A Study on the Search of the Most Discriminative Speech Features in the Speaker Dependent Speech Emotion Recognition

被引：6
|
作者：
Pao, Tsang-Long [1 ]
Wang, Chun-Hsiang [1 ]
Li, Yu-Ji [1 ]
机构：
[1] Tatung Univ, Dept Comp Sci & Engn, Taipei 104, Taiwan
来源：
2012 FIFTH INTERNATIONAL SYMPOSIUM ON PARALLEL ARCHITECTURES, ALGORITHMS AND PROGRAMMING (PAAP) | 2012年
关键词：
Speech Emotion Recognition; Speech Feature Selection; WD-KNN Classifier; GMM Classifier; IDENTIFICATION;
D O I：
10.1109/PAAP.2012.31
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
Expressing emotion to others and recognizing emotion state of the counterpart are not difficult for human. Emotion state of a person may be recognized from the facial expression, voice, and/or gesture. Speech emotion recognition research gained a lot of attention in recent years. One of the important subjects in speech emotion recognition research is the feature selection. The speech features used will greatly influence the recognition rate. In this research, we try to find the most discriminative features for emotion recognition out from a set of 78 features. We use these features to study the feature characteristics for individual speaker by using a GMM classifier. We obtained an average of 71% recognition rate in speaker dependent case while an average of 48% recognition rate in speaker independent case.
引用
收藏
页码：157 / 162
页数：6
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