关键词: 情感识别, 支持向量机, 特征选择

Abstract: To improve the recognition performance of speech emotion recognition, a high dimension acoustic feature set is constructed by basic acoustic features. A secondary feature selection method comprehensively considering the inherent properties between the features and emotions is adopted to select optimal subset with effective emotional recognizability. In the emotion recognition procedure, a binary tree structured multi-class classifier model is adopted to make compromise between total performance and complexity of the system. Kernel fusion method is utilized in SVM model to improve the recognition of the most confusable emotion. The experimental results of five emotions in Berlin database verify the effectiveness of the combination of secondary feature selection and kernel fusion on the improvement of emotional recognition accuracies and its robustness on noisy samples.

Key words: emotion recognition, support vector machine, feature selection