391 participants. The single phenotype data file has been separated into more user-friendly and intuitive individual files. Additional features were provided from the Speech Articulatory Coding (sparc) package as well as Phonetic Posteriorgrams from the ppgs package. The files have been reorganized.
b2ai-voice v2.0.1:
Corrections in the authorship list.
b2ai-voice v2.0:
This release provides data for an additional 136 new participants. Spectrograms were reprocessed to fix some minor issues identified in the previous release. All spectograms and Mel-frequency cepstral coefficients from free speech related files have been removed.
b2ai-voice v1.1:
This release added Mel-frequency cepstral coefficients (MFCCs).
b2ai-voice v1.0:
This was the first release of the Bridge2AI voice as a biomarker of health dataset [9].
Ethics
Data collection and sharing was approved by the University of South Florida Institutional Review Board.
Acknowledgements
This release would not be possible without the graceful contribution of data from all the participants of the study.
This project was funded by NIH project number 3OT2OD032720-01S1: Bridge2AI: Voice as a Biomarker of Health - Building an ethically sourced, bioaccoustic database to understand disease like never before. We would also like to thank the NIH for their continued support of the project.
Conflicts of Interest
None to declare.
References
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Bevers, I., Ghosh, S., Johnson, A., Brito, R., Bedrick, S., Catania, F., & Ng, E. (2017). b2aiprep library (Version 3.0.0) [Computer software].
https://github.com/sensein/b2aiprep
Johnson, A., Bélisle-Pipon, J., Dorr, D., Ghosh, S., Payne, P., Powell, M., Rameau, A., Ravitsky, V., Sigaras, A., Elemento, O., & Bensoussan, Y. (2024). Bridge2AI-Voice: An ethically-sourced, diverse voice dataset linked to health information (version 1.0). Health Data Nexus.
https://doi.org/10.57764/qb6h-em84
C. Churchwell, M. Morrison, and B. Pardo, "High-Fidelity Neural Phonetic Posteriorgrams," ICASSP 2024 Workshop on Explainable Machine Learning for Speech and Audio, April 2024.
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FILE: physionet_b2ai-voice-pediatric_1.1.0_2026-07-24.txt
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SOURCE METADATA
Project: VOICE
Source ID: physionet_pediatric_1_1_0
Source type: data resource
Source URL: https://physionet.org/content/b2ai-voice-pediatric/1.1.0/
Raw file: data/raw/VOICE/physionet_b2ai-voice-pediatric_1.1.0_2026-07-24.html
--------------------------------------------------------------------------------
Bridge2AI-Voice Pediatric Dataset v1.1.0
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Bridge2AI-Voice Pediatric Dataset
Yael Bensoussan
,
Alexandros Sigaras
,
Anais Rameau
,
Olivier Elemento
,
Maria Powell
,
David Dorr
,
Philip Payne
,
Vardit Ravitsky
,
Jean-Christophe Bélisle-Pipon
,
Ruth Bahr
,
Stephanie Watts
,
Donald Bolser
,
Jennifer Siu
,
Jordan Lerner-Ellis
,
Frank Rudzicz
,
Micah Boyer
,
Yassmeen Abdel-Aty
,
Toufeeq Ahmed Syed
,
Dona Amraei
,
James Anibal
,
Stephen Aradi
,
Kirollos Armosh
,
Ana Sophia Martinez
,
Shaheen Awan
,
Steven Bedrick
,
Helena Beltran
,
Alexander Bernier
,
Moroni Berrios
,
Isaac Bevers
,
Alden Blatter
,
Rahul Brito
,
Amy Brown
,
Johnathan Brown
,
Léo Cadillac
,
Selina Casalino
,
John Costello
,
Abhijeet Dalal
,
Iris De Santiago
,
Enrique Diaz-Ocampo
,
Amanda Doherty-Kirby
,
Mohamed Ebraheem
,
Ellie Eiseman
,
Mahmoud Elmahdy
,
Renee English
,
Emily Evangelista
,
Kenneth Fletcher
,
Hortense Gallois
,
Gaelyn Garrett
,
Alexander Gelbard
,
Omar Ghaffar
,
Anna Goldenberg
,
Karim Hanna
,
William Hersh
,
Jennifer Jain
,
Lochana Jayachandran
,
Kaley Jenney
,
Kathy Jenkins
,
Stacy Jo
,
Alistair Johnson
,
Ayush Kalia
,
Megha Kalia
,
Zoha Khawa
,
Kenji Kobayashi
,
Cindy Kostelnik
,
Alisa Krause
,
Andrea Krussel
,
Elisa Lapadula
,
Genelle Leo
,
Justin Levinsky
,
Chloe Loewith
,
Radhika Mahajan
,
Vrishni Maharaj
,
Siyu Miao
,
LeAnn Michaels
,
Matthew Mifsud
,
Marian Mikhael
,
Elijah Moothedan
,
Yosef Nafii
,
Tempestt Neal
,
Karlee Newberry
,
Evan Ng
,
Christopher Nickel
,
Amanda Peltier
,
Trevor Pharr
,
Michaela Pnacekova
,
Matthew Pontell
,
Jaiden Potter
,
Claire Premi-Bortolotto
,
Parnaz Rafatjou
,
JM Rahman
,
Gayathiri Rajkumar
,
John Ramos
,
Michael de Riesthal
,
Sarah Rohde
,
Jillian Rossi
,
Laurie Russell
,
Samantha Salvi Cruz
,
Joyce Samuel
,
Suketu Shah
,
Ahmed Shawkat
,
Elizabeth Silberholz
,
John Stark
,
Lala Su
,
Shrramana Ganesh Sudhakar
,
Duncan Sutherland
,
Venkata Swarna Mukhi
,
Jeffrey Tang
,
Luka Taylor
,
Jamie Toghranegar
,
Julie Tu
,
Megan Urbano
,
Gavin Victor
,
Kimberly Vinson
,
Jordan Wilke
,
Claire Wilson
,
Madeleine Zanin
,
Xijie Zeng
,
Theresa Zesiewicz
,
Robin Zhao
,
Pantelis Zisimopoulos
,
Satrajit Ghosh
Published: May 1, 2026. Version:
1.1.0
Raw Audio Data Access for Bridge2AI Voice Pediatric Cohort is via Synapse
(March 9, 2026, 10:07 a.m.)
The published Bridge2AI-Voice Pediatric Dataset contains derived features from the audio waveforms. This PhysioNet project does not contain raw audios.
Accessing raw audio is a more involved process and requires institutional sign off. Please reach out to the access committee if you are interested in access: DACO@b2ai-voice.org
Data will be made available via Synapse
:
https://www.synapse.org/Synapse:syn73617068
For questions regarding the dataset itself, please contact the corresponding author, listed on the sidebar.
Note that the Bridge2AI-Voice Adult Dataset is also available on PhysioNet:
https://physionet.org/content/b2ai-voice/
When using this resource, please cite:
Cite
Copy BibTeX
Bensoussan, Y., Sigaras, A., Rameau, A., Elemento, O., Powell, M., Dorr, D., Payne, P., Ravitsky, V., Bélisle-Pipon, J., Bahr, R., Watts, S., Bolser, D., Siu, J., Lerner-Ellis, J., Rudzicz, F., Boyer, M., Abdel-Aty, Y., Ahmed Syed, T., Amraei, D., ... Ghosh, S. (2026). Bridge2AI-Voice Pediatric Dataset (version 1.1.0).
PhysioNet
. RRID:SCR_007345.
https://doi.org/10.13026/h995-bt35
@article{PhysioNet-b2ai-voice-pediatric-1.1.0,
author = {Bensoussan, Yael and Sigaras, Alexandros and Rameau, Anais and Elemento, Olivier and Powell, Maria and Dorr, David and Payne, Philip and Ravitsky, Vardit and Bélisle-Pipon, Jean-Christophe and Bahr, Ruth and Watts, Stephanie and Bolser, Donald and Siu, Jennifer and Lerner-Ellis, Jordan and Rudzicz, Frank and Boyer, Micah and Abdel-Aty, Yassmeen and {Ahmed Syed}, Toufeeq and Amraei, Dona and Anibal, James and Aradi, Stephen and Armosh, Kirollos and Martinez, Ana Sophia and Awan, Shaheen and Bedrick, Steven and Beltran, Helena and Bernier, Alexander and Berrios, Moroni and Bevers, Isaac and Blatter, Alden and Brito, Rahul and Brown, Amy and Brown, Johnathan and Cadillac, Léo and Casalino, Selina and Costello, John and Dalal, Abhijeet and {De Santiago}, Iris and Diaz-Ocampo, Enrique and Doherty-Kirby, Amanda and Ebraheem, Mohamed and Eiseman, Ellie and Elmahdy, Mahmoud and English, Renee and Evangelista, Emily and Fletcher, Kenneth and Gallois, Hortense and Garrett, Gaelyn and Gelbard, Alexander and Ghaffar, Omar and Goldenberg, Anna and Hanna, Karim and Hersh, William and Jain, Jennifer and Jayachandran, Lochana and Jenney, Kaley and Jenkins, Kathy and Jo, Stacy and Johnson, Alistair and Kalia, Ayush and Kalia, Megha and Khawa, Zoha and Kobayashi, Kenji and Kostelnik, Cindy and Krause, Alisa and Krussel, Andrea and Lapadula, Elisa and Leo, Genelle and Levinsky, Justin and Loewith, Chloe and Mahajan, Radhika and Maharaj, Vrishni and Miao, Siyu and Michaels, LeAnn and Mifsud, Matthew and Mikhael, Marian and Moothedan, Elijah and Nafii, Yosef and Neal, Tempestt and Newberry, Karlee and Ng, Evan and Nickel, Christopher and Peltier, Amanda and Pharr, Trevor and Pnacekova, Michaela and Pontell, Matthew and Potter, Jaiden and Premi-Bortolotto, Claire and Rafatjou, Parnaz and Rahman, JM and Rajkumar, Gayathiri and Ramos, John and {de Riesthal}, Michael and Rohde, Sarah and Rossi, Jillian and Russell, Laurie and {Salvi Cruz}, Samantha and Samuel, Joyce and Shah, Suketu and Shawkat, Ahmed and Silberholz, Elizabeth and Stark, John and Su, Lala and Sudhakar, Shrramana Ganesh and Sutherland, Duncan and {Swarna Mukhi}, Venkata and Tang, Jeffrey and Taylor, Luka and Toghranegar, Jamie and Tu, Julie and Urbano, Megan and Victor, Gavin and Vinson, Kimberly and Wilke, Jordan and Wilson, Claire and Zanin, Madeleine and Zeng, Xijie and Zesiewicz, Theresa and Zhao, Robin and Zisimopoulos, Pantelis and Ghosh, Satrajit},
title = {{Bridge2AI-Voice Pediatric Dataset}},
journal = {{PhysioNet}},
year = {2026},
month = may,
note = {Version 1.1.0},
doi = {10.13026/h995-bt35},
url = {https://doi.org/10.13026/h995-bt35}
}
Cite
×
MLA
Bensoussan, Yael, et al. "Bridge2AI-Voice Pediatric Dataset" (version 1.1.0).
PhysioNet
(2026). RRID:SCR_007345.
https://doi.org/10.13026/h995-bt35
APA
Bensoussan, Y., Sigaras, A., Rameau, A., Elemento, O., Powell, M., Dorr, D., Payne, P., Ravitsky, V., Bélisle-Pipon, J., Bahr, R., Watts, S., Bolser, D., Siu, J., Lerner-Ellis, J., Rudzicz, F., Boyer, M., Abdel-Aty, Y., Ahmed Syed, T., Amraei, D., ... Ghosh, S. (2026). Bridge2AI-Voice Pediatric Dataset (version 1.1.0).
PhysioNet
. RRID:SCR_007345.
https://doi.org/10.13026/h995-bt35
Chicago
Bensoussan, Yael, Sigaras, Alexandros, Rameau, Anais, Elemento, Olivier, Powell, Maria, Dorr, David, Payne, Philip, Ravitsky, Vardit, Bélisle-Pipon, Jean-Christophe, Bahr, Ruth, Watts, Stephanie, Bolser, Donald, Siu, Jennifer, Lerner-Ellis, Jordan, Rudzicz, Frank, Boyer, Micah, Abdel-Aty, Yassmeen, Ahmed Syed, Toufeeq, Amraei, Dona, Anibal, James, Aradi, Stephen, Armosh, Kirollos, Martinez, Ana Sophia, Awan, Shaheen, Bedrick, Steven, Beltran, Helena, Bernier, Alexander, Berrios, Moroni, Bevers, Isaac, Blatter, Alden, Brito, Rahul, Brown, Amy, Brown, Johnathan, Cadillac, Léo, Casalino, Selina, Costello, John, Dalal, Abhijeet, De Santiago, Iris, Diaz-Ocampo, Enrique, Doherty-Kirby, Amanda, Ebraheem, Mohamed, Eiseman, Ellie, Elmahdy, Mahmoud, English, Renee, Evangelista, Emily, Fletcher, Kenneth, Gallois, Hortense, Garrett, Gaelyn, Gelbard, Alexander, Ghaffar, Omar, Goldenberg, Anna, Hanna, Karim, Hersh, William, Jain, Jennifer, Jayachandran, Lochana, Jenney, Kaley, Jenkins, Kathy, Jo, Stacy, Johnson, Alistair, Kalia, Ayush, Kalia, Megha, Khawa, Zoha, Kobayashi, Kenji, Kostelnik, Cindy, Krause, Alisa, Krussel, Andrea, Lapadula, Elisa, Leo, Genelle, Levinsky, Justin, Loewith, Chloe, Mahajan, Radhika, Maharaj, Vrishni, Miao, Siyu, Michaels, LeAnn, Mifsud, Matthew, Mikhael, Marian, Moothedan, Elijah, Nafii, Yosef, Neal, Tempestt, Newberry, Karlee, Ng, Evan, Nickel, Christopher, Peltier, Amanda, Pharr, Trevor, Pnacekova, Michaela, Pontell, Matthew, Potter, Jaiden, Premi-Bortolotto, Claire, Rafatjou, Parnaz, Rahman, JM, Rajkumar, Gayathiri, Ramos, John, de Riesthal, Michael, Rohde, Sarah, Rossi, Jillian, Russell, Laurie, Salvi Cruz, Samantha, Samuel, Joyce, Shah, Suketu, Shawkat, Ahmed, Silberholz, Elizabeth, Stark, John, Su, Lala, Sudhakar, Shrramana Ganesh, Sutherland, Duncan, Swarna Mukhi, Venkata, Tang, Jeffrey, Taylor, Luka, Toghranegar, Jamie, Tu, Julie, Urbano, Megan, Victor, Gavin, Vinson, Kimberly, Wilke, Jordan, Wilson, Claire, Zanin, Madeleine, Zeng, Xijie, Zesiewicz, Theresa, Zhao, Robin, Zisimopoulos, Pantelis, and Satrajit Ghosh. "Bridge2AI-Voice Pediatric Dataset" (version 1.1.0).
PhysioNet
(2026). RRID:SCR_007345.
https://doi.org/10.13026/h995-bt35
Harvard
Bensoussan, Y., Sigaras, A., Rameau, A., Elemento, O., Powell, M., Dorr, D., Payne, P., Ravitsky, V., Bélisle-Pipon, J., Bahr, R., Watts, S., Bolser, D., Siu, J., Lerner-Ellis, J., Rudzicz, F., Boyer, M., Abdel-Aty, Y., Ahmed Syed, T., Amraei, D., Anibal, J., Aradi, S., Armosh, K., Martinez, A. S., Awan, S., Bedrick, S., Beltran, H., Bernier, A., Berrios, M., Bevers, I., Blatter, A., Brito, R., Brown, A., Brown, J., Cadillac, L., Casalino, S., Costello, J., Dalal, A., De Santiago, I., Diaz-Ocampo, E., Doherty-Kirby, A., Ebraheem, M., Eiseman, E., Elmahdy, M., English, R., Evangelista, E., Fletcher, K., Gallois, H., Garrett, G., Gelbard, A., Ghaffar, O., Goldenberg, A., Hanna, K., Hersh, W., Jain, J., Jayachandran, L., Jenney, K., Jenkins, K., Jo, S., Johnson, A., Kalia, A., Kalia, M., Khawa, Z., Kobayashi, K., Kostelnik, C., Krause, A., Krussel, A., Lapadula, E., Leo, G., Levinsky, J., Loewith, C., Mahajan, R., Maharaj, V., Miao, S., Michaels, L., Mifsud, M., Mikhael, M., Moothedan, E., Nafii, Y., Neal, T., Newberry, K., Ng, E., Nickel, C., Peltier, A., Pharr, T., Pnacekova, M., Pontell, M., Potter, J., Premi-Bortolotto, C., Rafatjou, P., Rahman, J., Rajkumar, G., Ramos, J., de Riesthal, M., Rohde, S., Rossi, J., Russell, L., Salvi Cruz, S., Samuel, J., Shah, S., Shawkat, A., Silberholz, E., Stark, J., Su, L., Sudhakar, S. G., Sutherland, D., Swarna Mukhi, V., Tang, J., Taylor, L., Toghranegar, J., Tu, J., Urbano, M., Victor, G., Vinson, K., Wilke, J., Wilson, C., Zanin, M., Zeng, X., Zesiewicz, T., Zhao, R., Zisimopoulos, P., and Ghosh, S. (2026) 'Bridge2AI-Voice Pediatric Dataset' (version 1.1.0),
PhysioNet
. RRID:SCR_007345. Available at:
https://doi.org/10.13026/h995-bt35
Vancouver
Bensoussan Y, Sigaras A, Rameau A, Elemento O, Powell M, Dorr D, Payne P, Ravitsky V, Bélisle-Pipon J, Bahr R, Watts S, Bolser D, Siu J, Lerner-Ellis J, Rudzicz F, Boyer M, Abdel-Aty Y, Ahmed Syed T, Amraei D, Anibal J, Aradi S, Armosh K, Martinez A S, Awan S, Bedrick S, Beltran H, Bernier A, Berrios M, Bevers I, Blatter A, Brito R, Brown A, Brown J, Cadillac L, Casalino S, Costello J, Dalal A, De Santiago I, Diaz-Ocampo E, Doherty-Kirby A, Ebraheem M, Eiseman E, Elmahdy M, English R, Evangelista E, Fletcher K, Gallois H, Garrett G, Gelbard A, Ghaffar O, Goldenberg A, Hanna K, Hersh W, Jain J, Jayachandran L, Jenney K, Jenkins K, Jo S, Johnson A, Kalia A, Kalia M, Khawa Z, Kobayashi K, Kostelnik C, Krause A, Krussel A, Lapadula E, Leo G, Levinsky J, Loewith C, Mahajan R, Maharaj V, Miao S, Michaels L, Mifsud M, Mikhael M, Moothedan E, Nafii Y, Neal T, Newberry K, Ng E, Nickel C, Peltier A, Pharr T, Pnacekova M, Pontell M, Potter J, Premi-Bortolotto C, Rafatjou P, Rahman J, Rajkumar G, Ramos J, de Riesthal M, Rohde S, Rossi J, Russell L, Salvi Cruz S, Samuel J, Shah S, Shawkat A, Silberholz E, Stark J, Su L, Sudhakar S G, Sutherland D, Swarna Mukhi V, Tang J, Taylor L, Toghranegar J, Tu J, Urbano M, Victor G, Vinson K, Wilke J, Wilson C, Zanin M, Zeng X, Zesiewicz T, Zhao R, Zisimopoulos P, Ghosh S. Bridge2AI-Voice Pediatric Dataset (version 1.1.0). PhysioNet. 2026. RRID:SCR_007345. Available from:
https://doi.org/10.13026/h995-bt35
BibTeX
Copy
@article{PhysioNet-b2ai-voice-pediatric-1.1.0,
author = {Bensoussan, Yael and Sigaras, Alexandros and Rameau, Anais and Elemento, Olivier and Powell, Maria and Dorr, David and Payne, Philip and Ravitsky, Vardit and Bélisle-Pipon, Jean-Christophe and Bahr, Ruth and Watts, Stephanie and Bolser, Donald and Siu, Jennifer and Lerner-Ellis, Jordan and Rudzicz, Frank and Boyer, Micah and Abdel-Aty, Yassmeen and {Ahmed Syed}, Toufeeq and Amraei, Dona and Anibal, James and Aradi, Stephen and Armosh, Kirollos and Martinez, Ana Sophia and Awan, Shaheen and Bedrick, Steven and Beltran, Helena and Bernier, Alexander and Berrios, Moroni and Bevers, Isaac and Blatter, Alden and Brito, Rahul and Brown, Amy and Brown, Johnathan and Cadillac, Léo and Casalino, Selina and Costello, John and Dalal, Abhijeet and {De Santiago}, Iris and Diaz-Ocampo, Enrique and Doherty-Kirby, Amanda and Ebraheem, Mohamed and Eiseman, Ellie and Elmahdy, Mahmoud and English, Renee and Evangelista, Emily and Fletcher, Kenneth and Gallois, Hortense and Garrett, Gaelyn and Gelbard, Alexander and Ghaffar, Omar and Goldenberg, Anna and Hanna, Karim and Hersh, William and Jain, Jennifer and Jayachandran, Lochana and Jenney, Kaley and Jenkins, Kathy and Jo, Stacy and Johnson, Alistair and Kalia, Ayush and Kalia, Megha and Khawa, Zoha and Kobayashi, Kenji and Kostelnik, Cindy and Krause, Alisa and Krussel, Andrea and Lapadula, Elisa and Leo, Genelle and Levinsky, Justin and Loewith, Chloe and Mahajan, Radhika and Maharaj, Vrishni and Miao, Siyu and Michaels, LeAnn and Mifsud, Matthew and Mikhael, Marian and Moothedan, Elijah and Nafii, Yosef and Neal, Tempestt and Newberry, Karlee and Ng, Evan and Nickel, Christopher and Peltier, Amanda and Pharr, Trevor and Pnacekova, Michaela and Pontell, Matthew and Potter, Jaiden and Premi-Bortolotto, Claire and Rafatjou, Parnaz and Rahman, JM and Rajkumar, Gayathiri and Ramos, John and {de Riesthal}, Michael and Rohde, Sarah and Rossi, Jillian and Russell, Laurie and {Salvi Cruz}, Samantha and Samuel, Joyce and Shah, Suketu and Shawkat, Ahmed and Silberholz, Elizabeth and Stark, John and Su, Lala and Sudhakar, Shrramana Ganesh and Sutherland, Duncan and {Swarna Mukhi}, Venkata and Tang, Jeffrey and Taylor, Luka and Toghranegar, Jamie and Tu, Julie and Urbano, Megan and Victor, Gavin and Vinson, Kimberly and Wilke, Jordan and Wilson, Claire and Zanin, Madeleine and Zeng, Xijie and Zesiewicz, Theresa and Zhao, Robin and Zisimopoulos, Pantelis and Ghosh, Satrajit},
title = {{Bridge2AI-Voice Pediatric Dataset}},
journal = {{PhysioNet}},
year = {2026},
month = may,
note = {Version 1.1.0},
doi = {10.13026/h995-bt35},
url = {https://doi.org/10.13026/h995-bt35}
}
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Pollard, T., Moody, B. E., Lehman, L., Gow, B., Fernandes, C., Xie, C., Johnson, A., Mark, R. G., & Heldt, T. (2026). PhysioNet as a global platform for biomedical research. Nature Health. https://doi.org/10.1038/s44360-026-00096-z. Available from: https://rdcu.be/faatM
Cite
×
APA
Pollard, T., Moody, B. E., Lehman, L., Gow, B., Fernandes, C., Xie, C., Johnson, A., Mark, R. G., & Heldt, T. (2026). PhysioNet as a global platform for biomedical research. Nature Health. https://doi.org/10.1038/s44360-026-00096-z. Available from: https://rdcu.be/faatM
MLA
Pollard, Tom, et al. “PhysioNet as a Global Platform for Biomedical Research.” Nature Health, 2026, https://doi.org/10.1038/s44360-026-00096-z. Available from: https://rdcu.be/faatM
CHICAGO
Pollard, Tom, Benjamin E. Moody, Li-wei Lehman, Brian Gow, Chrystinne Fernandes, Chen Xie, Alistair Johnson, Roger G. Mark, and Thomas Heldt. “PhysioNet as a Global Platform for Biomedical Research.” Nature Health (2026). https://doi.org/10.1038/s44360-026-00096-z.i Available from: https://rdcu.be/faatM
HARVARD
Pollard, T., Moody, B.E., Lehman, L., Gow, B., Fernandes, C., Xie, C., Johnson, A., Mark, R.G. and Heldt, T., 2026. PhysioNet as a global platform for biomedical research. Nature Health. Available at: https://doi.org/10.1038/s44360-026-00096-z. Available from: https://rdcu.be/faatM
VANCOUVER
Pollard T, Moody BE, Lehman L, Gow B, Fernandes C, Xie C, et al. PhysioNet as a global platform for biomedical research. Nature Health. 2026. doi:10.1038/s44360-026-00096-z. Available from: https://rdcu.be/faatM
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Abstract
The human voice contains complex acoustic markers which have been linked to important health conditions including dementia, mood disorders, and cancer. When viewed as a biomarker, voice is a promising characteristic to measure as it is simple to collect, cost-effective, and has broad clinical utility. Recent advances in artificial intelligence have provided techniques to extract previously unknown prognostically useful information from dense data elements such as images. The Bridge2AI-Voice project seeks to create an ethically sourced flagship dataset to enable future research in artificial intelligence and support critical insights into the use of voice as a biomarker of health. Here we present Bridge2AI-Voice, a comprehensive collection of data derived from voice recordings with corresponding clinical information.
Bridge2AI-Voice Pediatric Dataset v1.1.0 contains derived audio features for 23,533 recordings collected from 300 participants aged 2-18. The release contains data considered low risk, including derivations such as spectrograms but not the original voice recordings. Detailed demographic, clinical, and validated questionnaire data are also made available.
Background
Understanding voice and speech development in children is essential for identifying communication or speech disorders early in life, and for supporting timely intervention [1]. Pediatric and adult voice/speech production are fundamentally different because the respiratory system and larynx undergo rapid functional maturation/development throughout childhood [2, 3]. These developmental changes can influence acoustic features such as fundamental frequency (F₀) [2]. As a result, evidence/normative data that we have in adults cannot be generalized to pediatric populations.
Despite the clinical importance of detecting pediatric communication disorders such as autism spectrum disorder and speech delays, the availability of large-scale pediatric databases/datasets remains/is limited. Data collection in pediatric populations introduces/poses unique challenges such as privacy issues, consent processes, the need for developmentally appropriate tasks. These factors have contributed to the lack of publicly/open access/ available pediatric data sets that enable machine learning for pediatric voice analysis [3].
Establishing a robust multi-institutional dataset that integrates pediatric voice data with demographic information would advance the understanding of voice and disease as well as early detection and intervention. Resources such as this project are intended to enable study of developmental norms, create AI-driven tools for early screening, and support clinical insight.
Methods
Patients/healthy volunteers at the Hospital for Sick Children were considered for enrollment in the study. Patients were considered eligible for the study if they fulfilled the inclusion criteria of 2 to 18 years of age, and English proficiency. Exclusion criteria included participants over 18 years of age, and individuals who were non-verbal. Non-patients, recruited through research postings, were evaluated for eligibility based on the study’s inclusion and exclusion criteria. Following confirmation of eligibility, parental or participant consent was obtained prior to data collection and data sharing. Once consented, patients were assigned a unique study identification number and a standardized age-appropriate protocol for data collection was adopted. The protocol included the collection of demographic information, voice and speech related questionnaires, and questionnaires inquiring about medical history.
All data was collected through customized software –
reproschema-ui –
on tablets. A headset was used to record for most participants, while the remaining recordings utilized the built-in tablet microphone due to low tolerance of wearing headphones or existing complex medical conditions. All participants completed the recording and demographic data collection in one session. For participants without adequate comprehension and familiarity with their past medical history, parents or decision-makers completed the survey during the recording on a separate tablet. The simultaneous completion of the recording and survey improved efficiency and minimized participant burden and fatigue. Data were exported and converted to tab delimited values using an open source library developed by our team [4].
Data Description
The dataset contains both derived audio data features (under features) and phenotypic information acquired during data collection (under phenotype), as well as metadata information for the recordings (available under metadata). Binary files are made available as Parquet, an open-source column-oriented data file format. Each of the parquet files is formatted similarly. Each element of the parquet formatted dataset contains a unique identifier for the participant (participant_id), a unique identifier for the recording session (session_id), the task performed (task_name), the number of time frames associated with that feature (n_frames), and the tensor data for the feature.
torchaudio_spectrograms.parquet (n=23533) contains spectrograms of dimension 201xT generated using the short-time Fast Fourier Transform (FFT) with a 25ms window size, 10ms hop length, and a 400-point FFT.
torchaudio_mel_spectrograms.parquet (n=23533) contains Mel spectrograms of dimension 60xT generated with a 25ms window size, 10ms hop length, a 400-point FFT and 60 Mel bins.
torchaudio_mfcc.parquet (n=23533) contains Mel-frequency cepstrum coefficients of dimension 60xT using the same parameters as the mel spectrograms.
torchaudio_pitch.parquet (n=23533) contains the detected pitch (fundamental frequency) over time and is of dimension T with a min and max pitch of 80 and 500 respectively.
sparc_ema.parquet (n=23532) contains the estimated electromagnetic articulography (EMA) using a deep learning model with dimensions Tx12 where the 12 correspond to X/Y positions of six articulators: tongue dorsum (TD), tongue body (TB), tongue tip (TT), lower incisor (LI), upper lip (UL), lower lip (LL), respectively.
sparc_loudness.parquet (n=23532) contains the estimated loudness based on the average absolute amplitude of the audio waveform of size T, using 20ms windows.
sparc_periodicity.parquet (n=23532) contains the estimated periodicity (confidence of pitch presence) derived from the audio using 20ms windows of dimension T.
sparc_pitch.parquet (n=23532) contains the estimated fundamental frequency (F0) of the audio signal using a different algorithm than before with a range of 50-550Hz and dimension T.
ppgs.parquet (n=23533) contains the phonetic posteriorgram probabilities across 40 phoneme categories giving a dimension of 40xT with a frame rate of 100Hz.
Spectrograms, Mel Spectrograms, MFC coefficients, PPGs, and EMAs for sensitive records and audio checks have been removed from v1.1. Additionally, some files, whether due to length or other issues, could not generate certain features and so are not included in the bundled data.
In addition to the parquet files, the features folder contains the following plain-text file, features derived from the open-source Speech and Music Interpretation by Large-space Extraction (openSMILE [5]), Praat [6], parselmouth [7], and torchaudio [8, 9] are provided. Each feature is present in the static_features.tsv file. There is also metrics related to audio quality derived from the recordings present in the audio_quality_metrics.tsv file.
All of the above files are associated with a data dictionary file which has the same file stem and a JSON suffixes (e.g. torch_spectrogram.json). The above data dictionaries have the same overall structure: a dictionary where keys are the column names matching the associated data file, and values are dictionaries with further detail. The description value in the data dictionary provides a one sentence summary of the respective column.
The code used to preprocess the raw audio waveforms into the parquet file and to merge the source data into the phenotype files has been made open source in the
b2aiprep library
[4].
Usage Notes
If using Python, the parquet dataset can be loaded in with the HuggingFace datasets library as follows:
from datasets import Dataset
ds = Dataset.from_parquet("torchaudio_spectrogram.parquet")
A spectrogram can be plotted in decibels by converting it from its original power representation:
from datasets import Dataset
import pandas as pd
import matplotlib.pyplot as plt
import librosa
import numpy as np
ds = Dataset.from_parquet("torchaudio_mel_spectrogram.parquet")
spectrogram = librosa.power_to_db(np.asarray(ds[0]['mel_spectrogram']))
plt.figure(figsize=(10, 4))
plt.imshow(spectrogram, aspect='auto', origin='lower')
plt.title('Spectrogram')
plt.xlabel('Time Step')
plt.ylabel('Frequency')
plt.colorbar()
plt.show()
A phenotype file can be loaded with any statistical analysis tool. For example, the pandas library in Python can read the data:
import pandas as pd
df = pd.read_csv("demographics.tsv", sep="\t", header=0)
Release Notes
b2ai-voice-pediatric v1.1: No new participants released in this minor update, but releasing audio features for all free speech tasks that were manually checked for presence of unconsented speakers and PII. Additionally, releases new metrics related to the audio quality of the recordings and per recording metadata information.
b2ai-voice-pediatric v1.0: This was the first release of the Bridge2AI-Voice Pediatric dataset.
Ethics
Data collection and sharing was approved by the Research Ethics Board at the Hospital for Sick Children.
Acknowledgements
This release would not be possible without the graceful contribution of data from all the participants of the study.
This project was funded by NIH project number 3OT2OD032720-01S1: Bridge2AI: Voice as a Biomarker of Health - Building an ethically sourced, bioaccoustic database to understand disease like never before. We would also like to thank the NIH for their continued support of the project.
Conflicts of Interest
None to declare.
References
Kelchner, L. N., Brehm, S. B., de Alarcon, A., & Weinrich, B. (2012). Update on pediatric voice and airway disorders: assessment and care. Current opinion in otolaryngology & head and neck surgery, 20(3), 160–164.
https://doi.org/10.1097/MOO.0b013e3283530ecb
Tavares, E. L., Labio, R. B., & Martins, R. H. (2010). Normative study of vocal acoustic parameters from children from 4 to 12 years of age without vocal symptoms: a pilot study. Brazilian journal of otorhinolaryngology, 76(4), 485–490.
https://doi.org/10.1590/S1808-86942010000400013
Fujiki RB, Venkatraman A, Heller Murray ES. The Pediatric Vocal Mechanism: Structure and Function. J Voice. 2025 Apr 4:S0892-1997(25)00118-3. doi: 10.1016/j.jvoice.2025.03.025. Epub ahead of print. PMID: 40187973; PMCID: PMC12353639.
Johnson, A., Bevers, I., Ng, E., Wilke, J., Brito, R., Bedrick, S., Catania, F. & Ghosh, S. (2025). Bridge2AI Data Processing Library (Version 3.0.0) [Computer software].
https://github.com/sensein/b2aiprep
Florian Eyben, Martin Wöllmer, Björn Schuller: "openSMILE - The Munich Versatile and Fast Open-Source Audio Feature Extractor", Proc. ACM Multimedia (MM), ACM, Florence, Italy, ISBN 978-1-60558-933-6, pp. 1459-1462, 25.-29.10.2010.
Boersma P, Van Heuven V. Speak and unSpeak with PRAAT. Glot International. 2001 Nov;5(9/10):341-7.
Jadoul Y, Thompson B, De Boer B. Introducing parselmouth: A python interface to praat. Journal of Phonetics. 2018 Nov 1;71:1-5.
Hwang, J., Hira, M., Chen, C., Zhang, X., Ni, Z., Sun, G., Ma, P., Huang, R., Pratap, V., Zhang, Y., Kumar, A., Yu, C.-Y., Zhu, C., Liu, C., Kahn, J., Ravanelli, M., Sun, P., Watanabe, S., Shi, Y., Tao, T., Scheibler, R., Cornell, S., Kim, S., & Petridis, S. (2023). TorchAudio 2.1: Advancing speech recognition, self-supervised learning, and audio processing components for PyTorch. arXiv preprint arXiv:2310.17864
Yang, Y.-Y., Hira, M., Ni, Z., Chourdia, A., Astafurov, A., Chen, C., Yeh, C.-F., Puhrsch, C., Pollack, D., Genzel, D., Greenberg, D., Yang, E. Z., Lian, J., Mahadeokar, J., Hwang, J., Chen, J., Goldsborough, P., Roy, P., Narenthiran, S., Watanabe, S., Chintala, S., Quenneville-Bélair, V, & Shi, Y. (2021). TorchAudio: Building Blocks for Audio and Speech Processing. arXiv preprint arXiv:2110.15018.
Contents
Abstract
Background
Methods
Data Description
Usage Notes
Release Notes
Ethics
Acknowledgements
Conflicts of Interest
References
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Only credentialed users who sign the DUA can access the files.
License (for files):
Bridge2AI Voice Registered Access License
Data Use Agreement:
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No training required
Discovery
DOI (version 1.1.0):
https://doi.org/10.13026/h995-bt35
DOI (latest version):
https://doi.org/10.13026/mf9s-5r03
Topics:
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================================================================================

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SOURCE METADATA
Project: VOICE
Source ID: documentation_repository
Source type: documentation
Source URL: https://github.com/eipm/bridge2ai-docs/tree/main/docs
Raw file: data/raw/VOICE/github_eipm_bridge2ai-docs_README_row22.md
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<p align="center">
    <img src="images/main_logo_black.svg#gh-light-mode-only" width="200" alt="B2AI Voice Logo">
    <img src="images/main_logo_white.svg#gh-dark-mode-only" width="200" alt="B2AI Voice Logo"><br>
    Voice as a Biomarker of Health
</p>

# bridge2ai-docs

Docs for the Bridge2AI Voice Project.

[![GitHub](https://img.shields.io/badge/github-3.0.1-green?style=flat&logo=github)](https://github.com/eipm/bridge2ai-docs) [![Python 3.12.0](https://img.shields.io/badge/python-3.12.0-blue.svg)](https://www.python.org/downloads/release/python-3120/) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)  [![DOI](https://zenodo.org/badge/860006845.svg)](https://zenodo.org/doi/10.5281/zenodo.13834653)


## 🤝 License
See [LICENSE](./LICENSE)

## 📚 How to Cite
> Sigaras, A., Zisimopoulos, P., Tang, J., Bevers, I., Gallois, H., Bernier, A., Bensoussan, Y., Ghosh, S. S., Rameau, A., Powell, M. E., Belisle-Pipon, J.-C., Ravitsky, V., Johnson, A., Elemento, O., Dorr, D., … Bridge2AI-Voice. (2024). eipm/bridge2ai-docs. Zenodo. [https://zenodo.org/doi/10.5281/zenodo.13834653](https://zenodo.org/doi/10.5281/zenodo.13834653)

## Prerequisites

```bash
pip install -r requirements.txt
```

## How to run the app

```bash
sh startup.sh
```


================================================================================
CRATE EVIDENCE
================================================================================

================================================================================
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ROLE: crate JSON-LD with file inventories collapsed; the substantive evidence (rai:* fields, ethics, access, provenance)
SIZE: 309,146 characters
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   "name": "B2AI Voice: An ethically-sourced, diverse voice dataset linked to health information",
   "description": "The human voice contains complex acoustic markers which have been linked to important health conditions including dementia, mood disorders, and cancer. When viewed as a biomarker, voice is a promising characteristic to measure as it is simple to collect, cost-effective, and has broad clinical utility. Recent advances in artificial intelligence have provided techniques to extract previously unknown prognostically useful information from dense data elements such as images. The Bridge2AI-Voice project seeks to create an ethically sourced flagship dataset to enable future research in artificial intelligence and support critical insights into the use of voice as a biomarker of health. Here we present Bridge2AI-Voice, a comprehensive collection of data derived from voice recordings with corresponding clinical information.\n\nBridge2AI-Voice v3.0 contains data for 833 participants across five sites in North America. Participants were selected based on known conditions which manifest within the voice waveform including voice disorders, neurological disorders, mood disorders, and respiratory disorders. The release contains data considered low risk, including derivations such as spectrograms but not the original voice recordings. Detailed demographic, clinical, and validated questionnaire data are also made available.\n\t",
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    "Voice as a biomarker",
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    "Human voice",
    "Vocal health",
    "Spectrogram",
    "Mel spectrogram",
    "MFCC (Mel-frequency cepstral coefficients)",
    "Fundamental frequency (F0)",
    "Phonetic posteriorgrams (PPGs)",
    "Articulatory features",
    "Acoustic features",
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   "version": "3.0.0",
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   "author": [
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    "David Dorr",
    "Philip Payne",
    "Vardit Ravitsky",
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    "Ruth Bahr",
    "Stephanie Watts",
    "Donald Bolser",
    "Jennifer Siu",
    "Jordan Lerner-Ellis",
    "Frank Rudzicz",
    "Micah Boyer",
    "Yassmeen Abdel-Aty",
    "Toufeeq Ahmed Syed",
    "James Anibal",
    "Dona Amraei",
    "Stephen Aradi",
    "Kirollos Armosh",
    "Ana Sophia Martinez",
    "Shaheen Awan",
    "Steven Bedrick",
    "Helena Beltran",
    "Alexander Bernier",
    "Moroni Berrios",
    "Isaac Bevers",
    "Alden Blatter",
    "Rahul Brito",
    "Amy Brown",
    "Johnathan Brown",
    "Léo Cadillac",
    "Selina Casalino",
    "John Costello",
    "Abhijeet Dalal",
    "Iris De Santiago",
    "Enrique Diaz-Ocampo",
    "Amanda Doherty-Kirby",
    "Mohamed Ebraheem",
    "Ellie Eiseman",
    "Mahmoud Elmahdy",
    "Renee English",
    "Emily Evangelista",
    "Kenneth Fletcher",
    "Hortense Gallois",
    "Gaelyn Garrett",
    "Alexander Gelbard",
    "Anna Goldenberg",
    "Karim Hanna",
    "William Hersh",
    "Jennifer Jain",
    "Lochana Jayachandran",
    "Kaley Jenney",
    "Kathy Jenkins",
    "Stacy Jo",
    "Alistair Johnson",
    "Ayush Kalia",
    "Megha Kalia",
    "Zoha Khawa",
    "Cindy Kostelnik",
    "Alisa Krause",
    "Andrea Krussel",
    "Elisa Lapadula",
    "Genelle Leo",
    "Justin Levinsky",
    "Chloe Loewith",
    "Radhika Mahajan",
    "Vrishni Maharaj",
    "Siyu Miao",
    "LeAnn Michaels",
    "Matthew Mifsud",
    "Marian Mikhael",
    "Elijah Moothedan",
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    "Karlee Newberry",
    "Evan Ng",
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    "Michaela Pnacekova",
    "Matthew Pontell",
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    "Parnaz Rafatjou",
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    "Jillian Rossi",
    "Laurie Russell",
    "Samantha Salvi Cruz",
    "Joyce Samuel",
    "Suketu Shah",
    "Ahmed Shawkat",
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    "John Stark",
    "Lala Su",
    "Shrramana Ganesh Sudhakar",
    "Duncan Sutherland",
    "Venkata Swarna Mukhi",
    "Jeffrey Tang",
    "Luka Taylor",
    "Jamie Toghranegar",
    "Julie Tu",
    "Megan Urbano",
    "Gavin Victor",
    "Kimberly Vinson",
    "Jordan Wilke",
    "Claire Wilson",
    "Madeleine Zanin",
    "Xijie Zeng",
    "Theresa Zesiewicz",
    "Robin Zhao",
    "Pantelis Zisimopoulos",
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   ],
   "publisher": "PhysioNet",
   "principalInvestigator": "Yael Bensoussan",
   "funder": "Funded by the NIH Common Fund. Award #3Tf-OTOD03272001S2",
   "citation": "Bensoussan, Y., Sigaras, A., Rameau, A., Elemento, O., Powell, M., Dorr, D., Payne, P., Ravitsky, V., Bélisle-Pipon, J., Bahr, R., Watts, S., Bolser, D., Siu, J., Lerner-Ellis, J., Rudzicz, F., Boyer, M., Abdel-Aty, Y., Ahmed Syed, T., Anibal, J., ... Ghosh, S. (2025). Bridge2AI-Voice: An ethically-sourced, diverse voice dataset linked to health information (version 3.0.0). PhysioNet. RRID:SCR_007345. https://doi.org/10.13026/k81f-qr68",
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    "Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P. C., Mark, R., ... & Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation [Online]. 101 (23), pp. e215–e220. RRID:SCR_007345."
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   "conditionsOfAccess": "https://physionet.org/content/b2ai-voice/view-dua/3.0.0/",
   "copyrightNotice": "Copyright © 2026 University of South Florida all rights reserved",
   "contentSize": "12.9 GB",
   "ethicalReview": "Ethical Review by Vardit Ravitsky at the Hastings Center for Bioethics",
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   "dataGovernanceCommittee": "Satrajit Ghosh",
   "rai:dataLimitations": "The feature-only release does not include raw audio waveforms or free-speech transcripts, which limits certain types of modeling and error analysis and may constrain the ability to reproduce end-to-end audio pipelines.\nThis version only includes an adult cohort; models trained solely on this dataset may not generalize to younger age groups or to languages beyond those represented.\nBecause of de-identification, some granular demographic and socio-economic variables, fine-grained location data and narrative context have been removed, which reduces the risk of re-identification but also limits detailed fairness assessments and certain confounder adjustments.\nThe dataset does not provide predefined train–validation–test splits or benchmarking tasks and does not include explicit per-instance label-uncertainty measures, so researchers must design their own evaluation protocols and handle label noise and missingness.\nThe registered-access and controlled-access governance structure is appropriate for privacy but may limit participation by some institutions or researchers and can complicate the reproducibility of pipelines that require both features and raw audio.",
   "rai:dataBiases": "Sampling bias: participants are recruited from specialty clinics and associated institutions using a non-probability sampling strategy, with inclusion focused on specific disease cohorts and fluent English speakers; this produces clinically enriched case mixes that may not represent general population distributions.\nGeographic and cultural bias: data are collected at a limited number of North American sites, and although the project seeks diversity, it may under-represent speakers from other regions, cultures, languages and healthcare systems; early releases focus on English, with Spanish protocols planned but not yet fully represented.\nClinical spectrum bias: because participants are selected into disease cohorts where voice changes are expected, the prevalence and severity of conditions in the dataset differ from those in routine primary care or community settings, which may inflate model performance when evaluated within this dataset.\nDevice and environment bias: recordings are made using standardized but not identical hardware and primarily in clinical environments; future at-home collection or deployment on other devices may encounter different background noise, channel characteristics and user behavior.\nAlgorithmic bias in machine annotations: transcripts and some derived features rely on off-the-shelf models such as OpenAI Whisper and other audio toolkits whose performance may vary across demographic groups; the dataset team has not independently audited these tools for fairness, so downstream users should consider their biases when interpreting results.",
   "rai:dataUseCases": "Development, training and fine-tuning of machine-learning models that associate voice-derived features with diagnostic categories or symptom severity for conditions such as vocal fold pathology, neurological and neurodegenerative diseases, mood and anxiety disorders and respiratory illnesses.\nBenchmarking and validation of existing voice-biomarker algorithms by testing their performance on a clinically diverse, multi-site cohort with standardized tasks and rich phenotype data.\nExploratory research on acoustic, phonetic, prosodic and articulatory correlates of disease using de-identified derived features, including work on representation learning, domain adaptation and multimodal integration with other health data.\nMethodological research on fairness, robustness and AI safety in clinical voice models, including studies of bias related to demographic subgroups or recording conditions, subject to the ethical constraints in the data use agreement.\nThe dataset is explicitly not intended for operational decision making about specific individuals such as hiring, insurance pricing, law enforcement or surveillance, nor for attempts at re-identification or for uses likely to stigmatize individuals or groups.",
   "rai:dataReleaseMaintenancePlan": "Dataset releases follow a static versioning scheme managed through PhysioNet and related platforms, with version numbers such as 1.0, 1.1, 2.0.0, 2.0.1 and 3.0.0 and associated DOIs for each snapshot and for the latest version.\nReleases are coordinated by the Bridge2AI-Voice project team and the MIT Laboratory for Computational Physiology; release notes document added participants, new feature sets, reorganized phenotype tables and corrections such as spectrogram reprocessing and authorship updates.\nFuture updates are planned as additional participants are enrolled and Spanish-language protocols are incorporated; older versions remain accessible for reproducibility while users are encouraged to adopt the latest version.",
   "rai:dataCollection": "Prospective observational study conducted at multiple specialty clinics and academic hospitals across North America. Eligible adults presenting to voice, neurology, psychiatry and respiratory clinics, plus healthy controls, were screened against predefined inclusion and exclusion criteria. After informed consent, a standardized protocol was administered that combined structured voice and respiratory tasks such as sustained vowels, coughs and reading passages with demographic questions, health history, disease-specific questionnaires and other patient-reported outcomes. Data were captured on a mobile or tablet application and stored in REDCap, with most participants completing a single in-clinic session and a subset completing repeated sessions.",
   "rai:dataCollectionType": [
    "Direct measurement",
    "Surveys",
    "Self-reporting",
    "Secondary Data analysis",
    "Document analysis"
   ],
   "rai:dataCollectionMissingData": "Phenotype tables include a row for a participant only when at least one variable is non-missing, and participants may have repeated visits with differing responses. Questionnaire completion varies by participant and cohort, so some forms and items are systematically or sporadically missing. Some derived audio features could not be computed for all recordings due to quality or length constraints, and features relating to free speech or sensitive records have been removed. No additional imputation is applied in the released files.",
   "rai:dataCollectionRawData": "The original raw data consist of high-quality voice, speech and respiratory audio waveforms recorded on tablet or smartphone devices with headset microphones when available, plus responses to structured demographic and medical history questionnaires, disease-specific validated questionnaires and other patient-reported outcomes, together with clinical diagnoses and other metadata extracted from electronic health records. The public feature-only PhysioNet release contains de-identified derived representations of the audio, such as spectrograms, mel spectrograms, MFCCs, articulatory and prosodic features and phonetic posteriorgrams, along with tabular phenotype files and data dictionaries; access to the underlying raw audio is available only through a separate controlled-access process.",
   "rai:dataCollectionTimeframe": [
    "Data collection for the adult flagship cohort began after project launch in 2023 and is ongoing across five North American sites toward an anticipated enrollment of approximately 3,000 participants by November 2026; the participants and recordings included in dataset version 3.0.0 were collected roughly between 2023 and 2025.",
    "Dataset releases are static snapshots: v1.0 (initial public release in 2024), v1.1 (2025-01-17), v2.0.0 (2025-04-16), v2.0.1 (2025-08-18) and v3.0.0 (2025-12-16), each corresponding to a frozen state of the underlying data collection."
   ],
   "rai:dataImputationProtocol": "The public datasets do not apply global statistical imputation to missing fields; instead, missing questionnaire responses, phenotype variables and derived features are left as explicit missing values or omitted rows. The data release team audited missingness and users can implement study-specific imputation or complete-case strategies appropriate to their analyses.",
   "rai:dataManipulationProtocol": "Prior to release, data are transformed to reduce re-identification risk and align with regulatory and ethical requirements. Direct identifiers and high-risk quasi-identifiers are removed; dates are coarsened or rebased; geographic information is generalized to the country level; narrative text fields and transcripts of free speech are removed; and raw voice waveforms are withheld from this feature-only release. Additional filtering removes features for sensitive records and audio checks, and some derived features are dropped when processing fails or quality checks fail.",
   "rai:dataPreprocessingProtocol": [
    "Raw audio waveforms are converted to mono and resampled to 16 kHz using anti-aliasing filtering; they are then segmented by task and passed through quality checks.",
    "From the standardized audio, multiple derived feature sets are computed including spectrograms, mel spectrograms, MFCCs, articulatory features using the Speech Articulatory Coding package, pitch and loudness estimates, acoustic features from openSMILE, phonetic and prosodic features from Praat and parselmouth, and phonetic posteriorgrams using a dedicated model; all are stored as dense tensors in Parquet files with participant, session and task identifiers.",
    "Tabular phenotype and questionnaire files are exported from REDCap and merged using a dedicated open-source b2aiprep library, reorganizing the former single phenotype table into thematically organized TSV files with accompanying JSON data dictionaries; static per-recording feature summaries are provided in static_features.tsv, and de-identification and filtering steps are applied before packaging."
   ],
   "rai:dataAnnotationProtocol": "Annotations for this dataset consist primarily of clinical labels and questionnaire-derived measures associated with each participant and recording. Participants complete validated instruments such as VHI-10, PHQ-9, GAD-7, PANAS, respiratory and cough questionnaires and voice instruments, which generate numerical scores and categorical indicators according to each instrument's published scoring guidelines. Clinical investigators and care teams record diagnostic categories for voice, neurological, mood and respiratory conditions and other phenotype variables, drawing on clinical assessments and electronic health records; these labels are represented in disease-specific TSV files with accompanying JSON dictionaries. In addition, machine-generated transcripts of certain voice tasks and numerous automatically extracted acoustic and articulatory features provide further labels and descriptors at the recording level.",
   "rai:dataAnnotationAnalysis": [
    "Questionnaire-based labels are generated using the standard scoring rules for each validated instrument, yielding total and subscale scores that can be mapped to symptom-severity bands; the scoring logic is encoded in the REDCap instruments and documented in the data dictionaries.",
    "Diagnostic labels are derived from clinical assessments and electronic health records rather than crowd workers, so there is no explicit inter-rater majority-vote scheme; however, diagnoses are treated as reference labels and may include multiple comorbid conditions per participant.",
    "The data release team audited the combined phenotype and feature tables for internal consistency, missingness patterns and data entry issues, but per-annotator disagreement statistics or detailed label-uncertainty measures are not presently included in the released dataset."
   ],
   "rai:personalSensitiveInformation": [
    "The dataset encodes health-related information including diagnostic categories for voice disorders, neurological and neurodegenerative conditions, mood and psychiatric disorders and respiratory diseases, as well as symptom scores from questionnaires, which constitute sensitive personal health data.",
    "Demographic variables such as age, sex and country of data collection are included in coarsened form, while finer-grained geographic identifiers, direct identifiers and many socio-economic and cultural details have been removed during de-identification to reduce re-identification risk.",
    "Highly sensitive content including detailed narrative responses, some information about household income, traumatic life experiences and granular cultural identifiers has been removed entirely from this feature-only dataset; raw voice recordings, which are themselves biometric identifiers, are made available only under controlled access.",
    "Use of the data is restricted to authorized researchers under a registered-access license and associated data use agreement that explicitly forbids attempts at re-identification, stigmatizing or discriminatory uses and applications such as surveillance or high-stakes individual decision making."
   ],
   "rai:dataSocialImpact": "The project aims to create an ethically sourced, diverse voice dataset to support research on using voice as an accessible, low-cost biomarker for screening, diagnosis and monitoring of a broad range of health conditions, potentially improving early detection and care for underserved populations. At the same time, the consortium explicitly recognizes the risks associated with voice data, including privacy loss, misuse of voice biometrics and algorithmic harms such as discrimination in hiring, insurance or surveillance contexts; these risks motivate a governance framework with IRB oversight, rigorous de-identification, separation of feature-only and raw-audio tiers and usage restrictions encoded in the registered-access license and data use agreement. Dataset documentation and ongoing ethics workstreams are intended to help downstream users reason about these risks and design responsible analyses and models.",
   "rai:annotationsPerItem": "Each recording is associated with one set of clinical labels and questionnaire responses derived from a single participant, although participants may contribute multiple sessions over time; there is no crowd-sourced labeling and the documentation does not report multiple independent human ratings per individual recording.",
   "rai:annotatorDemographics": [
    "Clinical annotations, including diagnostic labels and certain phenotype variables, are created by clinicians and research staff at participating North American institutions spanning otolaryngology, neurology, neuroscience, pulmonology and allied health disciplines; detailed demographic breakdowns of annotators are not provided in the public documentation.",
    "Questionnaire-based labels reflect self-reported information from participants themselves, whose demographics are captured in coarsened form in the phenotype tables, for example age bands, sex and country of data collection; however, per-annotator demographic information, such as which specific clinician or staff member entered a label, is not exposed."
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   "rai:machineAnnotationTools": [
    "OpenSMILE for extraction of acoustic feature sets capturing temporal dynamics and spectral characteristics.",
    "Praat and the parselmouth interface for phonetic and prosodic feature extraction, including measures of fundamental frequency, formants and voice quality.",
    "TorchAudio-based pipelines for computing spectrograms, mel spectrograms, MFCCs and pitch tracks.",
    "Speech Articulatory Coding (sparc) package for estimating articulatory kinematics and related features.",
    "Phonetic posteriorgram models that produce time-varying distributions over phonetic units for each recording.",
    "OpenAI Whisper Large for automatic speech transcription of structured speech tasks, with transcripts from free-speech audio removed before release.",
    "The open-source b2aiprep library for orchestrating preprocessing, feature extraction and merging of phenotype data into the released files."
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   "rai:datannotationPlatform": [
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    "REDCap-based data capture system used to manage clinical and phenotype data.",
    "Electronic health record systems used as a source of some gold-standard diagnostic information.",
    "Open-source preprocessing and curation tools, including the b2aiprep library, used to merge and organize annotations with derived features."
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    "Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P. C., Mark, R., ... & Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation [Online]. 101 (23), pp. e215–e220. RRID:SCR_007345."
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    ]
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    "_summarized_by": "d4d rocrate normalize",
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    "note": "identical to the property names; list omitted"
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   "@context": {
    "@vocab": "https://schema.org/",
    "EVI": "https://w3id.org/EVI#"
   },
   "description": "Schema for B2AI VOICE demographics.tsv",
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    "@vocab": "https://schema.org/",
    "EVI": "https://w3id.org/EVI#"
   },
   "description": "Schema for Precancerous lesions",
   "license": "https://creativecommons.org/licenses/by/4.0/",
   "keywords": [],
   "published": true,
   "properties": {
    "_summarized_by": "d4d rocrate normalize",
    "count": 44,
    "columns": [
     "participant_id:integer",
     "diagnois_pl_gold_standard_diagnosis:string",
     "diagnosis_pl_degree_b_1:number",
     "diagnosis_pl_degree_b_2:string",
     "diagnosis_pl_degree_comments:string",
     "diagnosis_pl_degree_l_1:number",
     "diagnosis_pl_degree_l_2:string",
     "diagnosis_pl_degree_os_1:number",
     "diagnosis_pl_degree_os_2:string",
     "diagnosis_pl_degree_p_1:number",
     "diagnosis_pl_degree_p_2:string",
     "diagnosis_pl_degree_r_1:number",
     "diagnosis_pl_degree_r_2:string",
     "diagnosis_pl_degree_s_1:number",
     "diagnosis_pl_degree_s_2:string",
     "diagnosis_pl_ds_e:string",
     "diagnosis_pl_ds_e_wd_g:string",
     "diagnosis_pl_ds_k:string",
     "diagnosis_pl_ds_k_wd_g:string",
     "diagnosis_pl_ds_l:string",
     "diagnosis_pl_ds_l_wd_g:string",
     "diagnosis_pl_gold_standard_diagnosis:string",
     "diagnosis_pl_treatment:string",
     "diagnosis_pl_ds___erythroplakia:number",
     "diagnosis_pl_ds___keratosis:number",
     "diagnosis_pl_ds___leukoplakia:number",
     "diagnosis_pl_ds___other:string",
     "diagnosis_pl_ds_ll___both_vocal_folds:number",
     "diagnosis_pl_ds_ll___left_vocal_fold:number",
     "diagnosis_pl_ds_ll___right_vocal_fold:number",
     "diagnosis_pl_ds_ll___subglottal_areas:string",
     "diagnosis_pl_ds_ll___ventricular_folds:string",
     "diagnosis_pl_etiology___laryngopharyngeal_reflux:number",
     "diagnosis_pl_etiology___other_irritants:number",
     "diagnosis_pl_etiology___past_or_present_smoking:number",
     "diagnosis_pl_gold_standard_diagnosis_confirmation_method___biopsy_pathology:number",
     "diagnosis_pl_gold_standard_diagnosis_confirmation_method___laryngoscopy_stroboscopy:number",
     "diagnosis_pl_treatment_select___laser_ablation:number",
     "diagnosis_pl_treatment_select___laser_resection:number",
     "diagnosis_pl_treatment_select___microlaryngeal_surgery_without_laser:number",
     "diagnosis_pl_treatment_select___other:string",
     "diagnosis_pl_treatment_select___radiotherapy_persistent_or_recurrent_lesions:string",
     "diagnosis_pl_treatment_select___surveillance_only:number",
     "diagnosis_pl_treatment_select___voice_or_speech_therapy:string"
    ]
   },
   "type": "object",
   "additionalProperties": true,
   "required": {
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    "count": 44,
    "note": "identical to the property names; list omitted"
   },
   "separator": "\t",
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   "$schema": "https://json-schema.org/draft/2020-12/schema"
  },
  {
   "@id": "ark:59853/b2ai-voice-schema-phenotype-psychiatric-history",
   "@type": "EVI:Schema",
   "name": "Schema for Psychiatric history",
   "@context": {
    "@vocab": "https://schema.org/",
    "EVI": "https://w3id.org/EVI#"
   },
   "description": "Schema for Psychiatric history",
   "license": "https://creativecommons.org/licenses/by/4.0/",
   "keywords": [],
   "published": true,
   "properties": {
    "participant_id": {
     "description": "Column participant_id",
     "index": 0,
     "type": "integer"
    },
    "mph_duration": {
     "description": "Column mph_duration",
     "index": 1,
     "type": "number"
    },
    "mph_prescribed_medication": {
     "description": "Column mph_prescribed_medication",
     "index": 2,
     "type": "string"
    },
    "mph_see_mental_health_professional": {
     "description": "Column mph_see_mental_health_professional",
     "index": 3,
     "type": "string"
    },
    "mph_session_id": {
     "description": "Column mph_session_id",
     "index": 4,
     "type": "string"
    }
   },
   "type": "object",
   "additionalProperties": true,
   "required": [
    "participant_id",
    "mph_duration",
    "mph_prescribed_medication",
    "mph_see_mental_health_professional",
    "mph_session_id"
   ],
   "separator": "\t",
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   "$schema": "https://json-schema.org/draft/2020-12/schema"
  },
  {
   "@id": "ark:59853/b2ai-voice-schema-phenotype-ptsd-adult",
   "@type": "EVI:Schema",
   "name": "Schema for Ptsd adult",
   "@context": {
    "@vocab": "https://schema.org/",
    "EVI": "https://w3id.org/EVI#"
   },
   "description": "Schema for Ptsd adult",
   "license": "https://creativecommons.org/licenses/by/4.0/",
   "keywords": [],
   "published": true,
   "properties": {
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    "count": 12,
    "columns": [
     "participant_id:integer",
     "avoiding_reminders:string",
     "emotionally_upset:string",
     "feeling_jumpy:string",
     "flashbacks:string",
     "irritable:string",
     "losing_interest:string",
     "neg_emotional_state:string",
     "ptsd_duration:number",
     "ptsd_session_id:string",
     "super_alert:string",
     "thinking_stressful_event:string"
    ]
   },
   "type": "object",
   "additionalProperties": true,
   "required": {
    "_summarized_by": "d4d rocrate normalize",
    "count": 12,
    "note": "identical to the property names; list omitted"
   },
   "separator": "\t",
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   "examples": [],
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   "fairscapeVersion": "1.0.24",
   "$schema": "https://json-schema.org/draft/2020-12/schema"
  },
  {
   "@id": "ark:59853/b2ai-voice-schema-phenotype-unexplained-chronic-cough",
   "@type": "EVI:Schema",
   "name": "Schema for Unexplained chronic cough",
   "@context": {
    "@vocab": "https://schema.org/",
    "EVI": "https://w3id.org/EVI#"
   },
   "description": "Schema for Unexplained chronic cough",
   "license": "https://creativecommons.org/licenses/by/4.0/",
   "keywords": [],
   "published": true,
   "properties": {
    "_summarized_by": "d4d rocrate normalize",
    "count": 32,
    "columns": [
     "participant_id:integer",
     "diagnosis_ucc_at:string",
     "diagnosis_ucc_cuae:string",
     "diagnosis_ucc_gediapc:string",
     "diagnosis_ucc_hcxob:string",
     "diagnosis_ucc_hsoe:string",
     "diagnosis_ucc_huatot:string",
     "diagnosis_ucc_hurada:string",
     "diagnosis_ucc_huse:string",
     "diagnosis_ucc_oai:string",
     "diagnosis_ucc_pdapc:string",
     "diagnosis_ucc_gold_standard_diagnosis:string",
     "diagnosis_ucc_rada:string",
     "diagnosis_ucc_at_s___multimodality_speech_pathology_therapy:number",
     "diagnosis_ucc_at_s___neuromodulator:number",
     "diagnosis_ucc_at_s___other:number",
     "diagnosis_ucc_at_s___superior_laryngeal_nerve_block:number",
     "diagnosis_ucc_atot___egd_or_tne:number",
     "diagnosis_ucc_atot___other:number",
     "diagnosis_ucc_atot___phimpedance_probe_or_bravo_probe:number",
     "diagnosis_ucc_atot___trial_of_proton_pump_inhibitor:number",
     "diagnosis_ucc_cxcob___bronchoscopy:number",
     "diagnosis_ucc_cxcob___chest_xray:number",
     "diagnosis_ucc_cxcob___ct_chest:number",
     "diagnosis_ucc_hspd___asthma:number",
     "diagnosis_ucc_hspd___bronchiectasis:number",
     "diagnosis_ucc_hspd___copd:number",
     "diagnosis_ucc_hspd___idiopathic_pulmonary_fibrosis:string",
     "diagnosis_ucc_hspd___lung_cancer:string",
     "diagnosis_ucc_hspd___other:number",
     "diagnosis_ucc_hspd___pulmonary_granuloma:string",
     "diagnosis_ucc_hspd___tb_infection:number"
    ]
   },
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    "count": 32,
    "note": "identical to the property names; list omitted"
   },
   "separator": "\t",
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   "$schema": "https://json-schema.org/draft/2020-12/schema"
  },
  {
   "@id": "ark:59853/b2ai-voice-schema-phenotype-unilateral-vocal-fold-paralysis",
   "@type": "EVI:Schema",
   "name": "Schema for Unilateral vocal fold paralysis",
   "@context": {
    "@vocab": "https://schema.org/",
    "EVI": "https://w3id.org/EVI#"
   },
   "description": "Schema for Unilateral vocal fold paralysis",
   "license": "https://creativecommons.org/licenses/by/4.0/",
   "keywords": [],
   "published": true,
   "properties": {
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    "count": 33,
    "columns": [
     "participant_id:integer",
     "diagnosis_degree_b:number",
     "diagnosis_degree_b_2:string",
     "diagnosis_degree_l:number",
     "diagnosis_degree_l_2:string",
     "diagnosis_degree_os:number",
     "diagnosis_degree_os_2:string",
     "diagnosis_degree_p:number",
     "diagnosis_degree_p_2:string",
     "diagnosis_degree_r:number",
     "diagnosis_degree_r_2:string",
     "diagnosis_degree_s:number",
     "diagnosis_degree_s_2:string",
     "diagnosis_uvfp_ds:string",
     "diagnosis_uvfp_treatment:string",
     "diagnosis_uvfp_dse:string",
     "diagnosis_uvfp_etiology:string",
     "diagnosis_uvfp_gold_standard_diagnosis:string",
     "diagnosis_uvfp_iatrogenic:string",
     "diagnosis_uvfp_tumor:string",
     "diagnosis_uvfp_treatment_select___other:number",
     "diagnosis_uvfp_treatment_select___speech_therapy:number",
     "diagnosis_uvfp_treatment_select___surgery:number",
     "diagnosis_uvfp_treatment_surgery___arytenoid_adduction:string",
     "diagnosis_uvfp_treatment_surgery___other:number",
     "diagnosis_uvfp_treatment_surgery___thyroplasty:number",
     "diagnosis_uvfp_treatment_surgery___vocal_fold_injection_augmentation:number",
     "diagnosis_uvfp_treatment_surgery_thyroplasty___goretex:number",
     "diagnosis_uvfp_treatment_surgery_thyroplasty___silastic:number",
     "diagnosis_uvfp_treatment_surgery_vfia___caha:number",
     "diagnosis_uvfp_treatment_surgery_vfia___fat_injection:number",
     "diagnosis_uvfp_treatment_surgery_vfia___gel:string",
     "diagnosis_uvfp_treatment_surgery_vfia___hyaluronic_acid_augmentation:number"
    ]
   },
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    "count": 33,
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  },
  {
   "@id": "ark:59853/b2ai-voice-schema-phenotype-custom-affect-scale",
   "@type": "EVI:Schema",
   "name": "Schema for Custom affect scale",
   "@context": {
    "@vocab": "https://schema.org/",
    "EVI": "https://w3id.org/EVI#"
   },
   "description": "Schema for Custom affect scale",
   "license": "https://creativecommons.org/licenses/by/4.0/",
   "keywords": [],
   "published": true,
   "properties": {
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    "columns": [
     "participant_id:integer",
     "agitated:number",
     "concentrated:number",
     "custom_affect_scale_duration:number",
     "custom_affect_scale_session_id:string",
     "desire_to_escape:number",
     "energetic:number",
     "irritated:number",
     "joyful:number",
     "lack_of_pleasure:number",
     "lonely:number",
     "motivated:number",
     "relaxed:number",
     "sad_or_down:number",
     "tired:number",
     "worried:number"
    ]
   },
   "type": "object",
   "additionalProperties": true,
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    "note": "identical to the property names; list omitted"
   },
   "separator": "\t",
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   "$schema": "https://json-schema.org/draft/2020-12/schema"
  },
  {
   "@id": "ark:59853/b2ai-voice-schema-phenotype-dsm5-adult",
   "@type": "EVI:Schema",
   "name": "Schema for Dsm5 adult",
   "@context": {
    "@vocab": "https://schema.org/",
    "EVI": "https://w3id.org/EVI#"
   },
   "description": "Schema for Dsm5 adult",
   "license": "https://creativecommons.org/licenses/by/4.0/",
   "keywords": [],
   "published": true,
   "properties": {
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    "count": 41,
    "columns": [
     "participant_id:integer",
     "avoiding_situations:string",
     "drinking_more:string",
     "dsm_5_duration:number",
     "dsm_5_session_id:string",
     "feeling_detached:string",
     "feeling_down:string",
     "feeling_more_irritated:string",
     "feeling_nervous:string",
     "feeling_panic:string",
     "hearing_things:string",
     "illness_not_taken_serious:string",
     "isolated:string",
     "little_interest:string",
     "medication_use:string",
     "memory_issues:string",
     "no_purpose:string",
     "q1_happy:string",
     "q2_self_confident:string",
     "q3_sleep:string",
     "q4_talk:string",
     "q5_active:string",
     "repeat_acts:string",
     "self_harm:string",
     "sleep_quality:string",
     "sleeping_less:string",
     "smoking_more:string",
     "social_phobia_1:string",
     "social_phobia_10:string",
     "social_phobia_2:string",
     "social_phobia_3:string",
     "social_phobia_4:string",
     "social_phobia_5:string",
     "social_phobia_6:string",
     "social_phobia_7:string",
     "social_phobia_8:string",
     "social_phobia_9:string",
     "someone_hear_thoughts:string",
     "starting_more_projects:string",
     "unexplained_aches:string",
     "unpleasant_thoughts:string"
    ]
   },
   "type": "object",
   "additionalProperties": true,
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    "note": "identical to the property names; list omitted"
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  {
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   "@type": "EVI:Schema",
   "name": "Schema for Dyspnea index",
   "@context": {
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    "EVI": "https://w3id.org/EVI#"
   },
   "description": "Schema for Dyspnea index",
   "license": "https://creativecommons.org/licenses/by/4.0/",
   "keywords": [],
   "published": true,
   "properties": {
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    "columns": [
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     "di_air_in:string",
     "di_breathing_stresses_me:string",
     "di_breathing_worse_stress:string",
     "di_effort_breathe:string",
     "di_exercise:string",
     "di_restrict_personal_social_life:string",
     "di_sound_breathing_in:string",
     "di_strain:string",
     "di_tightness_throat:string",
     "di_weather_changes:string",
     "dyspnea_index_duration:number",
     "dyspnea_index_session_id:string"
    ]
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   "name": "Schema for Gad7 anxiety",
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    "columns": [
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     "afraid_of_things:string",
     "cant_control_worry:string",
     "easily_agitated:string",
     "gad_7_duration:number",
     "gad_7_session_id:string",
     "hard_to_sit_still:string",
     "nervous_anxious:string",
     "tough_to_work:string",
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     "worry_too_much:string"
    ]
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  {
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   "name": "Schema for Leicester cough questionnaire",
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    "EVI": "https://w3id.org/EVI#"
   },
   "description": "Schema for Leicester cough questionnaire",
   "license": "https://creativecommons.org/licenses/by/4.0/",
   "keywords": [],
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   "properties": {
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    "columns": [
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     "lcq_anxious:string",
     "lcq_bout:string",
     "lcq_chest_stomach_pains:string",
     "lcq_embarrassed:string",
     "lcq_energy:string",
     "lcq_exposure_paint:string",
     "lcq_fed_up:string",
     "lcq_felt_in_control:string",
     "lcq_frustrated:string",
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     "lcq_interfere_job:string",
     "lcq_interfere_life:string",
     "lcq_interrupt_conversation:string",
     "lcq_other_people:string",
     "lcq_partner:string",
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     "leicester_cough_session_id:string"
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   "description": "Schema for Panas",
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   "fairscapeVersion": "1.0.24",
   "$schema": "https://json-schema.org/draft/2020-12/schema"
  },
  {
   "@id": "ark:59853/b2ai-voice-schema-phenotype-productive-vocabulary",
   "@type": "EVI:Schema",
   "name": "Schema for Productive vocabulary",
   "@context": {
    "@vocab": "https://schema.org/",
    "EVI": "https://w3id.org/EVI#"
   },
   "description": "Schema for Productive vocabulary",
   "license": "https://creativecommons.org/licenses/by/4.0/",
   "keywords": [],
   "published": true,
   "properties": {
    "_summarized_by": "d4d rocrate normalize",
    "count": 22,
    "columns": [
     "participant_id:integer",
     "vocabulary_duration:number",
     "vocabulary_item_correct_1:string",
     "vocabulary_item_correct_2:string",
     "vocabulary_item_correct_3:string",
     "vocabulary_item_correct_4:string",
     "vocabulary_item_correct_5:string",
     "vocabulary_item_correct_6:string",
     "vocabulary_item_difficulty_1:number",
     "vocabulary_item_difficulty_2:number",
     "vocabulary_item_difficulty_3:number",
     "vocabulary_item_difficulty_4:number",
     "vocabulary_item_difficulty_5:number",
     "vocabulary_item_difficulty_6:number",
     "vocabulary_item_word_1:string",
     "vocabulary_item_word_2:string",
     "vocabulary_item_word_3:string",
     "vocabulary_item_word_4:string",
     "vocabulary_item_word_5:string",
     "vocabulary_item_word_6:string",
     "vocabulary_recording_acoustic_task_id:string",
     "vocabulary_session_id:string"
    ]
   },
   "type": "object",
   "additionalProperties": true,
   "required": {
    "_summarized_by": "d4d rocrate normalize",
    "count": 22,
    "note": "identical to the property names; list omitted"
   },
   "separator": "\t",
   "header": true,
   "examples": [],
   "isPartOf": [],
   "fairscapeVersion": "1.0.24",
   "$schema": "https://json-schema.org/draft/2020-12/schema"
  },
  {
   "@id": "ark:59853/b2ai-voice-schema-phenotype-vhi10",
   "@type": "EVI:Schema",
   "name": "Schema for Vhi10",
   "@context": {
    "@vocab": "https://schema.org/",
    "EVI": "https://w3id.org/EVI#"
   },
   "description": "Schema for Vhi10",
   "license": "https://creativecommons.org/licenses/by/4.0/",
   "keywords": [],
   "published": true,
   "properties": {
    "_summarized_by": "d4d rocrate normalize",
    "count": 14,
    "columns": [
     "participant_id:integer",
     "ask_whats_wrong_voice:string",
     "left_out_convo:string",
     "strain_voice:string",
     "tough_to_understand:string",
     "vhi_duration:number",
     "vhi_session_id:string",
     "voice_clarity:string",
     "voice_difficult_hear:string",
     "voice_handicapped:string",
     "voice_lose_income:string",
     "voice_restrict_social:string",
     "voice_upsetting:string",
     "vhi_10_calc_score:number"
    ]
   },
   "type": "object",
   "additionalProperties": true,
   "required": {
    "_summarized_by": "d4d rocrate normalize",
    "count": 14,
    "note": "identical to the property names; list omitted"
   },
   "separator": "\t",
   "header": true,
   "examples": [],
   "isPartOf": [],
   "fairscapeVersion": "1.0.24",
   "$schema": "https://json-schema.org/draft/2020-12/schema"
  },
  {
   "@id": "ark:59853/b2ai-voice-schema-phenotype-voice-perception",
   "@type": "EVI:Schema",
   "name": "Schema for Voice perception",
   "@context": {
    "@vocab": "https://schema.org/",
    "EVI": "https://w3id.org/EVI#"
   },
   "description": "Schema for Voice perception",
   "license": "https://creativecommons.org/licenses/by/4.0/",
   "keywords": [],
   "published": true,
   "properties": {
    "participant_id": {
     "description": "Column participant_id",
     "index": 0,
     "type": "integer"
    },
    "voice_perception_duration": {
     "description": "Column voice_perception_duration",
     "index": 1,
     "type": "number"
    },
    "voice_perception_session_id": {
     "description": "Column voice_perception_session_id",
     "index": 2,
     "type": "string"
    },
    "voice_quality_perception": {
     "description": "Column voice_quality_perception",
     "index": 3,
     "type": "number"
    }
   },
   "type": "object",
   "additionalProperties": true,
   "required": [
    "participant_id",
    "voice_perception_duration",
    "voice_perception_session_id",
    "voice_quality_perception"
   ],
   "separator": "\t",
   "header": true,
   "examples": [],
   "isPartOf": [],
   "fairscapeVersion": "1.0.24",
   "$schema": "https://json-schema.org/draft/2020-12/schema"
  },
  {
   "@id": "ark:59853/b2ai-voice-schema-phenotype-acoustic-task",
   "@type": "EVI:Schema",
   "name": "Schema for Acoustic task",
   "@context": {
    "@vocab": "https://schema.org/",
    "EVI": "https://w3id.org/EVI#"
   },
   "description": "Schema for Acoustic task",
   "license": "https://creativecommons.org/licenses/by/4.0/",
   "keywords": [],
   "published": true,
   "properties": {
    "participant_id": {
     "description": "Column participant_id",
     "index": 0,
     "type": "integer"
    },
    "acoustic_task_cohort": {
     "description": "Column acoustic_task_cohort",
     "index": 1,
     "type": "string"
    },
    "acoustic_task_duration": {
     "description": "Column acoustic_task_duration",
     "index": 2,
     "type": "number"
    },
    "acoustic_task_id": {
     "description": "Column acoustic_task_id",
     "index": 3,
     "type": "string"
    },
    "acoustic_task_name": {
     "description": "Column acoustic_task_name",
     "index": 4,
     "type": "string"
    },
    "acoustic_task_session_id": {
     "description": "Column acoustic_task_session_id",
     "index": 5,
     "type": "string"
    },
    "acoustic_task_status": {
     "description": "Column acoustic_task_status",
     "index": 6,
     "type": "string"
    }
   },
   "type": "object",
   "additionalProperties": true,
   "required": [
    "participant_id",
    "acoustic_task_cohort",
    "acoustic_task_duration",
    "acoustic_task_id",
    "acoustic_task_name",
    "acoustic_task_session_id",
    "acoustic_task_status"
   ],
   "separator": "\t",
   "header": true,
   "examples": [],
   "isPartOf": [],
   "fairscapeVersion": "1.0.24",
   "$schema": "https://json-schema.org/draft/2020-12/schema"
  },
  {
   "@id": "ark:59853/b2ai-voice-schema-phenotype-harvard-sentences",
   "@type": "EVI:Schema",
   "name": "Schema for Harvard sentences",
   "@context": {
    "@vocab": "https://schema.org/",
    "EVI": "https://w3id.org/EVI#"
   },
   "description": "Schema for Harvard sentences",
   "license": "https://creativecommons.org/licenses/by/4.0/",
   "keywords": [],
   "published": true,
   "properties": {
    "participant_id": {
     "description": "Column participant_id",
     "index": 0,
     "type": "integer"
    },
    "harvard_sentences_duration": {
     "description": "Column harvard_sentences_duration",
     "index": 1,
     "type": "number"
    },
    "harvard_sentences_list_order": {
     "description": "Column harvard_sentences_list_order",
     "index": 2,
     "type": "string"
    },
    "harvard_sentences_recording_acoustic_task_id": {
     "description": "Column harvard_sentences_recording_acoustic_task_id",
     "index": 3,
     "type": "string"
    },
    "harvard_sentences_session_id": {
     "description": "Column harvard_sentences_session_id",
     "index": 4,
     "type": "string"
    }
   },
   "type": "object",
   "additionalProperties": true,
   "required": [
    "participant_id",
    "harvard_sentences_duration",
    "harvard_sentences_list_order",
    "harvard_sentences_recording_acoustic_task_id",
    "harvard_sentences_session_id"
   ],
   "separator": "\t",
   "header": true,
   "examples": [],
   "isPartOf": [],
   "fairscapeVersion": "1.0.24",
   "$schema": "https://json-schema.org/draft/2020-12/schema"
  },
  {
   "@id": "ark:59853/b2ai-voice-schema-phenotype-random-item-generation",
   "@type": "EVI:Schema",
   "name": "Schema for Random item generation",
   "@context": {
    "@vocab": "https://schema.org/",
    "EVI": "https://w3id.org/EVI#"
   },
   "description": "Schema for Random item generation",
   "license": "https://creativecommons.org/licenses/by/4.0/",
   "keywords": [],
   "published": true,
   "properties": {
    "participant_id": {
     "description": "Column participant_id",
     "index": 0,
     "type": "integer"
    },
    "random_duration": {
     "description": "Column random_duration",
     "index": 1,
     "type": "number"
    },
    "random_item_generation_category": {
     "description": "Column random_item_generation_category",
     "index": 2,
     "type": "string"
    },
    "random_recording_acoustic_task_id": {
     "description": "Column random_recording_acoustic_task_id",
     "index": 3,
     "type": "string"
    },
    "random_session_id": {
     "description": "Column random_session_id",
     "index": 4,
     "type": "string"
    }
   },
   "type": "object",
   "additionalProperties": true,
   "required": [
    "participant_id",
    "random_duration",
    "random_item_generation_category",
    "random_recording_acoustic_task_id",
    "random_session_id"
   ],
   "separator": "\t",
   "header": true,
   "examples": [],
   "isPartOf": [],
   "fairscapeVersion": "1.0.24",
   "$schema": "https://json-schema.org/draft/2020-12/schema"
  },
  {
   "@id": "ark:59853/b2ai-voice-schema-phenotype-recording",
   "@type": "EVI:Schema",
   "name": "Schema for Recording",
   "@context": {
    "@vocab": "https://schema.org/",
    "EVI": "https://w3id.org/EVI#"
   },
   "description": "Schema for Recording",
   "license": "https://creativecommons.org/licenses/by/4.0/",
   "keywords": [],
   "published": true,
   "properties": {
    "_summarized_by": "d4d rocrate normalize",
    "count": 11,
    "columns": [
     "participant_id:integer",
     "recording_acoustic_task_id:string",
     "recording_duration:number",
     "recording_id:string",
     "recording_input_gain:number",
     "recording_microphone:string",
     "recording_name:string",
     "recording_profile_name:string",
     "recording_profile_version:string",
     "recording_session_id:string",
     "recording_size:number"
    ]
   },
   "type": "object",
   "additionalProperties": true,
   "required": {
    "_summarized_by": "d4d rocrate normalize",
    "count": 11,
    "note": "identical to the property names; list omitted"
   },
   "separator": "\t",
   "header": true,
   "examples": [],
   "isPartOf": [],
   "fairscapeVersion": "1.0.24",
   "$schema": "https://json-schema.org/draft/2020-12/schema"
  },
  {
   "@id": "ark:59853/b2ai-voice-schema-phenotype-session",
   "@type": "EVI:Schema",
   "name": "Schema for Session",
   "@context": {
    "@vocab": "https://schema.org/",
    "EVI": "https://w3id.org/EVI#"
   },
   "description": "Schema for Session",
   "license": "https://creativecommons.org/licenses/by/4.0/",
   "keywords": [],
   "published": true,
   "properties": {
    "participant_id": {
     "description": "Column participant_id",
     "index": 0,
     "type": "integer"
    },
    "session_assigned_tasks": {
     "description": "Column session_assigned_tasks",
     "index": 1,
     "type": "string"
    },
    "session_duration": {
     "description": "Column session_duration",
     "index": 2,
     "type": "number"
    },
    "session_id": {
     "description": "Column session_id",
     "index": 3,
     "type": "string"
    },
    "session_is_control_participant": {
     "description": "Column session_is_control_participant",
     "index": 4,
     "type": "string"
    },
    "session_status": {
     "description": "Column session_status",
     "index": 5,
     "type": "string"
    },
    "session_completed_by___just_me": {
     "description": "Column session_completed_by___just_me",
     "index": 6,
     "type": "number"
    },
    "session_completed_by___someone_else": {
     "description": "Column session_completed_by___someone_else",
     "index": 7,
     "type": "string"
    }
   },
   "type": "object",
   "additionalProperties": true,
   "required": [
    "participant_id",
    "session_assigned_tasks",
    "session_duration",
    "session_id",
    "session_is_control_participant",
    "session_status",
    "session_completed_by___just_me",
    "session_completed_by___someone_else"
   ],
   "separator": "\t",
   "header": true,
   "examples": [],
   "isPartOf": [],
   "fairscapeVersion": "1.0.24",
   "$schema": "https://json-schema.org/draft/2020-12/schema"
  },
  {
   "@id": "ark:59853/b2ai-voice-schema-phenotype-stroop",
   "@type": "EVI:Schema",
   "name": "Schema for Stroop",
   "@context": {
    "@vocab": "https://schema.org/",
    "EVI": "https://w3id.org/EVI#"
   },
   "description": "Schema for Stroop",
   "license": "https://creativecommons.org/licenses/by/4.0/",
   "keywords": [],
   "published": true,
   "properties": {
    "_summarized_by": "d4d rocrate normalize",
    "count": 64,
    "columns": [
     "participant_id:integer",
     "stroop_duration:number",
     "stroop_item_color_1:string",
     "stroop_item_color_10:string",
     "stroop_item_color_11:string",
     "stroop_item_color_12:string",
     "stroop_item_color_13:string",
     "stroop_item_color_14:string",
     "stroop_item_color_15:string",
     "stroop_item_color_2:string",
     "stroop_item_color_3:string",
     "stroop_item_color_4:string",
     "stroop_item_color_5:string",
     "stroop_item_color_6:string",
     "stroop_item_color_7:string",
     "stroop_item_color_8:string",
     "stroop_item_color_9:string",
     "stroop_item_correct_1:string",
     "stroop_item_correct_10:string",
     "stroop_item_correct_11:string",
     "stroop_item_correct_12:string",
     "stroop_item_correct_13:string",
     "stroop_item_correct_14:string",
     "stroop_item_correct_15:string",
     "stroop_item_correct_2:string",
     "stroop_item_correct_3:string",
     "stroop_item_correct_4:string",
     "stroop_item_correct_5:string",
     "stroop_item_correct_6:string",
     "stroop_item_correct_7:string",
     "stroop_item_correct_8:string",
     "stroop_item_correct_9:string",
     "stroop_item_stimulus_1:string",
     "stroop_item_stimulus_10:string",
     "stroop_item_stimulus_11:string",
     "stroop_item_stimulus_12:string",
     "stroop_item_stimulus_13:string",
     "stroop_item_stimulus_14:string",
     "stroop_item_stimulus_15:string",
     "stroop_item_stimulus_2:string",
     "stroop_item_stimulus_3:string",
     "stroop_item_stimulus_4:string",
     "stroop_item_stimulus_5:string",
     "stroop_item_stimulus_6:string",
     "stroop_item_stimulus_7:string",
     "stroop_item_stimulus_8:string",
     "stroop_item_stimulus_9:string",
     "stroop_item_time_1:number",
     "stroop_item_time_10:number",
     "stroop_item_time_11:number",
     "stroop_item_time_12:number",
     "stroop_item_time_13:number",
     "stroop_item_time_14:number",
     "stroop_item_time_15:number",
     "stroop_item_time_2:number",
     "stroop_item_time_3:number",
     "stroop_item_time_4:number",
     "stroop_item_time_5:number",
     "stroop_item_time_6:number",
     "stroop_item_time_7:number",
     "stroop_item_time_8:number",
     "stroop_item_time_9:number",
     "stroop_recording_acoustic_task_id:string",
     "stroop_session_id:string"
    ]
   },
   "type": "object",
   "additionalProperties": true,
   "required": {
    "_summarized_by": "d4d rocrate normalize",
    "count": 64,
    "note": "identical to the property names; list omitted"
   },
   "separator": "\t",
   "header": true,
   "examples": [],
   "isPartOf": [],
   "fairscapeVersion": "1.0.24",
   "$schema": "https://json-schema.org/draft/2020-12/schema"
  },
  {
   "@id": "ark:59853/b2ai-voice-schema-phenotype-voice-perception",
   "@type": "EVI:Schema",
   "name": "Schema for Voice perception",
   "@context": {
    "@vocab": "https://schema.org/",
    "EVI": "https://w3id.org/EVI#"
   },
   "description": "Schema for Voice perception",
   "license": "https://creativecommons.org/licenses/by/4.0/",
   "keywords": [],
   "published": true,
   "properties": {
    "participant_id": {
     "description": "Column participant_id",
     "index": 0,
     "type": "integer"
    },
    "voice_perception_duration": {
     "description": "Column voice_perception_duration",
     "index": 1,
     "type": "number"
    },
    "voice_perception_session_id": {
     "description": "Column voice_perception_session_id",
     "index": 2,
     "type": "string"
    },
    "voice_quality_perception": {
     "description": "Column voice_quality_perception",
     "index": 3,
     "type": "number"
    }
   },
   "type": "object",
   "additionalProperties": true,
   "required": [
    "participant_id",
    "voice_perception_duration",
    "voice_perception_session_id",
    "voice_quality_perception"
   ],
   "separator": "\t",
   "header": true,
   "examples": [],
   "isPartOf": [],
   "fairscapeVersion": "1.0.24",
   "$schema": "https://json-schema.org/draft/2020-12/schema"
  },
  {
   "@id": "ark:59853/b2ai-voice-schema-phenotype-voice-problem-severity",
   "@type": "EVI:Schema",
   "name": "Schema for Voice problem severity",
   "@context": {
    "@vocab": "https://schema.org/",
    "EVI": "https://w3id.org/EVI#"
   },
   "description": "Schema for Voice problem severity",
   "license": "https://creativecommons.org/licenses/by/4.0/",
   "keywords": [],
   "published": true,
   "properties": {
    "participant_id": {
     "description": "Column participant_id",
     "index": 0,
     "type": "integer"
    },
    "describe_the_severity_of_a": {
     "description": "Column describe_the_severity_of_a",
     "index": 1,
     "type": "number"
    },
    "voice_severity_duration": {
     "description": "Column voice_severity_duration",
     "index": 2,
     "type": "number"
    },
    "voice_severity_session_id": {
     "description": "Column voice_severity_session_id",
     "index": 3,
     "type": "string"
    }
   },
   "type": "object",
   "additionalProperties": true,
   "required": [
    "participant_id",
    "describe_the_severity_of_a",
    "voice_severity_duration",
    "voice_severity_session_id"
   ],
   "separator": "\t",
   "header": true,
   "examples": [],
   "isPartOf": [],
   "fairscapeVersion": "1.0.24",
   "$schema": "https://json-schema.org/draft/2020-12/schema"
  },
  {
   "@id": "ark:59853/b2ai-voice-schema-phenotype-winograd",
   "@type": "EVI:Schema",
   "name": "Schema for Winograd",
   "@context": {
    "@vocab": "https://schema.org/",
    "EVI": "https://w3id.org/EVI#"
   },
   "description": "Schema for Winograd",
   "license": "https://creativecommons.org/licenses/by/4.0/",
   "keywords": [],
   "published": true,
   "properties": {
    "_summarized_by": "d4d rocrate normalize",
    "count": 282,
    "columns": [
     "participant_id:integer",
     "winograd_duration:number",
     "winograd_number_questions:number",
     "winograd_q_1:string",
     "winograd_q_10:string",
     "winograd_q_100:string",
     "winograd_q_101:string",
     "winograd_q_102:string",
     "winograd_q_103:string",
     "winograd_q_104:string",
     "winograd_q_105:string",
     "winograd_q_106:string",
     "winograd_q_107:string",
     "winograd_q_108:string",
     "winograd_q_109:string",
     "winograd_q_11:string",
     "winograd_q_110:string",
     "winograd_q_111:string",
     "winograd_q_112:string",
     "winograd_q_113:string",
     "winograd_q_114:string",
     "winograd_q_115:string",
     "winograd_q_116:string",
     "winograd_q_117:string",
     "winograd_q_118:string",
     "winograd_q_119:string",
     "winograd_q_12:string",
     "winograd_q_120:string",
     "winograd_q_121:string",
     "winograd_q_122:string",
     "winograd_q_123:string",
     "winograd_q_124:string",
     "winograd_q_125:string",
     "winograd_q_126:string",
     "winograd_q_127:string",
     "winograd_q_128:string",
     "winograd_q_129:string",
     "winograd_q_13:string",
     "winograd_q_130:string",
     "winograd_q_131:string",
     "winograd_q_132:string",
     "winograd_q_133:string",
     "winograd_q_134:string",
     "winograd_q_135:string",
     "winograd_q_136:string",
     "winograd_q_137:string",
     "winograd_q_138:string",
     "winograd_q_139:string",
     "winograd_q_14:string",
     "winograd_q_140:string",
     "winograd_q_141:string",
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================================================================================
FILE: ai_ready_score.json
ROLE: AI-readiness self-assessment
SIZE: 9,618 characters
--------------------------------------------------------------------------------
{
  "name": "AI-Ready Score for B2AI Voice: An ethically-sourced, diverse voice dataset linked to health information",
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      "details": "Dataset has DOI: https://doi.org/10.13026/k81f-qr68"
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    "interpretable": {
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      "details": "117 authors, Publisher: PhysioNet, PI: Yael Bensoussan"
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    "statistics": {
      "has_content": true,
      "details": "Total size: 12.9 GB"
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    "standards": {
      "has_content": true,
      "details": "55 schema(s) documented"
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      "has_content": true,
      "details": "Sampling bias: participants are recruited from specialty clinics and associated institutions using a non-probability sampling strategy, with inclusion focused on specific disease cohorts and fluent En..."
    },
    "data_quality": {
      "has_content": true,
      "details": "Phenotype tables include a row for a participant only when at least one variable is non-missing, and participants may have repeated visits with differing responses. Questionnaire completion varies by ..."
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      "has_content": true,
      "details": "Documentation is provided via the RO-Crate's structured JSON-LD metadata, this HTML Datasheet, and Croissant RAI properties."
    },
    "fit_for_purpose": {
      "has_content": true,
      "details": "Use cases: Development, training and fine-tuning of machine-learning models that associate voice-derived features with diagnostic categories or symptom severity for conditions such as vocal fold pathology, neurological and neurodegenerative diseases, mood and anxiety disorders and respiratory illnesses.\nBenchmarking and validation of existing voice-biomarker algorithms by testing their performance on a clinically diverse, multi-site cohort with standardized tasks and rich phenotype data.\nExploratory research on acoustic, phonetic, prosodic and articulatory correlates of disease using de-identified derived features, including work on representation learning, domain adaptation and multimodal integration with other health data.\nMethodological research on fairness, robustness and AI safety in clinical voice models, including studies of bias related to demographic subgroups or recording conditions, subject to the ethical constraints in the data use agreement.\nThe dataset is explicitly not intended for operational decision making about specific individuals such as hiring, insurance pricing, law enforcement or surveillance, nor for attempts at re-identification or for uses likely to stigmatize individuals or groups., Limitations: The feature-only release does not include raw audio waveforms or free-speech transcripts, which limits certain types of modeling and error analysis and may constrain the ability to reproduce end-to-end audio pipelines.\nThis version only includes an adult cohort; models trained solely on this dataset may not generalize to younger age groups or to languages beyond those represented.\nBecause of de-identification, some granular demographic and socio-economic variables, fine-grained location data and narrative context have been removed, which reduces the risk of re-identification but also limits detailed fairness assessments and certain confounder adjustments.\nThe dataset does not provide predefined train\u2013validation\u2013test splits or benchmarking tasks and does not include explicit per-instance label-uncertainty measures, so researchers must design their own evaluation protocols and handle label noise and missingness.\nThe registered-access and controlled-access governance structure is appropriate for privacy but may limit participation by some institutions or researchers and can complicate the reproducibility of pipelines that require both features and raw audio."
    },
    "verifiable": {
      "has_content": true,
      "details": "65% of files have checksums (11/17)"
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      "has_content": true,
      "details": "Data collection: Prospective observational study conducted at multiple specialty clinics and academic hospitals across North America. Eligible adults presenting to voice, neurology, psychiatry and respiratory clinics, plus healthy controls, were screened against predefined inclusion and exclusion criteria. After informed consent, a standardized protocol was administered that combined structured voice and respiratory tasks such as sustained vowels, coughs and reading passages with demographic questions, health history, disease-specific questionnaires and other patient-reported outcomes. Data were captured on a mobile or tablet application and stored in REDCap, with most participants completing a single in-clinic session and a subset completing repeated sessions., Human subject info: Yes"
    },
    "ethically_managed": {
      "has_content": true,
      "details": "Ethical review: Ethical Review by Vardit Ravitsky at the Hastings Center for Bioethics, Governance: Satrajit Ghosh"
    },
    "ethically_disseminated": {
      "has_content": true,
      "details": "License: https://physionet.org/content/b2ai-voice/view-license/3.0.0/, Sensitive info: ['The dataset encodes health-related information including diagnostic categories for voice disorders, neurological and neurodegenerative conditions, mood and psychiatric disorders and respiratory diseases, as well as symptom scores from questionnaires, which constitute sensitive personal health data.', 'Demographic variables such as age, sex and country of data collection are included in coarsened form, while finer-grained geographic identifiers, direct identifiers and many socio-economic and cultural details have been removed during de-identification to reduce re-identification risk.', 'Highly sensitive content including detailed narrative responses, some information about household income, traumatic life experiences and granular cultural identifiers has been removed entirely from this feature-only dataset; raw voice recordings, which are themselves biometric identifiers, are made available only under controlled access.', 'Use of the data is restricted to authorized researchers under a registered-access license and associated data use agreement that explicitly forbids attempts at re-identification, stigmatizing or discriminatory uses and applications such as surveillance or high-stakes individual decision making.']"
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      "details": "Confidentiality level: Limited dataset available with Data Use Agreement"
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      "details": "Dataset has DOI: https://doi.org/10.13026/k81f-qr68"
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    "domain_appropriate": {
      "has_content": true,
      "details": "Maintenance plan: Dataset releases follow a static versioning scheme managed through PhysioNet and related platforms, with version numbers such as 1.0, 1.1, 2.0.0, 2.0.1 and 3.0.0 and associated DOIs for each snapshot and for the latest version.\nReleases are coordinated by the Bridge2AI-Voice project team and the MIT Laboratory for Computational Physiology; release notes document added participants, new feature sets, reorganized phenotype tables and corrections such as spectrogram reprocessing and authorship updates.\nFuture updates are planned as additional participants are enrolled and Spanish-language protocols are incorporated; older versions remain accessible for reproducibility while users are encouraged to adopt the latest version."
    },
    "well_governed": {
      "has_content": true,
      "details": "Governance committee: Satrajit Ghosh"
    },
    "associated": {
      "has_content": true,
      "details": "All data, software, and computations are explicitly linked within the RO-Crate's provenance graph."
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      "details": "Formats: .py, text/tab-separated-values"
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    "computationally_accessible": {
      "has_content": true,
      "details": "Publisher: PhysioNet"
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      "details": "The dataset is packaged as a self-contained RO-Crate, a standard designed for portability across systems."
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    "contextualized": {
      "has_content": true,
      "details": "Context is provided by the RO-Crate's graph structure and detailed in properties such as rai:dataLimitations."
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