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A feature-driven hierarchical classification approach to emotions in speeches using SVMs

Emotion recognition continues to yield more as the sophisticated track of applications in the field of human computer interaction. The main consequence of classifying the emotional states is selecting best out of a large scale of features and implementing a pattern for the classification of emotions. In this paper we propose a hierarchical classification strategy using Mel — frequency cepstral coefficients (MFCC). Statistical measurements of MFCC were explored individually in classification and the optimal model for each decision node of a binary tree is constructed using a one-versus-all based SVM classifier a the subset of statistical measures that yields the highest recognition. The overall results of experiments disclose that the basic emotions such as anger, sad, happy, and neutral can be classified in a hierarchical structure. The proposed framework has been evaluated on two benchmark speech sets: Berlin and Danish, and provides better performance in emotion classification.