Decision tree SVM model with Fisher feature selection for speech emotion recognition

Abstract

The overall recognition rate will reduce due to the increase of emotional confusion in multiple speech emotion recognition. To solve the problem, we propose a speech emotion recognition method based on the decision tree support vector machine (SVM) model with Fisher feature selection. At the stage of feature selection, Fisher criterion is used to filter out the feature parameters of higher distinguish ability. At the emotion classification stage, an algorithm is proposed to determine the structure of decision tree. The decision tree SVM can realize the two-step classification of the first rough classification and the fine classification. Thus the redundant parameters are eliminated and the performance of emotion recognition is improved. In this method, the decision tree SVM framework is firstly established by calculating the confusion degree of emotion, and then the features with higher distinguish ability are selected for each SVM of the decision tree according to Fisher criterion. Finally, speech emotion recognition is realized based on this model. The decision tree SVM with Fisher feature selection on CASIA Chinese emotion speech corpus and Berlin speech corpus are constructed to validate the effectiveness of our framework. The experimental results show that the average emotion recognition rate based on the proposed method is 9% higher than traditional SVM classification method on CASIA, and 8.26% higher on Berlin speech corpus. It is verified that the proposed method can effectively reduce the emotional confusion and improve the emotion recognition rate.