Consensus-Based Definitions for Vocal Biomarkers: The International VOCAL Initiative
Pizzimenti, M.; Kalia, A.; Toghranegar, J. A.; Ebraheem, M.; Cummings, N.; Ghosh, S. S.; Anibal, J. T.; Au, R.; Azarang, A.; Bahr, R. H.; Bedrick, S. D.; Botha, H.; Coleman, O. C.; Elbeji, A.; Kourtis, L. C.; Rameau, A.; Sara, J. D. S.; Watts, S.; Hemmerling, D.; Mekyska, J.; Speights, M. L.; Belisle-Pipon, J.-C.; Bensoussan, Y. E.; Fagherazzi, G.
Show abstract
ImportanceVoice-based health technologies are growing rapidly, but they lack standardized terminology, which hinders interdisciplinary collaboration, research quality, and clinical translation. ObjectiveThe 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. DesignVOCAL is a rigorous, international, multi-stage consensus-building study conducted in 2024-2025. SettingMulti-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. ParticipantsA group of 24 international experts in medicine, clinical research, speech and language, audio signal processing, statistics, methodology, regulation, ethics. MethodsVOCALs 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 measuresConsensus-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). ResultsA 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 RelevanceThis 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 pointsO_ST_ABSQuestionC_ST_ABSCan we establish an international consensus on the definitions related to vocal biomarkers? FindingsThrough 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). MeaningStandardized 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.
Matching journals
The top 1 journal accounts for 50% of the predicted probability mass.
Similar papers in this journal
- A Machine-Learning Based Objective Measure for ALS Disease Severity 91%
- Automatic Identification of Tinnitus Malingering Based on Overt and Covert Behavioral Responses During Psychoacoustic Testing 90%
- Identification of acute exacerbations of chronic obstructive pulmonary disease using simple patient-reported symptoms and cough feature analysis: A diagnostic agreement study 90%
Similar papers in this journal
- Systematic Review of Large Language Models for Patient Care: Current Applications and Challenges 90%
- Prospective validation of smartphone-based heart rate and respiratory rate measurement algorithms 90%
- Effectiveness of bimodal neuromodulation for tinnitus treatment in a real-world clinical setting in United States: A retrospective chart review 89%
Similar papers in this journal
- Performance of ChatGPT in pediatric audiology as rated by students and experts 91%
- Neural adaptation at stimulus onset and speed of neural processing as critical contributors to speech comprehension independent of hearing threshold or age 90%
- Applicability of a short form of the Speech, Spatial und Qualities of Hearing Scale in 97 individuals with Meniere's disease in a multicentric registry 89%
Similar papers in this journal
- Auditory tests for characterizing hearing deficits in listeners with various hearing abilities: The BEAR test battery 90%
- Tinnitus Subtyping with Subgrouping Within Group Iterative Multiple Model Estimation: An Ecological Momentary Assessment Study 90%
- Speech-driven Facial Animations Improve Speech-in-Noise Comprehension of Humans 90%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.