Speech emotion recognition based on rough set and SVM

被引：0
|
作者：
Zhou, Jian [1 ,2 ]
Wang, Guoyin [2 ]
Yang, Yong [2 ]
Chen, Peijun [1 ,2 ]
机构：
[1] Southwest Jiaotong Univ, Sch Informat Sci & Technol, Chengdu 610031, Peoples R China
[2] Chongqing Univ Posts & Telecommun, Inst Comp Sci & Technol, Chongqing 400065, Peoples R China
来源：
PROCEEDINGS OF THE FIFTH IEEE INTERNATIONAL CONFERENCE ON COGNITIVE INFORMATICS, VOLS 1 AND 2 | 2006年
基金：
中国国家自然科学基金;
关键词：
speech emotion recognition; rough set; feature selection; SVM;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Speech emotion recognition is becoming more and more important in such computer application fields as health care, children education, etc. There are a few works have been done on speech emotion recognition using such methods as ANN, SVM, etc in the last years. Traditional feature selection method used in speech emotion recognition is computationally too expensive to determine an optimum or suboptimum feature subset. In this paper, a novel approach based on rough set theory and SVM for speech emotion recognition is proposed. The experiment results show this approach can reduce the calculation cost while keeping high recognition rate.
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页码：53 / 61
页数：9
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