Voice as a Digital Biomarker: Foundation Model-Based COPD Assessment | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Voice as a Digital Biomarker: Foundation Model-Based COPD Assessment Sang Mee Lee, Hyein Ryu, Sunga Kong, Sun Hye Shin, Wooseong Huh, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8302274/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Chronic obstructive pulmonary disease (COPD) is frequently underdiagnosed due to limited access to spirometry, highlighting the need for simple and scalable screening tools. Voice offers an easily obtainable and widely accessible respiratory biomarker, yet its potential for COPD assessment remains largely unexplored. Thus, we developed a voice-only COPD assessment model leveraging self-supervised speech representations from a wav2vec 2.0 foundation model using patient-recorded voice. We collected 1,709 recordings from 277 participants (227 COPD, 50 controls) across maximum phonation and standardized reading tasks captured both before and after a 30-second chair-stand test. Without handcrafted acoustic features or clinical variables, the model accurately predicted COPD presence and severity. The post-exercise reading condition achieved the highest performance, with an AUC of 0.81 for COPD detection and 0.71 for severity classification. Age-stratified analysis showed strong discrimination in adults younger than 65 years (AUC 0.87), while severity prediction remained consistent across age groups. These findings demonstrate that exertion-induced vocal alterations, combined with foundation-model representations, encode physiologic signatures of airflow limitation, enabling a practical and scalable approach for COPD screening and remote monitoring. Health sciences/Biomarkers Health sciences/Diseases Health sciences/Health care Health sciences/Medical research Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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