Speech Emotion Recognition Using Support Vector Machines

被引：0

作者：

Yu, Caiming ^{[1
]}

Tian, Qingxi ^{[1
]}

Cheng, Fang ^{[1
]}

Zhang, Shiqing ^{[1
]}

机构：

[1] Taizhou Univ, Sch Phys & Elect Engn, Taizhou 318000, Peoples R China

来源：

ADVANCED RESEARCH ON COMPUTER SCIENCE AND INFORMATION ENGINEERING, PT I | 2011年 / 152卷

关键词：

Emotion recognition; Feature extraction; Support vector machines;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Speech emotion recognition is a new and challenging subject in artificial intelligence field. This paper explores the issues involved in applying support vector machines (SVM) classifier to emotion recognition in speech signals. The performance of SVM on speech emotion recognition task is compared with linear discriminant classifiers (LDC), K-nearest-neighbor (KNN), and radial basis function neutral network (RBFNN). The experimental results on emotional Chinese speech corpus demonstrate that SVM obtains the highest accuracy of 85%, outperforming the other used classification methods.

引用

页码：215 / 220

页数：6

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