Consensus-Based Definitions for Vocal Biomarkers: The International VOCAL Initiative.

Introduction: Voice-based health technologies are growing rapidly, but they lack standardized terminology, which hinders interdisciplinary collaboration, research quality, and clinical translation. The objective of this work was to develop universally accepted definitions in the rapidly evolving field of vocal biomarkers, as part of the VOCAL (Vocal Biomarker Guidelines for Ontology, Classification, Application, and Logistics) initiative, a structured, international consensus-based framework that aims to provide standards and guidelines. Methods: VOCAL is a rigorous, international, multistage consensus-building study conducted in 2024-2025. It is a multi-institutional collaboration between representatives from the Bridge2AI-Voice Consortium (North America) and the eVoiceNet Network (European Union), involving a group of 24 international experts in medicine, clinical research, speech and language, audio signal processing, statistics, methodology, regulation, and ethics. VOCAL's iterative process involved 5 rounds of review and feedback and an in-person workshop at the 2025 Bridge2AI Voice Symposium. Results: Consensus-based definitions for vocal biomarkers were developed, spanning from broad concepts to domain-specific measures. A hierarchical continuum model of vocal biomarkers was established. We first distinguished between the concepts of vocal measures and vocal biomarkers. We then defined terms from broad, overarching concepts (level 0: biomarker, digital biomarker, vocal biomarker) to more specific physiological and cognitive domains (level 1: cardio-respiratory acoustic; level 2: voice; level 3: speech/articulatory; level 4: cognitive/language, including linguistic and paralinguistic subtypes). Conclusion: This work provides a shared vocabulary that is essential for fostering communication through interdisciplinary collaboration, improving the quality and efficiency of research and development, and ensuring the ethical, reliable, and scalable deployment of future voice-based health technologies. It lays foundational groundwork for upcoming guidelines and standards, which are crucial for advancing the field of vocal biomarkers into widespread clinical utility.

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PubMed
Published
2026-09-18
DOI
https://doi.org/10.1159/000553327
Primary Topic
Voice and Speech Disorders
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article
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Consensus-Based Definitions for Vocal Biomarkers: The International VOCAL Initiative.

Jaskanwal Deep Singh Sara, Stephanie Watts, Yaël Bensoussan, Steven Bedrick et al.
PubMed
Voice and Speech Disorders
article

Consensus-Based Definitions for Vocal Biomarkers: The International VOCAL Initiative.

Jaskanwal Deep Singh Sara, Stephanie Watts, Yaël Bensoussan, Steven Bedrick, Abir Elbéji, Lampros Kourtis, Mégane Pizzimenti, Ruth Huntley Bahr, Arian Azarang, Nicholas Cummins, Sybille Barvaux, James Anibal, Jamie Toghranegar, Oita C Coleman, Ayush Kalia, Mohamed Ebraheem, Jean-Christophe Bélisle-Pipon, Daria Hemmerling, Guy Fagherazzi, Jiri Mekyska, Anaïs Rameau, Rhoda Au, Marisha L Speights, Hugo Botha, Satrajit S Ghosh
article en

Abstract

Introduction: Voice-based health technologies are growing rapidly, but they lack standardized terminology, which hinders interdisciplinary collaboration, research quality, and clinical translation. The objective of this work was to develop universally accepted definitions in the rapidly evolving field of vocal biomarkers, as part of the VOCAL (Vocal Biomarker Guidelines for Ontology, Classification, Application, and Logistics) initiative, a structured, international consensus-based framework that aims to provide standards and guidelines. Methods: VOCAL is a rigorous, international, multistage consensus-building study conducted in 2024-2025. It is a multi-institutional collaboration between representatives from the Bridge2AI-Voice Consortium (North America) and the eVoiceNet Network (European Union), involving a group of 24 international experts in medicine, clinical research, speech and language, audio signal processing, statistics, methodology, regulation, and ethics. VOCAL's iterative process involved 5 rounds of review and feedback and an in-person workshop at the 2025 Bridge2AI Voice Symposium. Results: Consensus-based definitions for vocal biomarkers were developed, spanning from broad concepts to domain-specific measures. A hierarchical continuum model of vocal biomarkers was established. We first distinguished between the concepts of vocal measures and vocal biomarkers. We then defined terms from broad, overarching concepts (level 0: biomarker, digital biomarker, vocal biomarker) to more specific physiological and cognitive domains (level 1: cardio-respiratory acoustic; level 2: voice; level 3: speech/articulatory; level 4: cognitive/language, including linguistic and paralinguistic subtypes). Conclusion: This work provides a shared vocabulary that is essential for fostering communication through interdisciplinary collaboration, improving the quality and efficiency of research and development, and ensuring the ethical, reliable, and scalable deployment of future voice-based health technologies. It lays foundational groundwork for upcoming guidelines and standards, which are crucial for advancing the field of vocal biomarkers into widespread clinical utility.

PubMedVol. 10(1)
Boston University (US), Northwestern University (US), University of North Carolina at Chapel Hill (US), Central European Institute of Technology (CZ), Abbott Northwestern Hospital (US), Alzheimer's Drug Discovery Foundation (US), McGovern Institute for Brain Research (US), Oregon Health & Science University (US), Simon Fraser University (CA), King's College London (GB), Cornell University (US), University of South Florida (US), Yale University (US), University of Oxford (GB), Central European Institute of Technology – Masaryk University (CZ), Luxembourg Institute of Health (LU), Open Society (CZ), Framingham Heart Study (US), Mayo Clinic in Arizona (US), Minneapolis Heart Institute Foundation (US), National Institutes of Health Clinical Center (US), Brno University of Technology (CZ), Massachusetts Institute of Technology (US), AGH University of Krakow (PL)
Partnerships for the goals
Openalex Percentile: Top 34%
Voice and Speech Disorders
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