# VOICE Dataset Documentation

Datasheet for Dataset - Human Readable Format

🎯

## Motivation

Why was the dataset created?

Purposes

Description | Purpose Details
--- | ---
To integrate the use of voice as a biomarker of health in clinical care by generating a substantial ... | Voice is a promising biomarker as it is simple to collect, cost-effective, and has broad clinical utility, Recent AI advances enable extraction of prognostically useful information from voice data, ... (+2 more)
To develop new standards of acoustic and voice data collection and analysis for voice AI research, i... | Standardized voice data collection protocols across sites, Acoustic quality standardization and calibration, ... (+2 more)
To create software and cloud infrastructure for automated voice data collection through smartphone a... | Custom tablet/smartphone application for voice recording, Integrated acoustic quality standardization, ... (+2 more)

Funders

Description | Funder Name | Grant Info
--- | --- | ---
NIH Office of the Director, Bridge to Artificial Intelligence (Bridge2AI) program, grant 3OT2OD03272... | National Institutes of Health (NIH) | Grant number 3OT2OD032720-01S1 (current), 3OT2OD032720-01S3 (referenced), {'Administering IC': 'NIH Office of the Director'}, ... (+12 more)
Additional infrastructure support from National Institute of Biomedical Imaging and Bioengineering (... | NIBIB | Grant number R01EB030362, Supports PhysioNet infrastructure, MIT Laboratory for Computational Physiology

📊

## Composition

What do the instances represent?

Instances

Access | Count | Description | Format | Instance Type | Privacy Note
--- | --- | --- | --- | --- | ---
| 12523 | Voice and speech audio recordings from 306 participants across five clinical sites in North America,... | Derived features (spectrograms, MFCCs) in Parquet format | Audio recordings |
| 12523 | Spectrograms computed using short-time Fast Fourier Transform (FFT) with 25ms window size, 10ms hop ... | Parquet (spectrograms.parquet) | Spectrograms |
| 12523 | Mel-frequency cepstral coefficients (MFCCs) with 60 coefficients extracted from spectrograms, result... | Parquet (mfcc.parquet) | MFCCs |
|  | Acoustic features extracted using OpenSMILE (Speech and Music Interpretation by Large-space Extracti... | TSV (static_features.tsv) | Acoustic features |
|  | Phonetic and prosodic features computed using Parselmouth (Python interface to Praat), providing mea... | TSV (static_features.tsv) | Prosodic features |
| 306 | Demographic data from 306 participants including de-identified geographic information (country retai... | TSV (phenotype.tsv) | Demographics |
| 306 | Self-reported medical history questionnaires covering health status, disease history, medication use... | TSV (phenotype.tsv) | Medical history |
|  | Disease-specific validated questionnaires tailored to participant's disease cohort membership (voice... | TSV (phenotype.tsv) | Clinical questionnaires |
|  | Targeted questionnaires on known confounders for voice including smoking status, vocal use patterns,... | TSV (phenotype.tsv) | Voice confounders |
With participant consent |  | Electronic health record (EHR) data accessed with participant consent for gold standard validation o... |  | EHR data |
|  | Automated transcriptions generated using OpenAI's Whisper Large model. Free speech transcripts remov... |  | Transcriptions | Free speech transcripts removed for privacy

Subpopulations

Description | Subpopulation Type
--- | ---
Multi-institutional participants recruited from five clinical sites across North America to ensure g... | Geographic diversity
Disease cohort-based sampling targeting five categories with known voice manifestations: (1) Voice d... | Disease cohort stratification
Intentional recruitment of diverse participants to address historical underrepresentation in voice A... | DEI-focused recruitment

Distribution Formats

1. Description
     Parquet file containing spectrograms with participant_id, session_id, task_name, and 513xN dimension time-frequency representation arrays. Parquet format provides efficient columnar storage and fast queries.
   File Name
     spectrograms.parquet
   Format
     Parquet
   Structure
     Columnar with metadata and dense arrays
   Size
     Large (dense spectrogram data)
2. Description
     Parquet file containing 60xN dimension MFCC arrays derived from spectrograms. Added in version 1.1 release (January 17, 2025).
   File Name
     mfcc.parquet
   Format
     Parquet
   Structure
     Columnar with metadata and dense arrays
   Version Added
     1.1
3. Description
     Tab-delimited phenotype data with one row per unique participant (306 rows total), containing demographics, acoustic confounders, and responses to validated questionnaires. Accompanied by JSON data dictionary (phenotype.json) with column descriptions.
   File Name
     phenotype.tsv
   Format
     TSV (tab-separated values)
   Structure
     One row per participant
   Rows
     306
   Data Dictionary
     phenotype.json
4. Description
     Tab-delimited static features with one row per unique recording (12,523 rows total), containing features derived from OpenSMILE, Praat, parselmouth, and torchaudio. Accompanied by JSON data dictionary (static_features.json) with feature descriptions.
   File Name
     static_features.tsv
   Format
     TSV (tab-separated values)
   Structure
     One row per recording
   Rows
     12,523
   Data Dictionary
     static_features.json
5. Description
     Primary distribution through PhysioNet registered access system managed by MIT Laboratory for Computational Physiology, supported by NIBIB grant R01EB030362. Requires Data Access Compliance Office (DACO) approval with Data Transfer and Use Agreement (DTUA).
   Platform
     PhysioNet
   URL
     https://physionet.org/content/b2ai-voice/
   DOI V1 1
     https://doi.org/10.13026/249v-w155
   DOI Latest
     https://doi.org/10.13026/37yb-1t42
   Access Mechanism
     Registered access with DTUA
6. Description
     Secondary distribution through Health Data Nexus platform providing alternative access point for AI-ready biomedical datasets.
   Platform
     Health Data Nexus
   URL
     https://healthdatanexus.ai/content/b2ai-voice/1.0/
   Access Mechanism
     Registered access
7. Description
     Project documentation, protocols, and software tools available through official website and GitHub repositories under open-source licenses (MIT License for software).
   Platform
     Project website and GitHub
   URL Documentation
     https://docs.b2ai-voice.org
   URL Github Docs
     https://github.com/eipm/bridge2ai-docs
   URL Github B2aiprep
     https://github.com/sensein/b2aiprep
   License
     MIT License (software)
8. Description
     Raw audio data available through controlled access only by contacting Data Access Compliance Office (DACO@b2ai-voice.org). Raw audio waveforms disseminated with additional privacy protections to protect participant confidentiality.
   Platform
     Controlled access (DACO)
   Contact
     DACO@b2ai-voice.org
   Data Type
     Raw audio waveforms
   Privacy Level
     Enhanced (controlled access only)

🔍

## Collection Process

How was the data acquired?

ID *
bridge2ai-voice-dataset
Name
Bridge2AI-Voice
Title
Bridge2AI-Voice - An ethically-sourced, diverse voice dataset linked to health information
Description
The Bridge2AI-Voice dataset contains comprehensive voice, speech, and language data linked to health information, collected through a multi-institutional initiative funded by NIH's Bridge to Artificial Intelligence program. The dataset includes samples from conventional acoustic tasks such as respiratory sounds, cough sounds, and free speech prompts. Participants perform speaking tasks and complete self-reported demographic and medical history questionnaires, as well as disease-specific validated questionnaires. The project aims to integrate voice as a biomarker of health in clinical care by generating a substantial, ethically sourced, and diverse voice database linked to multimodal health biomarkers (EHR, radiomics, genomics) to fuel voice AI research and build predictive models for screening, diagnosis, and treatment across a broad range of diseases. Data collection is conducted via smartphone application linked to electronic health records, supported by federated learning technology to protect data privacy. Version 1.1 provides 12,523 recordings for 306 participants collected across five sites in North America. The dataset is distributed through PhysioNet and Health Data Nexus under a registered access license.
Page
https://physionet.org/content/b2ai-voice/
Language
en
Keywords

- voice
- speech
- bridge2ai
- voice biomarker
- acoustic biomarker
- AI
- machine learning
- health
- disease screening
- voice disorders
- neurological disorders
- mood disorders
- respiratory disorders
- pediatric
- PhysioNet
- federated learning
- ethical AI
- FAIR data
- CARE principles
- multimodal biomarkers

Addressing Gaps

Description | Existing Limitations | Gap Type
--- | --- | ---
Address the pressing need for large, high quality, multi-institutional and diverse voice databases l... | Previous literature used small datasets with limited demographic diversity reporting, Lack of standardized data collection protocols precluding meta-analysis, ... (+4 more) | Dataset availability
Address ethical concerns about patient privacy protection, fair representation of populations, and c... | Industry development lacks comprehensive ethical oversight, Privacy protection inadequate in commercial voice AI, ... (+3 more) | Ethical framework
Build bridges between the medical voice research world, acoustic engineers, and the AI/ML community ... | Siloed research communities, Limited clinical translation of voice AI research, ... (+2 more) | Interdisciplinary collaboration

Creators

Role | Name | ORCID | Affiliation
--- | --- | --- | ---
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Subsets

Cohort | Data Dictionary | Description | File Size | Format | ID | Name | Rows | Status | Version Added
--- | --- | --- | --- | --- | --- | --- | --- | --- | ---
|  | Parquet file (spectrograms.parquet) containing time-frequency representations with participant_id, s... | Large (dense array data) | Parquet | voice:spectrograms | Spectrograms |  |  |
|  | Parquet file (mfcc.parquet) containing 60xN dimension MFCC arrays derived from spectrograms. Added i... |  | Parquet | voice:mfcc | Mel-frequency Cepstral Coefficients |  |  | 1.1
| phenotype.json | Tab-delimited file (phenotype.tsv) with one row per unique participant (306 rows), containing demogr... |  | TSV | voice:phenotype | Phenotype Data | 306 |  |
| static_features.json | Tab-delimited file (static_features.tsv) containing features derived from raw audio using OpenSMILE,... |  | TSV | voice:static-features | Static Acoustic Features | 12523 |  |
Voice disorders |  | Participants with vocal pathologies including laryngeal cancers, vocal fold paralysis, and benign la... |  |  | voice:cohort-voice-disorders | Voice Disorders Cohort |  |  |
Neurological disorders |  | Participants with neurological and neurodegenerative conditions including Alzheimer's disease, Parki... |  |  | voice:cohort-neuro | Neurological Disorders Cohort |  |  |
Mood and psychiatric disorders |  | Participants with mood and psychiatric conditions including depression, schizophrenia, and bipolar d... |  |  | voice:cohort-mood | Mood and Psychiatric Disorders Cohort |  |  |
Respiratory disorders |  | Participants with respiratory conditions including pneumonia, COPD, heart failure, and obstructive s... |  |  | voice:cohort-respiratory | Respiratory Disorders Cohort |  |  |
Pediatric |  | Pediatric participants with conditions including autism and speech delay. Not included in version 1.... |  |  | voice:cohort-pediatric | Pediatric Cohort |  | Not included in v1.1 (adult cohort only) |

Sampling Strategies

Description | Rationale | Sampling Method
--- | --- | ---
Participants selected based on membership to five predetermined disease cohort groups identified fro... | Target conditions with established voice-disease associations and clinical unmet needs | Disease cohort-based selection
Multi-institutional recruitment across five sites in North America to ensure geographic diversity, s... | Generalizability and reduced site-specific bias | Multi-site geographic sampling
Intentional focus on recruiting diverse participants historically underrepresented in voice AI resea... | Fairness, representativeness, and reduction of algorithmic bias | Diversity-targeted recruitment
Patients screened at specialty clinics based on predetermined inclusion/exclusion criteria developed... | Clinical validity and gold standard diagnosis | Clinician-guided screening

Acquisition Methods

Collection Mode | Data Sources | Description | Equipment | Instruments | Was Directly Observed
--- | --- | --- | --- | --- | ---
In-person at clinical sites |  | Voice recordings collected using custom tablet application with headsets at clinical sites during sc... | Custom tablet application (REDCap-based v3.20.0), Headsets for audio capture with acoustic quality control, ... (+2 more) |  | True
Self-report via tablet application |  | Structured questionnaires administered via custom data collection application on tablets, capturing ... |  | Demographic questionnaires, Medical history questionnaires, ... (+2 more) | True
EHR data extraction with consent | Institutional EHR systems at participating sites, Diagnostic codes and clinical notes, ... (+2 more) | Electronic health record (EHR) data accessed through institutional platforms with participant consen... |  |  | False

Collection Mechanisms

Components | Description | Mechanism Type
--- | --- | ---
Tablets for application deployment, Headsets with acoustic specifications, ... (+2 more) | Hardware infrastructure including tablets with integrated headsets for standardized audio capture, a... | Hardware
REDCap v3.20.0 (doi:10.5281/zenodo.14148755), Custom tablet/smartphone application, ... (+7 more) | Software infrastructure including REDCap electronic data capture framework (v3.20.0), custom voice r... | Software
Institutional EHR APIs, Secure data linkage protocols, ... (+2 more) | EHR integration platforms enabling secure linkage between voice data and clinical records across par... | Data integration

Data Collectors

Collector Type | Description | Sites | Systems
--- | --- | --- | ---
Human - Clinical research staff | Clinical research coordinators and trained study personnel at participating sites responsible for pa... | University of South Florida, Massachusetts Institute of Technology, ... (+3 more) |
Automated - Computational pipelines | Automated computational systems for audio preprocessing, feature extraction, transcription, and qual... |  | b2aiprep preprocessing library, OpenSMILE acoustic feature extraction, ... (+4 more)

Collection Timeframes

- Description
    Project initiated September 1, 2022 with planned completion November 30, 2026. Ongoing data collection with periodic versioned releases.
  Start Date
    2022-09-01
  End Date
    2026-11-30
  Collection Status
    Ongoing
- Description
    Version release timeline: v1.0 released January 2024 (initial release with 306 participants, 12,523 recordings), v1.1 released January 17, 2025 (added MFCCs), v2.0.0 planned April 16, 2025, v2.0.1 planned August 18, 2025.
  Release Schedule
    - V1.0
        January 2024
    - V1.1
        January 17, 2025
    - V2.0.0
        April 16, 2025 (planned)
    - V2.0.1
        August 18, 2025 (planned)
- Description
    Most participants complete data collection in a single session. Subset of participants require multiple sessions to complete protocol, resulting in multiple sessions per participant for some individuals.
  Session Structure
    Single or multi-session per participant

Preprocessing Strategies

Description | Methods | Preprocessing Type | Privacy Measures | Tools
--- | --- | --- | --- | ---
Raw audio preprocessing pipeline standardizes all recordings to monaural (single-channel) format and... | Conversion to monaural (mono) audio, Resampling to 16 kHz, Butterworth anti-aliasing filter | Audio standardization |  |
Spectrogram extraction using short-time Fast Fourier Transform (FFT) with 25ms window size, 10ms hop... | Short-time FFT with 25ms window, 10ms hop length, ... (+3 more) | Time-frequency transformation |  |
Mel-frequency cepstral coefficient (MFCC) extraction with 60 coefficients computed from spectrograms... | 60 MFCC coefficients, Derived from spectrograms, ... (+2 more) | Perceptual feature extraction |  |
Acoustic feature extraction using OpenSMILE (Speech and Music Interpretation by Large-space Extracti... |  | Acoustic feature extraction |  | OpenSMILE (Eyben et al. 2010), LLD computation, ... (+2 more)
Phonetic and prosodic feature computation using Parselmouth (Python interface to Praat) for fundamen... |  | Prosodic analysis |  | Parselmouth (Jadoul et al. 2018), Praat phonetic analysis, ... (+3 more)
Automated speech transcription using OpenAI's Whisper Large model for accurate transcription of voic... |  | Automated transcription | Free speech transcripts removed, Only non-identifying task transcriptions retained | OpenAI Whisper Large model, Automatic speech recognition (ASR)
REDCap data export and conversion using open-source b2aiprep library (v0.21.0) for standardized extr... |  | Data export and formatting |  | b2aiprep v0.21.0 (https://github.com/sensein/b2aiprep), REDCap API integration, ... (+2 more)

Cleaning Strategies

Cleaning Type | Description | Identifiers Removed | Privacy Measures | Quality Measures
--- | --- | --- | --- | ---
HIPAA Safe Harbor de-identification | HIPAA Safe Harbor de-identification method applied systematically to remove all 18 categories of ide... | Names, Geographic subdivisions smaller than state (state/province removed, country retained), ... (+16 more) |  |
Privacy-preserving feature extraction | Raw audio waveforms excluded from public releases v1.0 and v1.1 to protect participant privacy and p... |  | Raw audio omitted from v1.0 and v1.1, Only derived features publicly released, ... (+2 more) |
Transcript privacy protection | Free speech transcripts removed from all public releases to prevent disclosure of potentially identi... |  | Free speech transcripts removed, Task-based transcriptions retained (non-identifying prompts only), Reduces re-identification risk from unique speech patterns |
Quality assurance | Data quality control procedures including acoustic quality validation, outlier detection, completene... |  |  | Acoustic quality thresholds, Outlier detection and flagging, ... (+3 more)

Maintainers

Grantor | Grant Name | Grant Number
--- | --- | ---
- | - | -
- | - | -
- | - | -
- | - | -

Governance Model

Role | Name | ORCID | Affiliation
--- | --- | --- | ---
Contributor |  | - | -
Contributor |  | - | -

Third Party Restrictions

Role | Name | ORCID | Affiliation
--- | --- | --- | ---
Contributor |  | - | -
Contributor |  | - | -
Contributor |  | - | -

Citation Requirements

Citation Type | Description | Format | Policy
--- | --- | --- | ---
Dataset citation | Primary dataset citation required for all publications, presentations, and other uses of data. Shoul... | Johnson, A., Bélisle-Pipon, J., Dorr, D., Ghosh, S., Payne, P., Powell, M., Rameau, A., Ravitsky, V.... |
Platform citation | PhysioNet platform citation required as standard acknowledgment of infrastructure supporting data di... | Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P. C., Mark, R., ... & Stanley, H. E. ... |
Attribution requirement | Recipient agrees to recognize contribution of Provider as source of data in all written, visual, or ... |  | Provider recognition required in all public disclosures
Publication encouragement | Recipients encouraged to make results publicly available in open-access journals or pre-print server... |  | Open-access publication encouraged

Project Specific Aims

Aim Number | Aim Title | Description
--- | --- | ---
1 | Data Acquisition Module | To build a multi-modal, multi-institutional, large scale, diverse and ethically sourced human voice ...
2 | Standard Module | To introduce the field of acoustic biomarkers by developing new standards of acoustic and voice data...
3 | Tool Development and Optimization | To develop a software and cloud infrastructure for automated voice data collection through a smartph...
4 | Ethics Module | To integrate existing scholarship, tools, and guidance with development of new standard and normativ...
5 | Teaming Module | To build bridges between the medical voice research world, the acoustic engineers, and the AI/ML wor...
6 | Skills and Workforce Development Module | To develop a unique curriculum on voice biomarkers of health and the development, validation, and im...

Related Publications

Citation
Rameau, A., Ghosh, S., Sigaras, A., Elemento, O., Belisle-Pipon, J.-C., Ravitsky, V., Powell, M., Jo...
Bensoussan, Y., Ghosh, S. S., Rameau, A., Boyer, M., Bahr, R., Watts, S., Rudzicz, F., Bolser, D., L...
Sigaras, A., Zisimopoulos, P., Tang, J., Bevers, I., Gallois, H., Bernier, A., Bensoussan, Y., Ghosh...
Johnson, A., Bélisle-Pipon, J., Dorr, D., Ghosh, S., Payne, P., Powell, M., Rameau, A., Ravitsky, V....

Software And Tools

Description | DOI | License | Name | Reference | Topics | URL | Version
--- | --- | --- | --- | --- | --- | --- | ---
Open source library for preprocessing raw audio waveforms and merging source data into phenotype fil... |  | Open source | b2aiprep |  |  | https://github.com/sensein/b2aiprep | 0.21.0
Custom REDCap configuration for voice data collection | https://doi.org/10.5281/zenodo.14148755 |  | Bridge2AI Voice REDCap |  |  |  | v3.20.0
Documentation dashboard and project documentation | https://zenodo.org/doi/10.5281/zenodo.13834653 | MIT License | bridge2ai-docs |  | ai, bridge2ai, ... (+3 more) | https://github.com/eipm/bridge2ai-docs | 2.0.5
The Munich Versatile and Fast Open-Source Audio Feature Extractor |  |  | OpenSMILE | Florian Eyben, Martin Wöllmer, Björn Schuller: "openSMILE - The Munich Versatile and Fast Open-Sourc... |  | https://audeering.github.io/opensmile/ |
Phonetic analysis software |  |  | Praat | Boersma P, Van Heuven V. Speak and unSpeak with PRAAT. Glot International. 2001 Nov;5(9/10):341-7. |  | http://www.praat.org/ |
Python interface to Praat for phonetic analysis |  |  | Parselmouth | Jadoul Y, Thompson B, De Boer B. Introducing parselmouth: A python interface to praat. Journal of Ph... |  | https://github.com/YannickJadoul/Parselmouth |
Audio processing library for PyTorch |  |  | TorchAudio | Yang, Y.-Y., Hira, M., Ni, Z., Chourdia, A., Astafurov, A., Chen, C., Yeh, C.-F., Puhrsch, C., Polla... |  | https://github.com/pytorch/audio |
Automatic speech recognition model (Large variant) |  |  | OpenAI Whisper |  |  | https://github.com/openai/whisper |

🚀

## Uses

What (other) tasks could the dataset be used for?

Tasks

Description | Target Applications | Target Populations | Task Type
--- | --- | --- | ---
Enable AI/ML research for disease screening, diagnosis, and treatment monitoring across five disease... |  | Adults with voice disorders, Adults with neurological/neurodegenerative conditions, ... (+3 more) | AI/ML model development
Discovery and validation of novel acoustic biomarkers associated with health conditions, expanding b... | Voice changes in depression (decreased fundamental frequency, monotonous speech), Voice changes in anxiety (increased fundamental frequency), ... (+3 more) |  | Biomarker discovery
Development of clinical decision support tools integrating voice biomarkers into healthcare workflow... | Point-of-care voice screening tools, Remote patient monitoring using voice, ... (+2 more) |  | Clinical application
Multi-modal biomarker research integrating voice with EHR, radiomics, genomics, and other data sourc... | Voice + EHR integration for diagnosis validation, Voice + genomics for personalized medicine, ... (+2 more) |  | Multi-modal integration

Existing Uses

- Description
    Dataset publicly released through PhysioNet and Health Data Nexus for voice AI research community access under registered access license. Initial research outputs include protocol development publication and open-source software tools.
  Publication References
    - Rameau A, et al. (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, et al. (2024) Bridge2AI Voice REDCap (v3.20.0). Zenodo, doi:10.5281/zenodo.14148755
    - Sigaras A, et al. (2024) eipm/bridge2ai-docs. Zenodo, doi:10.5281/zenodo.13834653
    - Johnson A, et al. (2024) Bridge2AI-Voice v1.0. Health Data Nexus, doi:10.57764/qb6h-em84

Future Use Impacts

Description | Impact Type | Potential Benefits | Potential Harms
--- | --- | --- | ---
Voice biomarker discovery for disease screening and diagnosis may enable earlier detection, non-inva... | Clinical decision support | Earlier disease detection through voice screening, Non-invasive monitoring tools, ... (+3 more) | False positive results causing unnecessary anxiety and interventions, False negative results delaying diagnosis and treatment, ... (+3 more)
Multi-modal AI model development integrating voice with EHR, genomics, and imaging data may provide ... | Multi-modal integration | Comprehensive patient phenotyping, Improved diagnostic accuracy through data fusion, ... (+2 more) | Increased re-identification risk from linked data, Privacy concerns about comprehensive patient profiles, ... (+2 more)
Federated learning applications may enable privacy-preserving collaborative research across institut... | Privacy-preserving collaboration | Multi-institutional model training without data sharing, Preservation of patient privacy, ... (+2 more) | Model inversion attacks extracting training data, Gradient leakage revealing patient information, ... (+2 more)
Commercial voice AI applications (e.g., smartphone-based screening) may increase accessibility but r... | Commercial applications | Consumer-accessible health monitoring, Scalable screening tools, ... (+2 more) | Data exploitation for profit, Biometric surveillance concerns, ... (+3 more)

Intended Uses

Description | Use Case
--- | ---
Development and validation of AI/ML models for voice-based disease screening, diagnosis, and monitor... | AI/ML model development
Discovery and validation of novel acoustic biomarkers associated with health conditions not previous... | Biomarker discovery
Development of clinical decision support tools integrating voice biomarkers into healthcare workflow... | Clinical decision support
Multi-modal biomarker research integrating voice with EHR, radiomics, genomics, and other data sourc... | Multi-modal data integration
Federated learning applications for privacy-preserving collaborative research across institutions, e... | Federated learning research
Development of standards, best practices, and quality measures for acoustic and voice data collectio... | Standards development
Education and training of interdisciplinary researchers in voice biomarkers, AI/ML methods, and ethi... | Workforce development

Discouraged Uses

Role | Name | ORCID | Affiliation
--- | --- | --- | ---
Contributor |  | - | -
Contributor |  | - | -
Contributor |  | - | -
Contributor |  | - | -
Contributor |  | - | -

License And Use Terms

Description
  Bridge2AI Voice Registered Access License with Data Transfer and Use Agreement (DTUA) required for all data access. Registered users must sign DTUA and obtain approval from Data Access Compliance Office (DACO) before accessing files. Recipients must establish administrative, technical, and physical safeguards to protect Personally Identifiable Information (PII) per OMB M-07-16 and ensure only authorized persons access data. Data provided "AS IS" without warranties of any kind. Recipients assume all liability for use, storage, disclosure, or disposal. No unauthorized disclosure to third parties; collaborators must apply independently. Attribution required citing both dataset DOI and PhysioNet platform. Commercial use allowed under DTUA terms. Recipients may retain derivative works with proper attribution and may publish results (open-access encouraged). Two-year use period from DTUA start date upon completion of project, expiration of ethics approval, or termination, whichever occurs first; renewable with Provider approval. One archival copy allowed for records retention compliance. Provider Institution (University of South Florida) may unilaterally amend if Federal sponsor requires; recipient may object resulting in immediate termination. Certificate of Confidentiality protections apply and must be asserted against compulsory legal demands. DTUA approved for use through August 31, 2025.
License Name
  Bridge2AI Voice Registered Access License
Agreement Required
  Data Transfer and Use Agreement (DTUA)
Approval Authority
  Data Access Compliance Office (DACO)
Provider Institution
  University of South Florida Board of Trustees
Effective Through
  August 31, 2025
Key Terms
  - Registered access with DACO approval required
  - Data classified as Personally Identifiable Information (PII, OMB M-07-16)
  - Administrative, technical, physical safeguards required
  - Certificate of Confidentiality protections (must assert against legal demands)
  - Data provided "AS IS" without warranties
  - Recipients assume liability for use
  - No unauthorized third-party disclosure
  - Collaborators apply independently
  - Attribution requirements (dataset DOI + PhysioNet)
  - Commercial use allowed
  - Open-access publication encouraged
  - Two-year use period (renewable)
  - Archival copy allowed for records retention
  - Provider may amend if Federal sponsor requires
  - Termination results in data destruction (certification required)

📤

## Distribution

How will the dataset be distributed?

License
Bridge2AI Voice Registered Access License
Version Access

Description
  All versions available through PhysioNet with version-specific DOIs for citability and reproducibility. Latest version DOI always points to most recent release. Users can access specific versions for replication or access latest version for most current data.
Version DOI 1 0
  https://doi.org/10.57764/qb6h-em84
Version DOI 1 1
  https://doi.org/10.13026/249v-w155
Latest DOI
  https://doi.org/10.13026/37yb-1t42
Current Latest Version
  2.0.1

🔄

## Maintenance

How will the dataset be maintained?

Version
1.1
Updates

Anticipated Changes | Changes | Description | DOI | Platform | Release Date | Status | Version
--- | --- | --- | --- | --- | --- | --- | ---
| Initial public release, 12,523 recordings, 306 participants, ... (+6 more) | Initial release of Bridge2AI-Voice dataset with 12,523 recordings from 306 participants across five ... | https://doi.org/10.57764/qb6h-em84 | Health Data Nexus | January 2024 |  | 1.0
| Added mfcc.parquet file, 60 MFCC coefficients (60xN dimension), Derived from existing spectrograms | Added Mel-frequency cepstral coefficients (MFCCs) with 60 coefficients per recording, providing addi... | https://doi.org/10.13026/249v-w155 | PhysioNet | January 17, 2025 |  | 1.1
Additional participants, Expanded disease cohorts, ... (+2 more) |  | Planned future release with additional participants, enhanced features, and expanded cohorts. Detail... |  |  | April 16, 2025 (planned) | Planned | 2.0.0
Bug fixes and corrections, Documentation improvements, Minor feature enhancements |  | Planned maintenance release with bug fixes, documentation updates, and minor enhancements. Currently... |  |  | August 18, 2025 (planned) | Planned (latest version) | 2.0.1

Update Frequency
Ongoing data collection with periodic versioned releases. Major releases planned approximately every 6-12 months during active project period (2022-2026). Future releases will include additional participants from adult cohorts, pediatric cohort data (currently not included), and potentially raw audio waveforms with enhanced privacy protections. Post-project maintenance will continue through PhysioNet infrastructure with updates as needed for corrections, documentation, and community contributions.
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## Human Subjects

Does the dataset relate to people?

Privacy Controls

Role | Name | ORCID | Affiliation
--- | --- | --- | ---
Contributor |  | - | -
Contributor |  | - | -
Contributor |  | - | -
Contributor |  | - | -

Generated on 2025-12-09 18:07:08 using Bridge2AI Data Sheets Schema