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COVID-19 assessment using HMM cough recognition system
Mohamed Hamidi1,2, Ouissam Zealouk3, Hassan Satori3
1Advanced Systems Engineering Laboratory, ENSA-UIT, Kenitra, Morocco.
International Journal of Information Technology : an Official Journal of Bharati Vidyapeeth'S Institute of Computer Applications and Management
|October 31, 2022
Summary
This study uses Hidden Markov Models (HMMs) to analyze cough sounds, distinguishing between healthy and sick individuals. The system shows promising results for identifying COVID-19 related cough symptoms.
Area of Science:
- Biomedical Engineering
- Computational Linguistics
- Infectious Disease Research
Background:
- The COVID-19 pandemic necessitates novel diagnostic tools.
- Automatic analysis of respiratory sounds like coughs offers a non-invasive approach.
Purpose of the Study:
- To develop and evaluate a Hidden Markov Model (HMM) based automatic speech recognition system for cough signal analysis.
- To differentiate between coughs from healthy individuals and those indicative of illness, including COVID-19 symptoms.
Main Methods:
- Utilized Hidden Markov Models (HMMs) and Gaussian Mixture Models (GMMs).
- Extracted Mel frequency spectral coefficients (MFCCs) from a cough corpus.
- Trained and tested the model on cough data from healthy and sick voluntary speakers.
Main Results:
- The system achieved high sensitivity (85.86%–91.57%) in classifying dry coughs.
- Demonstrated specificity (5%–10%) in differentiating dry coughs from COVID-19 related cough symptoms.
Conclusions:
- The HMM-based approach shows potential for a cough-based diagnostic system for respiratory illnesses.
- Further data enrichment and model refinement are recommended to enhance diagnostic performance.
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