Speech Emotion Recognition Based on Linear Discriminant Analysis and Support Vector Machine Decision Tree

For speech emotion recognition, emotional feature set with high dimension may produce redundant features and influence the recognition accuracy. To solve this problem and obtain the optimal emotional feature subset of speech, a feature dimension reduction based on linear discriminant analysis is proposed. According to the confusion degree between different basic emotions, an emotion recognition method based on support vector machine decision tree is proposed. Experiment on speaker-dependent speech emotion recognition using Chinese speech database from institute of automation of Chinese academy of sciences is performed and a speech emotion recognition system is presented, where standard feature sets of the INTER-SPEECH and classic classifiers are used in comparative experiments respectively. Experimental results show that the proposal achieves 84.39% recognition accuracy on average. By proposal, it would be fast and efficient to discriminate emotional states of diverse speakers from speech, and it would make it possible to realize the interaction between speaker and computer/robot in the future.

1 Introduction

Human-computer interaction (HCI) has been paid more attention in recent years, by which the emotional state and intentions of the human could be understood [1]. Primary demand that the HCI faces is how to master the ability of identifying emotional information accurately, which is similar to the emotional intelligence capability in human-robot interaction [2]. As fast way for communication and an important medium of emotional expression, speech signal has been used to identify emotions.