# VOICE d4d alldocs

Datasheet for Dataset - Human Readable Format

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## Collection Process

How was the data acquired?

ID *
https://docs.b2ai-voice.org
Name
Bridge2AI-Voice
Title
Bridge2AI-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. Bridge2AI-Voice provides a comprehensive collection of derived data from voice recordings with corresponding clinical information, demographic data, and validated questionnaires collected across multiple North American sites. Initial releases focus on low-risk, de-identified derived data (e.g., spectrograms, acoustic features) and phenotype data to reduce re-identification risk while enabling broad research utility.
Page
https://docs.b2ai-voice.org
Keywords

- voice
- bridge2ai
- PhysioNet
- Health Data Nexus

Created By

- Alistair Johnson
- Jean-Christophe Bélisle-Pipon
- David Dorr
- Satrajit Ghosh
- Philip Payne
- Maria Powell
- Anais Rameau
- Vardit Ravitsky
- Alexandros Sigaras
- Olivier Elemento
- Yael Bensoussan
- Bridge2AI-Voice Consortium
- PhysioNet

Resources

1. ID
     physionet-b2ai-voice-1.1
   Name
     Bridge2AI-Voice v1.1 (PhysioNet)
   Title
     Bridge2AI-Voice: An ethically-sourced, diverse voice dataset linked to health information (version 1.1)
   Description
     Derived audio representations (e.g., spectrograms, MFCCs, acoustic and phonetic/prosodic features) and associated phenotype and questionnaire data from adult participants recruited at specialty clinics across five North American sites. Raw audio is not included in this release to reduce re-identification risk. Common identifiers include participant_id, session_id, and task_name.
   DOI
     doi:10.13026/249v-w155
   Issued
     2025-01-17
   Version
     1.1
   Keywords
     - voice
     - bridge2ai
     - PhysioNet
     - RRID:SCR_007345
   License
     Bridge2AI Voice Registered Access License
   Created By
     - PhysioNet
     - Bridge2AI-Voice Consortium
   Purposes
     - Name
         primary-purpose
       Response
         Enable ethically sourced, large-scale research on voice as a biomarker of health by linking derived voice representations to demographic, clinical, and questionnaire data.
   Tasks

     Name | Response
     --- | ---
     example-task-1 | Development and benchmarking of models associating voice-derived features with health conditions.
     example-task-2 | Exploration of acoustic, phonetic, and prosodic correlates of disease using de-identified derived da...

   Addressing Gaps
     - Name
         gap-addressed
       Response
         Lack of an ethically sourced, clinically linked, multi-site voice dataset with robust de-identification suitable for AI research.
   Funders

     Grantor | Grant Name | Grant Number
     --- | --- | ---
     National Institutes of Health | Bridge2AI: Voice as a Biomarker of Health | 3OT2OD032720-01S1
     National Institute of Biomedical Imaging and Bioengineering (NIBIB), NIH | PhysioNet infrastructure support | R01EB030362

   Instances
     1. Name
          dataset-instances
        Representation
          Derived representations of voice recordings linked to participant-level phenotype and questionnaire data.
        Instance Type
          Participants and their recordings; per-recording static feature rows and per-participant phenotype rows.
        Data Type
          De-identified derived features (spectrograms, MFCCs, acoustic and phonetic/prosodic features) and structured phenotype/questionnaire data.
        Counts
          12,523
        Label
          Clinical and demographic attributes (e.g., condition groups, validated questionnaires) are available as metadata; no explicit machine-learning labels are defined in this release.
        Sampling Strategies
          1. Name
               purposive-sampling
             Is Sample
               - True
             Is Random
               - False
             Source Data
               - Adult patients recruited at specialty clinics across five North American sites
             Is Representative
               - False
             Why Not Representative
               - Participants were selected based on conditions known to manifest in voice, which may affect generalizability.
             Strategies
               - Purposive sampling by predefined condition groups
        Missing Information
          - Name
              privacy-removals
            Missing
              - Free speech transcripts
              - Raw audio waveforms
            Why Missing
              - Removed to reduce re-identification risk and comply with HIPAA Safe Harbor.
   Subpopulations
     - Name
         adult-cohort
       Identification
         - Adults only in v1.1
       Distribution
         - 306 participants; 12,523 recordings across predefined clinical condition groups
   Sensitive Elements
     - Name
         health-data
       Description
         - Contains de-identified health-related data (clinical and questionnaire information).
   Is Deidentified
     Name
       hipaa-safe-harbor
     Description
       - HIPAA Safe Harbor de-identification applied; removal of identifiers (e.g., names, fine-grained dates, contact details, geographic locators below country), removal of free speech transcripts, and omission of raw audio in v1.1.
   Acquisition Methods
     1. Name
          acquisition-overview
        Description
          - Voice recordings directly observed; phenotype/questionnaire data reported by participants; derived features computed from recordings.
        Was Directly Observed
          True
        Was Reported By Subjects
          True
        Was Inferred Derived
          True
        Was Validated Verified
          Standardized protocols and multi-site QA were used; derived features computed via established toolkits.
   Collection Mechanisms
     - Name
         data-capture
       Description
         - Custom tablet application and headset microphones used when possible; data exported from REDCap and converted using an open-source library.
   Data Collectors
     - Name
         site-teams
       Description
         - Researchers and clinicians at five North American specialty-clinic sites.
   Collection Timeframes
   Ethical Reviews
     - Name
         irb-approval
       Description
         - Data collection and sharing approved by the University of South Florida Institutional Review Board.
   Preprocessing Strategies
     - Name
         waveform-prep-and-feature-extraction
       Description
         - Waveforms converted to mono and resampled to 16 kHz with anti-aliasing; spectrograms via STFT (25 ms window, 10 ms hop, 512-point FFT, power); 60-coefficient MFCCs computed; static acoustic features extracted (e.g., openSMILE) and phonetic/prosodic features via Parselmouth/Praat; transcripts generated by OpenAI Whisper Large (free speech transcripts removed prior to release).
       Used Software

         Description | Name | URL
         --- | --- | ---
         Open-source library used to preprocess waveforms and merge phenotype data. | b2aiprep | https://github.com/sensein/b2aiprep
         Acoustic feature extraction toolkit. | openSMILE |
         Speech analysis software for phonetics. | Praat |
         Python interface to Praat. | Parselmouth |
         Audio processing components for PyTorch. | torchaudio |
         ASR model used to generate transcripts (free speech transcripts removed before release). | OpenAI Whisper Large |

   Cleaning Strategies
     - Name
         de-identification-and-redactions
       Description
         - Removal of HIPAA Safe Harbor identifiers; removal of state/province with retention of country; removal of free speech transcripts; omission of raw audio waveforms in v1.1.
   Labeling Strategies
     - Name
         transcription
       Description
         - Automatic speech recognition using OpenAI Whisper Large; transcripts of free speech removed prior to release.
   Raw Sources
     - Name
         raw-audio-availability
       Description
         - Raw audio waveforms were collected but are not distributed in v1.1; only derived representations are provided.
   Existing Uses

     Description | Name
     --- | ---
     {'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'} | dataset-citation
     Goldberger, A., et al. (2000). PhysioBank, PhysioToolkit, and PhysioNet. Circulation. RRID:SCR_007345. | platform-citation

   Use Repository
     - Name
         project-and-platform-links
       Description
         - Project Documentation Site
             https://docs.b2ai-voice.org
         - DOI Landing For V1.1 On Physionet
             https://doi.org/10.13026/249v-w155
   Other Tasks
   Future Use Impacts
     - Name
         derived-only-constraints
       Description
         - The absence of raw audio may limit certain analyses (e.g., new feature extraction requiring original waveforms) but reduces re-identification risk.
   Discouraged Uses
   Distribution Formats
     - Name
         formats
       Description
         - Parquet
         - TSV
         - JSON
   Distribution Dates
     - Name
         initial-release
       Description
         - 2025-01-17
   License And Use Terms
     Name
       access-and-licensing
     Description
       - Platform
           PhysioNet
       - Access
           Registered/Restricted Access; only registered users who sign the Bridge2AI Voice Registered Access Agreement may access files.
       - License
           Bridge2AI Voice Registered Access License; applicable Data Use Agreement required.
   Ip Restrictions
   Regulatory Restrictions
   Maintainers
     - Name
         maintainers
       Description
         - PhysioNet platform team
         - Bridge2AI-Voice consortium
   Errata
   Updates
     Name
       version-history
     Description
       - V1.0 (2024)
           Initial release.
       - V1.1 (2025 01 17)
           Added MFCCs; files for v1.1 are no longer available on the platform.
       - Newer Versions Available
           2.0.0 (2025-04-16), 2.0.1 (2025-08-18).
   Retention Limit
   Version Access
     Name
       older-version-availability
     Description
       - Older version 1.1 files are no longer available; latest version on the platform is 2.0.1.
   Extension Mechanism
   Is Tabular
     yes
2. ID
     healthdatanexus-voice-1.0
   Name
     Health Data Nexus VOICE 1.0
   Title
     VOICE 1.0
   Description
     Resource page for the VOICE 1.0 project on Health Data Nexus.
   Page
     https://healthdatanexus.ai/content/b2ai-voice/1.0/
   Version
     1.0
   DOI
     doi:10.57764/qb6h-em84
   Keywords
     - VOICE
     - Health Data Nexus
     - b2ai-voice
     - healthdatanexus.ai
   Created By
     - Health Data Nexus
   Distribution Formats
   Distribution Dates
   License And Use Terms
     Name
       unspecified-license
     Description
       - Resource/landing page for the VOICE 1.0 project; licensing for downloadable data not specified on this page.

Generated on 2025-11-09 10:17:35 using Bridge2AI Data Sheets Schema