Identification of Speech Features relevant for COVID-19 Detection in Real Time Scenario

The world's biggest pandemic, COVID-19, has shown its lethal impact on human life. The current diagnostics methods are reverse transcription-polymerase chain reaction (RT-PCR) and rapid diagnostic assays have several bottlenecks in terms of the nature of sample collection as it needs some laboratory experts and careful handling of the potentially infectious samples. However, one of the non-invasive ways of diagnostics is to focus on speech modality, which has been paid less attention, during the detection of COVID-19. Hence in this work, the speech features, particularly temporal and spectral features have been studied for COVID-19 detection. The temporal features used in this work are Short-Time Energy, Long-Term Log Energy Variation (LTLEV) Zero Crossing Count (ZCC) and Pitch etc. On the other hand, the spectral features used herein are Power Spectral Density, Average Power, Mel-Frequency Cepstral Coefficients, Group delay spectrum, Spectral Entropy etc. Such spectral and temporal speech features have not been analyzed in the identification of COVID-19 symptoms to the best of authors knowledge. Further, this paper has shown the impact of COVID-19 on a real time human voice, analyzed using speech processing techniques, and shown their efficacy in detecting COVID-19. These features are safe, comparatively faster, cost-effective, and require fewer complexities. Our article will motivate the scientific community to use such features for further research in the collective battle against COVID-19.