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Recently there has been a formidable work which has been put up from thepeople who are working in the frontlines such as hospitals, clinics, and labsalongside researchers and scientists who are also putting tremendous efforts inthe fight against COVID-19 pandemic. Due to the preposterous spread of thevirus, the integration of the artificial intelligence has taken a considerablepart in the health sector, by implementing the fundamentals of Automatic SpeechRecognition (ASR) and deep learning algorithms. In this paper, we illustratethe importance of speech signal processing in the extraction of theMel-Frequency Cepstral Coefficients (MFCCs) of the COVID-19 and non-COVID-19samples and find their relationship using Pearson correlation coefficients. Ourresults show high similarity in MFCCs between different COVID-19 cough andbreathing sounds, while MFCC of voice is more robust between COVID-19 andnon-COVID-19 samples. Moreover, our results are preliminary, and there is apossibility to exclude the voices of COVID-19 patients from further processingin diagnosing the disease.

Studying the Similarity of COVID-19 Sounds based on Correlation Analysis of MFCC