# healthnexus tab acknowledgements d4d

Datasheet for Dataset - Human Readable Format

🎯

## Motivation

Why was the dataset created?

Funders

Grantor | Grant Name | Grant Number
--- | --- | ---
National Institutes of Health (NIH) | Bridge2AI: Voice as a Biomarker of Health - Building an ethically sourced, bioaccoustic database to understand disease like never before | 3OT2OD032720-01S1

Purposes

- Response
    Create an ethically sourced, diverse, multi-institutional voice dataset linked to health information to enable AI research on voice as a biomarker of health.

📊

## Composition

What do the instances represent?

Instances

Counts | Data Type | Instance Type | Representation | Sampling Strategies
--- | --- | --- | --- | ---
12523 | Derived spectrograms (513 x N), static acoustic/phonetic features (OpenSMILE, Praat, Parselmouth, to... | Audio recordings and sessions (time-frequency representations and derived features; raw waveforms om... | Voice-derived recordings | {'strategies': ['Participants prospectively selected into 5 predetermined clinical groups (Respiratory, Voice, Neurological, Mood/Psychiatric, Pediatric); v1.0 includes adult cohort only.'], 'source_data': ['Five clinical sites in North America'], 'is_representative': ['No; targeted clinical cohorts'], 'why_not_representative': ['Cohorts intentionally sampled for disorders with known voice manifestations; not a population-representative sample.']}
306 | Demographics, validated health questionnaires, acoustic confounders, and disease-specific clinical i... | Individuals (adult cohort in v1.0) | Participants |

Subpopulations

- Identification
    - Adult cohort only in v1.0
    - Disorder Cohorts
        Voice, Neurological/Neurodegenerative, Mood/Psychiatric, Respiratory, Pediatric (pediatric not included in v1.0)
  Distribution
    - 306 adult participants across five North American sites
    - Participants selected based on membership in predefined clinical cohorts

Distribution Formats

- Description
    - spectrograms.parquet (Parquet; time-frequency representations per recording)
    - static_features.tsv (tab-delimited; one row per recording)
    - static_features.json (data dictionary for features)
    - phenotype.tsv (tab-delimited; one row per participant)
    - phenotype.json (data dictionary for phenotype)

Distribution Dates

- Description
    - First Public Release (v1.0)
        2024-11-27

🔍

## Collection Process

How was the data acquired?

ID *
doi:10.57764/qb6h-em84
Name
Bridge2AI-Voice
Title
Bridge2AI-Voice: An ethically-sourced, diverse voice dataset linked to health information
Description
Bridge2AI-Voice is a comprehensive, ethically sourced dataset to enable research on the human voice as a biomarker of health. Version 1.0 provides 12,523 recordings for 306 adult participants collected across five sites in North America, selected based on conditions known to manifest in the voice waveform (voice disorders, neurological/neurodegenerative disorders, mood and psychiatric disorders, and respiratory disorders). This initial release contains low-risk derived data (e.g., spectrograms and acoustic/phonetic features) and detailed demographic, clinical, and validated questionnaire data. Original audio waveforms are not included in v1.0. Standardized collection protocols, de-identification under HIPAA Safe Harbor, and data access via registered, credentialed workflows are used to protect participants and enable responsible research.
Issued
2024-11-27
Page
https://docs.b2ai-voice.org
Keywords

- voice
- bridge2ai
- audio

Creators

Role | Name | ORCID | Affiliation
--- | --- | --- | ---
Principal Investigator | Alistair Johnson | - | -
Principal Investigator | Jean-Christophe Bélisle-Pipon | - | -
Principal Investigator | David Dorr | - | -
Principal Investigator | Satrajit Ghosh | - | -
Principal Investigator | Philip Payne | - | -
Principal Investigator | Maria Powell | - | -
Principal Investigator | Anaïs Rameau | - | -
Principal Investigator | Vardit Ravitsky | - | -
Principal Investigator | Alexandros Sigaras | - | -
Principal Investigator | Olivier Elemento | - | -
Principal Investigator | Yael Bensoussan | - | -

Addressing Gaps

- Response
    Address the lack of large, standardized, demographically diverse, multi-institutional voice datasets linked to health biomarkers to improve external validity and clinical utility in voice AI research.

Sampling Strategies

1. Strategies
     - Targeted, multi-site clinical recruitment into 5 disorder cohort categories; adult cohort only in v1.0.
   Source Data
     - Specialty clinics across five North American sites
   Is Representative
     - No; targeted clinical cohorts
   Why Not Representative
     - Designed to cover diverse voice-related conditions rather than represent the general population.

Acquisition Methods

1. Description
     - Data directly observed via standardized voice recording tasks (e.g., sustained vowel phonation) plus participant-reported questionnaires and clinical data; derived features computed from raw audio.
   Was Directly Observed
     yes
   Was Reported By Subjects
     yes
   Was Inferred Derived
     yes
   Was Validated Verified
     yes

Collection Mechanisms

- Description
    - Standardized protocol using a custom tablet application; headset used for data collection when possible; data exported from REDCap using an open-source library; multiple sessions for some participants as needed.

Data Collectors

- Description
    - Project investigators at specialty clinics across five North American sites

Ethical Reviews

- Description
    - Data collection and sharing approved by the University of South Florida Institutional Review Board; submitted for review to the University of Toronto Research Ethics Board.

Preprocessing Strategies

- Description
    - Raw audio converted to monaural and resampled to 16 kHz with a Butterworth anti-aliasing filter.
    - Spectrograms computed via short-time FFT (25 ms window, 10 ms hop, 512-point FFT).
    - Acoustic features extracted with OpenSMILE; phonetic/prosodic features computed with Parselmouth and Praat; additional features via torchaudio.
  Used Software

    Name | URL
    --- | ---
    openSMILE | https://audeering.github.io/opensmile/
    Praat | https://www.fon.hum.uva.nl/praat/
    Parselmouth | https://parselmouth.readthedocs.io/
    torchaudio | https://pytorch.org/audio
    b2aiprep | https://github.com/sensein/b2aiprep

Cleaning Strategies

- Description
    - HIPAA Safe Harbor identifiers removed.
    - State and province removed; country of data collection retained.
    - Transcripts of free speech audio removed to reduce re-identification risk.

Labeling Strategies

- Description
    - Machine-generated transcriptions produced using OpenAI Whisper Large model for certain tasks.
  Used Software
    - Name
        OpenAI Whisper
      URL
        https://github.com/openai/whisper

Raw Sources

- Description
    - Original audio waveforms were collected but are omitted from v1.0 release; only spectrograms and other derived features are provided.

External Resources

- External Resources
    - Project Documentation Website
        https://docs.b2ai-voice.org
    - Bridge2AI Voice REDCap (v3.20.0) reference: "https://doi.org/10.5281/zenodo.14148755"
    - B2aiprep Preprocessing Library
        https://github.com/sensein/b2aiprep
  Future Guarantees
    - Versioned DOIs provided (version-specific and latest) for discoverability.
  Archival
    - Versioned DOI For V1.0
        https://doi.org/10.57764/qb6h-em84
    - Latest DOI
        https://doi.org/10.57764/3sg0-7440

Confidential Elements

- Description
    - Clinical information and validated questionnaire responses collected during visits.

Sensitive Elements

- Description
    - Health-related data linked to voice-derived features.
    - Voice as a potential biometric/biobehavioral marker.

Is Deidentified

Description
  - HIPAA Safe Harbor identifiers removed.
  - State and province removed; country retained.
  - Free speech transcripts removed.
  - Original audio waveforms omitted from v1.0.

Third Party Sharing

Description
  Dataset is available to external, credentialed users under registered access terms (DUA and required training).

Maintainers

- Description
    - Health Data Nexus (Temerty Centre for AI Research and Education in Medicine)
    - Supported by the Temerty Foundation

🚀

## Uses

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

Tasks

- Response
    Support AI methods development and clinical research using derived voice representations (e.g., spectrograms and acoustic features) linked with demographic, clinical, and questionnaire data across targeted disorder cohorts.

License And Use Terms

Description
  - License
      Bridge2AI Voice Registered Access License.
  - Access Policy
      Only credentialed users who sign the DUA can access files.
  - Data Use Agreement
      Bridge2AI Voice Registered Access Agreement.
  - Required Training
      TCPS 2: CORE 2022.
  - Access provided via Health Data Nexus credentialed workflow.

📤

## Distribution

How will the dataset be distributed?

DOI
doi:10.57764/qb6h-em84
License
Bridge2AI Voice Registered Access License
Version Access

Description
  - Version Specific DOI (v1.0)
      https://doi.org/10.57764/qb6h-em84
  - Latest DOI
      https://doi.org/10.57764/3sg0-7440
  - Older versions discoverable via DOI versioning; documentation website will communicate updates and changes.

🔄

## Maintenance

How will the dataset be maintained?

Version
1.0
Updates

Description
  - Future releases aim to include original voice waveforms with additional security safeguards; updates and documentation via https://docs.b2ai-voice.org.

Generated on 2025-11-09 10:17:34 using Bridge2AI Data Sheets Schema