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BRIEF RESEARCH REPORT article

Front. Digit. Health

Sec. Connected Health

Volume 7 - 2025 | doi: 10.3389/fdgth.2025.1609811

This article is part of the Research TopicAdvancing Vocal Biomarkers and Voice AI in Healthcare: Multidisciplinary Focus on Responsible and Effective Development and UseView all 10 articles

Voice as a Biomarker: Exploratory Analysis for Benign and Malignant Vocal Fold Lesions

Provisionally accepted
  • 1Oregon Health and Science University, Portland, United States
  • 2Portland State University, Portland, Oregon, United States

The final, formatted version of the article will be published soon.

Benign and malignant vocal fold lesions can alter voice quality and lead to significant morbidity or, in the case of malignancy, mortality. Early, noninvasive identification of these lesions using voice as a biomarker may improve diagnostic access and outcomes. In this study, we analyzed data from the initial release of the Bridge2AI-Voice dataset to evaluate which acoustic features best distinguish laryngeal cancer and benign vocal fold lesions from other vocal pathologies and healthy voice function. Seven diagnostic cohorts were grouped into two analyses: the first included participants with laryngeal cancer, benign lesions, or no voice disorder; the second included those with laryngeal cancer or benign lesions without other voice disorders, as well as individuals with spasmodic dysphonia or vocal fold paralysis. Acoustic features including fundamental frequency, jitter, shimmer, and harmonic-to-noise ratio (HNR) were extracted from standardized speech recordings and compared using nonparametric statistical methods. Among the overall sample, significant differences were identified in HNR and fundamental frequency between benign lesions and both healthy controls and laryngeal cancer. In cisgender men, these distinctions were also observed, particularly in HNR and its variability. No statistically significant differences were observed among cisgender women, likely due to the limited sample size. These findings suggest that HNR, particularly its variability, may hold promise as a voicebased marker for early detection and monitoring of vocal fold lesions. Further research with larger, more diverse populations is needed to refine these features and validate their clinical utility.

Keywords: Voice biomarkers, Bridge 2 AI, machine learning (ML), Laryngeal lesions, Voice 2 AI

Received: 11 Apr 2025; Accepted: 25 Jun 2025.

Copyright: © 2025 Jenkins, Harrison, Bedrick, Karstens and Hersh. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Phillip Jenkins, Oregon Health and Science University, Portland, United States

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