AUTHOR=Mishra Soumya , Dash Tusar Kanti , Panda Ganapati TITLE=Speech phoneme and spectral smearing based non-invasive COVID-19 detection JOURNAL=Frontiers in Artificial Intelligence VOLUME=5 YEAR=2023 URL=https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2022.1035805 DOI=10.3389/frai.2022.1035805 ISSN=2624-8212 ABSTRACT=

COVID-19 is a deadly viral infection that mainly affects the nasopharyngeal and oropharyngeal cavities before the lung in the human body. Early detection followed by immediate treatment can potentially reduce lung invasion and decrease fatality. Recently, several COVID-19 detections methods have been proposed using cough and breath sounds. However, very little study has been done on the use of phoneme analysis and the smearing of the audio signal in COVID-19 detection. In this paper, this problem has been addressed and the classification of speech samples has been carried out in COVID-19-positive and healthy audio samples. Additionally, the grouping of the phonemes based on reference classification accuracies have been proposed for effectiveness 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.