COVID-19 assessment using HMM cough recognition system

Autor: Hamidi, Mohamed, Zealouk, Ouissam, Satori, Hassan, Laaidi, Naouar, Salek, Amine
Zdroj: International Journal of Information Technology; January 2023, Vol. 15 Issue: 1 p193-201, 9p
Abstrakt: This paper is a part of our contributions to research on the ongoing COVID-19 pandemic around the world. This research aims to use Hidden Markov Model (HMM) based automatic speech recognition system to analyze the cough signal and determine whether the signal belongs to a sick or healthy speaker. We built a configurable model by using HMMs, Gaussian Mixture Models (GMMs), Mel frequency spectral coefficients (MFCCs) and a cough corpus collected from healthy and sick voluntary speakers. Our proposed method is able to classify dry cough with sensitivity from 85.86% to 91.57%, differentiate the dry cough, and cough COVID-19 symptom with specificity from 5 to 10%. The obtained results are very encouraging to enrich our corpus with more data and increase the performance of our diagnostic system.
Databáze: Supplemental Index
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