Evidence map›Paper›PMID 42757077›Full record

ArticleDigital biomarkers

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

Mégane Pizzimenti, Ayush Kalia, Jamie A Toghranegar, Mohamed Ebraheem, Nicholas Cummins, Satrajit S Ghosh, James T Anibal, Rhoda Au, Arian Azarang, Ruth H Bahr and 16 more

Abstract read
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In one paragraph

Article in Digital biomarkers. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. An End-to-End Overview of Clinical Speech AI.IEEE transactions on audio, speech, and language processing (2025) · 2026
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

26 authors.

Mégane PizzimentiDeep Digital Phenotyping Research Unit, Department of Precision Health, Luxembourg Institute of Health, Strassen, Luxembourg.
Ayush KaliaUSF Health Voice Center, Department of Otolaryngology-Head and Neck Surgery, University of South Florida, Tampa, FL, USA.
Jamie A ToghranegarUSF Health Voice Center, Department of Otolaryngology-Head and Neck Surgery, University of South Florida, Tampa, FL, USA.
Mohamed EbraheemUSF Health Voice Center, Department of Otolaryngology-Head and Neck Surgery, University of South Florida, Tampa, FL, USA.
Nicholas CumminsDepartment of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
Satrajit S GhoshMcGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA.
James T AnibalCenter for Interventional Oncology, NIH Clinical Center, National Institutes of Health, Bethesda, MD, USA.
Rhoda AuDepartment of Medicine, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, USA.
Arian AzarangDepartment of Radiology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Ruth H BahrDepartment of Communication Sciences and Disorders, University of South Florida, Tampa, FL, USA.
Sybille BarvauxDeep Digital Phenotyping Research Unit, Department of Precision Health, Luxembourg Institute of Health, Strassen, Luxembourg.
Steven D BedrickDepartments of Medical Informatics and Clinical Epidemiology and Medicine, Oregon Health and Science University, Portland, OR, USA.
Hugo BothaDepartment of Neurology, Mayo Clinic, Rochester, MN, USA.
Oita C ColemanOpen Voice TrustMark Initiative, Apex, NC, USA.
Abir ElbejiDeep Digital Phenotyping Research Unit, Department of Precision Health, Luxembourg Institute of Health, Strassen, Luxembourg.
Lampros C KourtisAlzheimer's Drug Discovery Foundation, Boston, MA, USA.
Anaïs RameauDepartment of Otolaryngology-Head and Neck Surgery, Weill Cornell Medical College, Sean Parker Institute for the Voice, New York, NY, USA.
Jaskanwal Deep Singh SaraDepartment of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA.
Stephanie W WattsUSF Health Voice Center, Department of Otolaryngology-Head and Neck Surgery, University of South Florida, Tampa, FL, USA.
Daria HemmerlingDepartment of Measurement and Electronics, AGH University of Krakow, Kraków, Poland.
Jiri MekyskaDepartment of Telecommunications, Brno University of Technology, Brno, Czech Republic.
Marisha L SpeightsRoxelyn and Richard Pepper Department of Communication Sciences and Disorders, Northwestern University, Evanston, IL, USA.
Jean-Christophe Bélisle-PiponDepartment of Bio-ethics, Faculty of Health Sciences, Simon Fraser University, Burnaby, BC, Canada.
Yael E BensoussanUSF Health Voice Center, Department of Otolaryngology-Head and Neck Surgery, University of South Florida, Tampa, FL, USA.
Guy FagherazziDeep Digital Phenotyping Research Unit, Department of Precision Health, Luxembourg Institute of Health, Strassen, Luxembourg.
Bridge2AI Voice Consortium; the eVoiceNet COST Action (CA24128)

Funding

Bridge2AI: Voice as a Biomarker of Health - Building an ethically sourced, bioaccoustic database to understand disease like never beforeOT2OD032720 · OD · UNIVERSITY OF SOUTH FLORIDA · PI BENSOUSSAN, YAEL EMILIE, BÉLISLE-PIPON, JEAN-CHRISTOPHE · 2022 to 2025
$18.0M
NIH HHS OT2 OD032720
6 · The paper itself

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.

Indexed as

Artificial intelligenceDigital biomarkerDigital healthSignal processingVocal biomarker

Identifiers

PMID42757077
PMCPMC13585421

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.