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

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 dis...

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Bibliographic Details
Published in:International journal of environmental research and public health 2023-02, Vol.20 (4), p.3415
Main Authors: Omiya, Yasuhiro, Mizuguchi, Daisuke, Tokuno, Shinichi
Format: Article
Language:English
Subjects:
Accuracy
Analysis
Biomarkers
Communications industry
Coronaviruses
COVID-19
Data collection
Data mining
Disease transmission
Health aspects
Illnesses
Infection
Infections
Machine learning
Medical research
Medicine, Experimental
Neurological diseases
Participation
Pulse oximetry
Questionnaires
Signs and symptoms
Smartphones
Speech
Speech recognition
Statistical analysis
Telecommunications services industry
Vowels
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Summary: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.
ISSN:1660-4601
1661-7827
1660-4601
DOI:10.3390/ijerph20043415