Speech Emotion Recognition Method Based on Improved Decision Tree and Layered Feature Selection

In this paper, in order to improve the classification accuracy with features as few as possible, a new hierarchical recognition method based on an improved SVM decision tree and the layered feature selection method combining neural network with genetic algorithm are proposed. The improved SVM decision tree is constructed according to confusion degrees between two emotions or those between two emotion groups. The classifier in each node of the improved decision tree is a SVM. On the emotional speech corpus recorded by our workgroup including 7 emotions, with the features and parameters gotten by the method combining neural network with genetic algorithm, improved SVM decision tree, multi-SVM, SVM-based binary decision tree, the traditional SVM-based decision directed acyclic graph and HMM are evaluated respectively. The experiments reveal that, compared with the other four methods, the proposed method in this paper appears better classification accuracy with fewer features and less time.

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