GMM supervector based SVM with spectral features for speech emotion recognition

被引：0
|
作者：
Hu, Hao [1 ]
Xu, Ming-Xing [1 ]
Wu, Wei [1 ]
机构：
[1] Tsinghua Univ, Tsinghua Natl Lab Informat Sci & Technol, Ctr Speech Technol, Beijing 100084, Peoples R China
来源：
2007 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOL IV, PTS 1-3 | 2007年
基金：
中国国家自然科学基金;
关键词：
speech emotion recognition; SVM; GMM supervector; spectral features;
D O I：
暂无
中图分类号：
O42 [声学];
学科分类号：
070206 ; 082403 ;
摘要：
Speech emotion recognition is a challenging yet important speech technology. In this paper, the GMM supervector based SVM is applied to this field with spectral features. A GMM is trained for each emotional utterance, and the corresponding GMM supervector is used as the input feature for SVM. Experimental results on an emotional speech database demonstrate that the GMM supervector based SVM outperforms standard GMM on speech emotion recognition.
引用
收藏
页码：413 / +
页数：2
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