Abstract
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.
Highlights
In recent years we have seen a growing interest in using voice characteristics to monitor a variety of health conditions [1]
Significant attention has been focused on identifying acoustic features associated with voice changes in disorders such as Parkinson’s disease [2], [3], depression [4], dementia [5], hypertension [6], post-traumatic stress disorder [7], and COVID
In addition to previously studied acoustic features such as Voice Low tone to High tone Ratio (VLHR) [10] and third-octave band metrics [13], we explored a wider set of acoustic features and showed that features capturing spectral structure correlate well with self-reported change in symptoms
Summary
In recent years we have seen a growing interest in using voice characteristics to monitor a variety of health conditions [1]. This has been driven by the low-cost and non-invasive nature of voice recordings, as well as advances in machine learning and audio signal processing. Changes in voice characteristics during influenzalike illnesses are not well understood. Air flows through the oral tract and the nasal passages, when pronouncing nasal consonants or other nasal sounds. The nasal passages produce resonances at distinct
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