Ayush Kalia

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 is 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, multi-stage 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 five 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.

Importance: Voice-based health technologies are growing rapidly, but they lack standardized terminology, which hinders interdisciplinary collaboration, research quality, and clinical translation. Objective: The objective of this work is to develop universally accepted definitions in the rapidly evolving field of vocal biomarkers, as part of the VOCAL ( V ocal Biomarker Guidelines for O ntology, C lassification, A pplication, and L ogistics) initiative, a structured, international consensus-based framework that aims to provide standards, and guidelines. Design: VOCAL is a rigorous, international, multi-stage consensus-building study conducted in 2024-2025. Setting: Multi-institutional collaboration between representatives from the Bridge2AI-Voice Consortium (North America) and the eVoiceNet Network (European Union), culminating in an in-person workshop at the 2025 Bridge2AI Voice Symposium. Participants: A group of 24 international experts in medicine, clinical research, speech and language, audio signal processing, statistics, methodology, regulation, ethics. Methods: VOCAL’s iterative process involved five rounds of review, feedback, and an in-person workshop at the international 2025 Bridge2AI Voice Symposium, ensuring the incorporation of diverse perspectives and achieving a robust agreement on the proposed definitions. Main outcomes and measures: Consensus-based definitions for vocal biomarkers, spanning from broad concepts (biomarker, digital biomarker, vocal biomarker) to domain-specific measures (cardio-respiratory acoustic, voice, speech/articulatory, cognitive/language). Results: 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). Conclusions and Relevance: 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. Key points: Question: Can we establish an international consensus on the definitions related to vocal biomarkers? Findings: Through a multi-stage international consensus process involving expert representatives from the Bridge2AI-Voice Consortium and eVoiceNet Network, a hierarchical model of vocal biomarkers was developed, defining terms from broad concepts (biomarker, digital biomarker, vocal biomarker) to specific physiological and cognitive domains (cardio-respiratory acoustic, voice, speech/articulatory, cognitive/language). Meaning: Standardized definitions for vocal biomarkers provide an essential shared vocabulary for interdisciplinary collaboration and lay the foundational groundwork for future guidelines needed to advance the implementation of voice-based health technologies into clinical practice and clinical research.