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

Author

Listed:
  • Yasuhiro Omiya

    (PST Inc., Yokohama 231-0023, Japan
    Department of Bioengineering, Graduate School of Engineering, The University of Tokyo, Tokyo 113-8656, 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
    Graduate School of Health Innovation, Kanagawa University of Human Services, Yokosuka 210-0821, Japan)

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.

Suggested Citation

  • Yasuhiro Omiya & Daisuke Mizuguchi & Shinichi Tokuno, 2023. "Distinguish the Severity of Illness Associated with Novel Coronavirus (COVID-19) Infection via Sustained Vowel Speech Features," IJERPH, MDPI, vol. 20(4), pages 1-13, February.
    • Handle: RePEc:gam:jijerp:v:20:y:2023:i:4:p:3415-:d:1069439
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    References listed on IDEAS

    as
    1. Shuji Shinohara & Mitsuteru Nakamura & Yasuhiro Omiya & Masakazu Higuchi & Naoki Hagiwara & Shunji Mitsuyoshi & Hiroyuki Toda & Taku Saito & Masaaki Tanichi & Aihide Yoshino & Shinichi Tokuno, 2021. "Depressive Mood Assessment Method Based on Emotion Level Derived from Voice: Comparison of Voice Features of Individuals with Major Depressive Disorders and Healthy Controls," IJERPH, MDPI, vol. 18(10), pages 1-12, May.
    Full references (including those not matched with items on IDEAS)

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