A Research of Speech Emotion Recognition Based on Deep Belief Network and SVM. (12th August 2014)

• Record Type: Journal Article
• Authors: Huang, Chenchen; Gong, Wei; Fu, Wenlong; Feng, Dongyu
• Other Names: Balint Stefan Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2014(2014)
• Publication Date: 2014-08-12
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2014/749604
• Languages: English
• ISSNs: 1024-123X

Abstract

Abstract : 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.