Speech Emotion Recognition in Machine Learning to Improve Accuracy using Novel Support Vector Machine and Compared with Decision Tree Algorithm

被引：0

作者：

Amartya, J. Guru Monish ^{[1
]}

Kumar, S. Magesh ^{[1
]}

机构：

[1] Saveetha Univ, Saveetha Inst Med & Tech Sci, Dept Comp Sci & Engn, Chennai 602105, Tamil Nadu, India

来源：

JOURNAL OF PHARMACEUTICAL NEGATIVE RESULTS | 2022年 / 13卷

关键词：

Speech Emotion; Novel Support Vector Machine algorithm; Machine Learning; Decision Tree algorithm; wav audio; Feature Extraction;

D O I：

暂无

中图分类号：

R9 [药学];

学科分类号：

1007 ;

摘要：

Aim: The aim of this research is to improve accuracy for speech emotion recognition using SVM algorithm and DT algorithm. Materials and Methods: The research contains two groups namely SVM algorithm is developed in the first group and DT algorithm is developed in the second group contains 104 samples. The DT algorithm has a sample size of 52 whereas the SVM algorithm has a sample size of 52 and G power (value = 0.8). Results: The performance has been improved in terms of accuracy for the SVM algorithm with 91% while the DT algorithm has shown an accuracy of 62%. The mean accuracy detection is +/- 2SD and the significant value is 0.0415(p<0.05) from an independent sample T test, which is statistically significant between two groups. Conclusion: The final outcome of the SVM (91%) algorithm is found to be significantly more accurate than the DT algorithm(62%).

引用

页码：185 / 192

页数：8

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