Improved Accuracy in Speech Recognition System for Detection of COVID-19 using Support Vector Machine and Comparing with Convolution Neural Network Algorithm
11th International Conference on System Modeling and Advancement in Research Trends, SMART 2022
; : 1303-1307, 2022.
Article
in English
| Scopus | ID: covidwho-2264663
ABSTRACT
The objective of the research aims to detect Covid-19 patients by innovative speech recognition using a Support Vector Machine (SVM) and comparing accuracy with Convolutional Neural Network (CNN). Speech recognition using SVM is considered as group 1 and Convolutional Neural Network is considered as group 2, where each group has 20 samples. A T-test with 95% CI, G-power of 80%, and alpha=0.05 was used to compare the two sets of data. CNN achieves an accuracy of 87.5% and SVM achieves an accuracy of 92.5% with significance value 0.043 (P<0.05). Covid-19 prediction using an innovative speech recognition using SVM achieves significantly better accuracy than CNN. © 2022 IEEE.
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Database:
Scopus
Language:
English
Journal:
11th International Conference on System Modeling and Advancement in Research Trends, SMART 2022
Year:
2022
Document Type:
Article
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