Preliminary Diagnosis of COVID-19 using Speech Processing Techniques
2023 International Conference on Intelligent Systems for Communication, IoT and Security, ICISCoIS 2023
; : 157-161, 2023.
Article
in English
| Scopus | ID: covidwho-2327239
ABSTRACT
This project aims to devise an alternative for Coronavirus detection using various audio signals. The aim is to create a machine-learning model assisted by speech processing techniques that can be trained to distinguish symptomatic and asymptomatic Coronavirus cases. Here the features exclusive to the vocal cord of a person is used for covid detection. The procedure is to train the classifier using a data set containing data of people of various ages both infected and disease-free, including patients with comorbidities. We presented a machine learning-based Coronavirus classifier model that can separate Coronavirus positive or negative patients from cough, breathing, and speech recordings. The model was trained and evaluated using several machine learning classifiers such as Random Forest Classifier, Logistic Regression (LR), Decision Tree Classifier, k-nearest Neighbour (KNN), Naive Bayes Classifier, Linear Discriminant Analysis, and a neural network. This project helps track COVID-19 patients at a low cost using a non-contactable procedure and reduces the workload on testing centers. © 2023 IEEE.
COVID-19; MFCC; Random Forest Classifier; speech samples; Decision trees; Diagnosis; Discriminant analysis; Learning systems; Nearest neighbor search; Speech processing; Audio signal; Coronaviruses; Data set; Machine learning models; Machine-learning; Processing technique; Speech sample; Vocal cords; Coronavirus
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Experimental Studies
/
Randomized controlled trials
Language:
English
Journal:
2023 International Conference on Intelligent Systems for Communication, IoT and Security, ICISCoIS 2023
Year:
2023
Document Type:
Article
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