Speech Recognition for COVID-19 Keywords Using Machine Learning

Wael Ben Amara¹ , Amani Touihri² , Salma Hamza³

Section:Research Paper, Product Type: Journal-Paper
Vol.8 , Issue.4 , pp.51-57, Aug-2020

Online published on Aug 31, 2020

Copyright © Wael Ben Amara, Amani Touihri, Salma Hamza . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
As of June 01, 2020, coronavirus disease, 2019 (COVID-19) has been confirmed in 7,274,000 people worldwide, affecting over 213 countries. It becomes a major healthcare challenge around the world to counter this novel epidemic. The aim of this study is to investigate the detection of patients with suspected COVID-19 infection through phone calls. Artificial Neural Network (ANN) and Support Vector Machine (SVM) classifiers for detection to COVID patients are built and their performances are compared. Experiments were carried out on Arabic speech signals of recorded phone calls. From speech signals, relevant feature extraction of keywords is achieved. The results are very promising. We have reached 97% accuracy. Thanks to this classification, we would be able to know if the recorded call deserves a callback or not which would ease the workload on the health care system. The model can evolve by building better and more solid classifiers that can be used in public security when it comes to analyzing phone calls

Key-Words / Index Term :
COVID-19; Support Vector Machine; Artificial Neural Network

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