Speech emotion recognition based on Fuzzy Least Squares Support Vector Machines

Abstract

A new method of speech emotion recognition in speech signal via Fuzzy Least Squares Support Vector Machines (FLSSVM) is proposed for speech emotion recognition. Based on extracting prosody and voice quality features from emotional speech, FLSSVM is used to construct the optimum separating hyperplane to realize recognizing the four main speech emotion in Chinese including anger, happiness, sadness and surprise. Compared with other present methods of speech emotion recognition, computer simulation results show that FLSSVM can achieve higher average correct rate and better anti-noise recognition effect in different level of signal-to-noise ratios. This demonstrates the efficiency of the proposed FLSSVM method.