Distinguish the Severity of Illness Associated with Novel Coronavirus (COVID-19) Infection via Sustained Vowel Speech Features

Yasuhiro Omiya (), Daisuke Mizuguchi and Shinichi Tokuno
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Yasuhiro Omiya: PST Inc., Yokohama 231-0023, Japan
Daisuke Mizuguchi: PST Inc., Yokohama 231-0023, Japan
Shinichi Tokuno: Department of Bioengineering, Graduate School of Engineering, The University of Tokyo, Tokyo 113-8656, Japan

IJERPH, 2023, vol. 20, issue 4, 1-13

Abstract: The authors are currently conducting research on methods to estimate psychiatric and neurological disorders from a voice by focusing on the features of speech. It is empirically known that numerous psychosomatic symptoms appear in voice biomarkers; in this study, we examined the effectiveness of distinguishing changes in the symptoms associated with novel coronavirus infection using speech features. Multiple speech features were extracted from the voice recordings, and, as a countermeasure against overfitting, we selected features using statistical analysis and feature selection methods utilizing pseudo data and built and verified machine learning algorithm models using LightGBM. Applying 5-fold cross-validation, and using three types of sustained vowel sounds of /Ah/, /Eh/, and /Uh/, we achieved a high performance (accuracy and AUC) of over 88% in distinguishing “asymptomatic or mild illness (symptoms)” and “moderate illness 1 (symptoms)”. Accordingly, the results suggest that the proposed index using voice (speech features) can likely be used in distinguishing the symptoms associated with novel coronavirus infection.

Keywords: voice biomarker; COVID-19; sustained vowel (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2023
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