COVID-19 assessment using HMM cough recognition system

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...

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Autores principales: Hamidi, Mohamed, Zealouk, Ouissam, Satori, Hassan, Laaidi, Naouar, Salek, Amine
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Nature Singapore 2022
Materias:
Original Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9595586/
https://www.ncbi.nlm.nih.gov/pubmed/36313860
http://dx.doi.org/10.1007/s41870-022-01120-7
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author Hamidi, Mohamed
Zealouk, Ouissam
Satori, Hassan
Laaidi, Naouar
Salek, Amine
author_facet Hamidi, Mohamed
Zealouk, Ouissam
Satori, Hassan
Laaidi, Naouar
Salek, Amine
author_sort Hamidi, Mohamed
collection PubMed
description 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.
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spelling pubmed-95955862022-10-25 COVID-19 assessment using HMM cough recognition system Hamidi, Mohamed Zealouk, Ouissam Satori, Hassan Laaidi, Naouar Salek, Amine Int J Inf Technol Original Research 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. Springer Nature Singapore 2022-10-25 2023 /pmc/articles/PMC9595586/ /pubmed/36313860 http://dx.doi.org/10.1007/s41870-022-01120-7 Text en © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Original Research
Hamidi, Mohamed
Zealouk, Ouissam
Satori, Hassan
Laaidi, Naouar
Salek, Amine
COVID-19 assessment using HMM cough recognition system
title COVID-19 assessment using HMM cough recognition system
title_full COVID-19 assessment using HMM cough recognition system
title_fullStr COVID-19 assessment using HMM cough recognition system
title_full_unstemmed COVID-19 assessment using HMM cough recognition system
title_short COVID-19 assessment using HMM cough recognition system
title_sort covid-19 assessment using hmm cough recognition system
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9595586/
https://www.ncbi.nlm.nih.gov/pubmed/36313860
http://dx.doi.org/10.1007/s41870-022-01120-7
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