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

Chenchen Huang,Wenlong Fu,Dongyu Feng,Wei Gong

doi:10.1155/2014/749604

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.

Highlights

Speech emotion recognition was a technology that extract emotional feature from speech signals by computer and contrasts and analyses the characteristic parameters and the emotional change acquired
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 deep belief network (DBN) in Deep neural network (DNN) to extract emotional features in speech signal automatically
The quality of feature extraction directly affects the accuracy of speech emotion recognition

Summary

Introduction

Speech emotion recognition was a technology that extract emotional feature from speech signals by computer and contrasts and analyses the characteristic parameters and the emotional change acquired. The law of speech and emotion was concluded and speech emotional states were judged according to the law. Speech emotion recognition was an emerging crossing field of artificial intelligence and artificial psychology; besides, it was a hot research topic of signal processing and pattern recognition [1]. The research was widely applied in human-computer interaction, interactive teaching, entertainment, security fields, and so on. Speech emotion processing and recognition system was generally composed of three parts, which were speech signal acquisition, feature extraction, and emotion recognition.

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