Speech signal analysis of COVID-19 patients via machine learning approach

The novel coronavirus is the most crucial pandemic that has been faced in recent times. The pandemic has caused severe economic and social devastation, and due to the lack of experience, many countries have a huge burden to protect their people from the coronavirus. This virus spreads from person to person so easily via the droplets. Hence, it is hard to identify the virus if they do not show the symptoms; thus, the asymptomatic person can become a super spreader and spread the virus faster than a symptomatic person. It is essential to seek a technological-based application that can be globally used. Therefore, this paper proposes a machine learning-based speech signal processing application to identify the asymptomatic and mild symptomatic COVID-19 virus-infected individuals. The proposed method uses gammatone cepstral features along with eight machine learning methods for the classification task. Finally, the best-trained machine learning model will apply in a real-time-based speech signal processing application. The final results show that the K-Nearest Neighbour (KNN) and Ensemble Bagged Tree (EBT) can provide better classification results than the other machine learning models.