Speech emotion recognition using emotion perception spectral feature

Speech emotion recognition is an important technique for human-computer interface applications. Due to contain rich information of emotion, the spectral feature is widely used for emotion recognition. However, the recognition performance is limited because of imprecise extracted rule and uncertain size of resolution of spectral feature. To address this issue, motivated by speech coding, we introduced psychoacoustics model, provided a perception spectral subband partition method for obtaining more precise frequency resolution. Moreover, we also provided a new spectral feature on the divided subband frequency signals. The proposed feature includes emotional perception entropy, spectral inclination, and spectral flatness. Then, a Support Vector Machine classifier is used to recognize emotion categories. The experiment results show that the proposed spectral feature is superior to the traditional MFCC feature, and also better than the state-of-the-art Fourier feature and multi-resolution amplitude feature.