Mandarin emotional speech recognition based on SVM and NN

The exploration of how we as human beings react to the world and interact with it and each other remains one of the greatest scientific challenges. The ability to recognize emotional states of a person perhaps the most important for successful inter-personal social interaction. Automatic emotional speech recognition system can be characterized by the used features, the investigated emotional categories, the methods to collect speech utterances, the languages, and the type of classifier used in the experiments. In this paper, we used SVM and NN classifiers and feature selection algorithm to classify five emotions from Mandarin emotional speech and compared their experimental results. The overall experimental results reveal that the SVM classifier (84.2%) outperforms than NN classifier (80.8%) and detects anger perfectly, but confuses happiness with sadness, boredom and neutral. The NN classifier achieves better performance in recognizing sadness and neutral and differentiates happiness and boredom perfectly.