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. 2022 Sep 20:10:4901309.
doi: 10.1109/JTEHM.2022.3208057. eCollection 2022.

Voice Features of Sustained Phoneme as COVID-19 Biomarker

Nemuel D Pah  1 , Veronica Indrawati  1 , Dinesh K Kumar  2
Affiliations

Voice Features of Sustained Phoneme as COVID-19 Biomarker

Nemuel D Pah et al. IEEE J Transl Eng Health Med. .

Abstract

Background: The COVID-19 pandemic has resulted in enormous costs to our society. Besides finding medicines to treat those infected by the virus, it is important to find effective and efficient strategies to prevent the spreading of the disease. One key factor to prevent transmission is to identify COVID-19 biomarkers that can be used to develop an efficient, accurate, noninvasive, and self-administered screening procedure. Several COVID-19 variants cause significant respiratory symptoms, and thus a voice signal may be a potential biomarker for COVID-19 infection.

Aim: This study investigated the effectiveness of different phonemes and a range of voice features in differentiating people infected by COVID-19 with respiratory tract symptoms.

Method: This cross-sectional, longitudinal study recorded six phonemes (i.e., /a/, /e/, /i/, /o/, /u/, and /m/) from 40 COVID-19 patients and 48 healthy subjects for 22 days. The signal features were obtained for the recordings, which were statistically analyzed and classified using Support Vector Machine (SVM).

Results: The statistical analysis and SVM classification show that the voice features related to the vocal tract filtering (e.g., MFCC, VTL, and formants) and the stability of the respiratory muscles and lung volume (Intensity-SD) were the most sensitive to voice change due to COVID-19. The result also shows that the features extracted from the vowel /i/ during the first 3 days after admittance to the hospital were the most effective. The SVM classification accuracy with 18 ranked features extracted from /i/ was 93.5% (with F1 score of 94.3%).

Conclusion: A measurable difference exists between the voices of people with COVID-19 and healthy people, and the phoneme /i/ shows the most pronounced difference. This supports the potential for using computerized voice analysis to detect the disease and consider it a biomarker.

Keywords: COVID-19; support vector machine; sustained phoneme; voice features.

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Figures

FIGURE 1.
FIGURE 1.
The number of significant features and average effect size (ES) of statistical test between control subjects (HC) and COVID-19 patients (CV) for each day of recordings.
FIGURE 2.
FIGURE 2.
The number of significant features and average effect size (ES) of statistical test between control subjects (HC) and COVID-19 patients (CV) for each phonemes recorded on days 4 to 6.
FIGURE 3.
FIGURE 3.
The performance (F1 score, accuracy, sensitivity, and selectivity) of SVM classification with different number of ranked features. The input to SVM is the recordings from Days 1–3.
FIGURE 4.
FIGURE 4.
Confusion matrix of SVM classification between COVID-19 patients (CV) and control subjects (HC) based on 18 ranked features of phoneme /i/ recorded during the first 3 days in the hospital.
See this image and copyright information in PMC

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References

    1. Cucinotta D. and Vanelli M., “WHO declares COVID-19 a pandemic,” Acta Biomed., vol. 91, no. 1, pp. 157–160, Mar. 2020. - PMC - PubMed
    1. WHO. WHO Coronavirus (COVID-19) Dashboard. Accessed: Jan. 28, 2022. [Online]. Available: https://covid19.who.int/
    1. Suppakitjanusant P.et al. , “Identifying individuals with recent COVID-19 through voice classification using deep learning,” Sci. Rep., vol. 11, no. 1, pp. 1–7, Dec. 2021. - PMC - PubMed
    1. Elliott J.et al. , “Predictive symptoms for COVID-19 in the community: REACT-1 study of over 1 million people,” PLoS Med., vol. 18, no. 9, 2021, Art. no. e1003777. - PMC - PubMed
    1. Lechien J. R.et al. , “Features of mild-to-moderate COVID-19 patients with dysphonia,” J. Voice, vol. 36, no. 2, pp. 249–255, 2020. - PMC - PubMed

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