Speech Emotion Recognition Based on Deep Learning and Kernel Nonlinear PSVM

For the sake of ameliorating the precision of speech emotion recognition, this paper put forward a new emotion recognition technique based on Deep Learning and Kernel Nonlinear PSVM (Proximal Support Vector Machine) to discern four fundamental human emotion (angry, joy, sadness, surprise). First of all, preprocess speech signal. And then use DBN (Deep Belief Networks) to extract emotional features in speech signal automatically. After that, integrate the DBN automatic features and traditional features (prosody features and quality features) as the total features. Finally, use six Nonlinear Proximal Support Vector Machines to recognize the emotion and use majority voting principle to obtain the final identification result. To assess the new method, we compare the total features, DBN automatic features and traditional features. The experimental results indicate that the total features are better than the other two methods.