Speech phoneme and spectral smearing based non-invasive COVID-19 detection - Mishra, Soumya; Dash, Tusar Kanti; Panda, Ganapati (Frontiers in Artificial Intelligence, 2023)
COVID-19 is a deadly viral infection which mainly affects the nasopharyngeal and oropharyngeal cavity prior to the lung in the human body. Early detection followed by immediate treatment can potentially reduce lung invasion and decrease fatality. Recently, several COVID-19 detection methods have been proposed using cough and breath sounds. However, very little work has been done on the phoneme analysis and smearing of the audio signal in COVID-19 detection. In this paper, this problem has been considered for the classification of speech samples into COVID-19 positive and healthy audio samples. Additionally, the grouping of the phonemes based on reference classification accuracies has been proposed for effective and faster detection of the disease at a primary stage. The Mel and Gammatone Cepstral Coefficients, and their derivatives are used as the features for five standard Machine Learning based classifiers. It is observed that the Generalized Additive Model provides the highest accuracy of 97.22% for the phoneme grouping ‘/t//r//n//g//l/’. This smearing-based phoneme classification technique can also be used in the future to classify other speech-related disease detections.