Voice Biomarkers of Recovery From Acute Respiratory Illness
Open accessAbstract
Voice analysis is an emerging technology which has the potential to provide low-cost, at-home monitoring of symptoms associated with a variety of health conditions. While voice has received significant attention for monitoring neurological disease, few studies have focused on voice changes related to flu-like symptoms. Herein, we investigate the relationship between changes in acoustic features of voice and self-reported symptoms during recovery from a flu-like illness in a cohort of 29 subjects. Acoustic features were automatically extracted from "sick" and "well" visit data collected in the laboratory setting, and feature down-selection was used to identify those that change significantly between visits. The selected acoustic features were extracted from at-home data and used to construct a combined distance metric that correlated with self-reported symptoms (0.63 rank correlation). Changes in self-reported symptoms corresponding to 10% of the ordinal scale used in the study were detected with an area under the curve of 0.72. The results show that acoustic features derived from voice recordings may provide an objective measure for diagnosing and monitoring symptoms of respiratory illnesses.
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Voice biomarkers such as jitter, shimmer, and spectral contrast show consistent trends for recovery from acute respiratory illness and can distinguish recovering participants from healthy users.
“Voice biomarkers have also been analysed for recovery from acute respiratory illness [61]. The findings suggest that jitter, shimmer and spectral contrast within certain frequency bands show consistent trends for recovering participants, and can be informative to distinguish them from healthy users as well.”
From Human-centred artificial intelligence for mobile health sensing: challenges and opportunities (2023) · cites this paper as [61]
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- Authors
- Tracey, B., Patel, S., Zhang, Y., Chappie, K., Volfson, D., Parisi, F., Adans-Dester, C., Bertacchi, F., Bonato, P., & Wacnik, P.
- Affiliations
- Signal Insights LLC, Arlington, MA, USA
- Pfizer (United States) (US)
- Harvard University (US)
- ORCID
- Venue
- IEEE Journal of Biomedical and Health Informatics
- Published
- 2022-06
- Volume
- 26
- Issue
- 6
- Pages
- 2787-2795
- ISSN
- 2168-2194 (ISSN-L)
- 2168-2208
- OpenAlex
- W4200161565
- Cited by
- 10
- References
- 30
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 2 · 2023: 4
- FWCI
- 0.94
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- IEEE Journal of Biomedical and Health Informatics (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Spaulding Rehabilitation Hospital
- Pfizer
- Topics
- Health Sciences › Medicine › Physiology › Voice and Speech Disorders
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Phonocardiography and Auscultation Techniques
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Keywords
- vocal biomarkers, acoustic features, influenza-like illness, acute respiratory illness, symptom recovery, self-reported symptoms, distance measures
- MeSH terms
- Acoustics, Humans, Voice, Biomarkers, Respiratory Rate, Self Report
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