Emotion Classification of Mandarin Speech Based on TEO Nonlinear Features

Abstract

To study effective speech features which can represent different emotion styles in mandarin speech, nonlinear features based on Teager Energy Operator(TEO) are researched. Neutral state and 3 emotional states (i.e. happiness, anger and sadness) are classified from the mandarin speech database. MFCC extraction and HMM-based emotion recognition are used as baseline system to evaluate the emotional classification performance of TEO-based features. In comparison with MFCC, while text- dependent, improvements of classification capacity are obtained when using all 4 nonlinear features (i.e. NFD_Mel, AF_Mel, DAF_Mel, AM_SBCC). While text-independent, the performance of emotion classification are improved by using NFD_Mel, AF_Mel and DAF_Mel, but deteriorated by using AM_SBCC. The results of classification demonstrate that the nonlinear features based on TEO, when using NFD_Mel, AF_Mel and DAF_Mel, are better able to represent different emotion styles in speech than that of MFCC.