Enhancement of Accuracy in Speech Recognition System for Detection of Covid-19 using Support Vector Machine

• Published in: 2022 11th International Conference on System Modeling & Advancement in Research Trends (SMART) pp. 1608 - 1613
• Main Authors: Jhansi, Rallapalli, Uganya, G.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 16-12-2022
• Subjects: COVID-19; Covid-19 Detection; Innovative Speech Recognition; Machine Learning; Predictive models; Radio frequency; Random Forest; Speech recognition; Statistical analysis; Support Vector Machine; Support vector machines
• ISBN: 9781665487320; 1665487321
• ISSN: 2767-7362
• DOI: 10.1109/SMART55829.2022.10047498

The goal of this study is todetect Covid-19 by innovative speech recognition using Support Vector Machine (SVM) and comparing its accuracy with Random Forest (RF). Speech recognition using SVM is considered as group 1 and Random Forest is considered as group 2, where each group has 20 samples. In order to solve classification and regression problems, a machine learning technique known as RF is employed. Using the average of the samples for regression and the majority vote for classification, it generates decision trees. A machine learning algorithm called SVM used to identify the classification of patients based on genes and other biological problems. These groups were analysed by an independent sample T -test with alpha is 0.05; G power is 80% and confidence interval is 95%. RF achieves an accuracy of 84.5% and SVM achieves an accuracy of 94.6% with significance value 0.045 (p<0.05). From statistical analysis, it is observed that SVM has significantly better accuracy than RF.