Voice processing for COVID-19 scanning and prognostic indicator
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Savita Sondhi1, Ashok Salhan2, Claire A Santoso1
1Bina Nusantara Group, Anggrek Campus, 11530 Jakarta, Indonesia.
Abstract:
COVID-19 pandemic has posed serious risk of contagion to humans. There is a need to find reliable non-contact tests like vocal correlates of COVID-19 infection. Thirty-six Asian ethnic volunteers 16 (8M & 8F) infected subjects and 20 (10M &10F) non-infected controls participated in this study by vocalizing vowels /a/, /e/, /i/, /o/, /u/. Voice correlates of 16 COVID-19 positive patients were compared during infection and after recovery with 20 non-infected controls. Compared to non-infected controls, significantly higher values of energy intensity for /o/ (p = 0.048); formant F1 for /o/ (p = 0.014); and formant F3 for /u/ (p = 0.032) were observed in male patients, while higher values of Jitter (local, abs) for /o/ (p = 0.021) and Jitter (ppq5) for /a/ (p = 0.014) were observed in female patients. However, formant F2 for /u/ (p = 0.018), mean pitch F0 for /e/, /i/ and /o/ (p = 0.033; 0.036; 0.047) decreased for female patients under infection. Compared to recovered conditions, HNR for /e/ (p = 0.014) was higher in male patients under infection, while Jitter (rap) for /a/ (p = 0.041); Jitter (ppq5) for /a/ (p = 0.032); Shimmer (local, dB) for /i/ (p = 0.024); Shimmer (apq5) for /u/ (p = 0.019); and formant F4 for vowel /o/ (p = 0.022) were higher in female patients under infection. However, HNR for /e/ (p = 0.041); and formant F1 for /o/ (p = 0.002) were lower in female patients compared to their recovered conditions. Obtained results support the hypothesis since changes in voice parameters were observed in the infected patients which can be correlated to a combination of acoustic measures like fundamental frequency, formant characteristics, HNR, and voice perturbations like jitter and shimmer for different vowels. Thus, voice analysis can be used for scanning and prognosis of COVID-19 infection. Based on the findings of this study, a mobile application can be developed to analyze human voice in real-time to detect COVID-19 symptoms for remedial measures and necessary action.
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