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Mathematical Problems in Engineering
Volume 2014 (2014), Article ID 749604, 7 pages
Research Article

A Research of Speech Emotion Recognition Based on Deep Belief Network and SVM

Department of Computer, Communication University of China, Beijing 100024, China

Received 27 May 2014; Revised 21 July 2014; Accepted 21 July 2014; Published 12 August 2014

Academic Editor: Stefan Balint

Copyright © 2014 Chenchen Huang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Feature extraction is a very important part in speech emotion recognition, and in allusion to feature extraction in speech emotion recognition problems, this paper proposed a new method of feature extraction, using DBNs in DNN to extract emotional features in speech signal automatically. By training a 5 layers depth DBNs, to extract speech emotion feature and incorporate multiple consecutive frames to form a high dimensional feature. The features after training in DBNs were the input of nonlinear SVM classifier, and finally speech emotion recognition multiple classifier system was achieved. The speech emotion recognition rate of the system reached 86.5%, which was 7% higher than the original method.