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The world of voice biomarkers is rapidly evolving thanks to the use of artificial intelligence (AI) allowing large-scale analysis of voice, speech, and respiratory sound data. The Bridge2AI-Voice project aims to build a large-scale, ethically sourced, and diverse voice database of human voices linked to health information to help fuel Voice AI research, dubbed Audiomics. The current paper describes the development of protocols of data acquisition across 4 different adult cohorts of disease (voice, respiratory, neurodegenerative diseases, mood, and anxiety disorders) using a Team Science approach for broader adoption by the research community and feedback. Demographic Surveys, Confounders Assessments, Acoustic tasks, validated patient-reported outcome (PRO) questionnaires and clinician-validated diagnostic questions were grouped in a common PART A across all cohorts and individual PART B, with cohort-specific tasks.
Bensoussan et al. (Sun,) studied this question.