Speech Emotion Recognition Using Gaussian Mixture Model

DOI

10.2991/iccasm.2012.311How to use a DOI?

Keywords

Speech Emotion Recognition, Wavelet transform, MFCC, PCA, GMM

Abstract

The importance of automatically recognizing emotions in human speech has grown with the increasing role of spoken language interfaces in human-computer interaction applications. In this paper, a emotion classification method base on GMM is presented. Five primary human emotions, including anger, surprise, happiness, neutral and sadness, are investigated. For speech emotion recognition, we combined 60 basic features to form the feature vector. Finally, the features of the speech were extracted by PCA were sent into the improved GMM for classification and recognition. Results show that the selected features are robust and effective for the emotion recognition .

Copyright

© 2012, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Xianglin Cheng
AU  - Qiong Duan
PY  - 2012/08
DA  - 2012/08
TI  - Speech Emotion Recognition Using Gaussian Mixture Model
BT  - Proceedings of the 2012 International Conference on Computer Application and System Modeling (ICCASM 2012)
PB  - Atlantis Press
SP  - 1222
EP  - 1225
SN  - 1951-6851
UR  - https://doi.org/10.2991/iccasm.2012.311
DO  - 10.2991/iccasm.2012.311
ID  - Cheng2012/08
ER  -