  Recently there has been a formidable work which has been put up from the
people who are working in the frontlines such as hospitals, clinics, and labs
alongside researchers and scientists who are also putting tremendous efforts in
the fight against COVID-19 pandemic. Due to the preposterous spread of the
virus, the integration of the artificial intelligence has taken a considerable
part in the health sector, by implementing the fundamentals of Automatic Speech
Recognition (ASR) and deep learning algorithms. In this paper, we illustrate
the importance of speech signal processing in the extraction of the
Mel-Frequency Cepstral Coefficients (MFCCs) of the COVID-19 and non-COVID-19
samples and find their relationship using Pearson correlation coefficients. Our
results show high similarity in MFCCs between different COVID-19 cough and
breathing sounds, while MFCC of voice is more robust between COVID-19 and
non-COVID-19 samples. Moreover, our results are preliminary, and there is a
possibility to exclude the voices of COVID-19 patients from further processing
in diagnosing the disease.
