Gabor Block Spectrum Features based on Local Binary Pattern for Speech Emotion Recognition

Spectral features play an important role in speech emotion recognition. However, the existing spectral features are still not fully expressed emotion information in spectrum. In order to study the relationship between speech emotion and spectrum, a new feature called Gabor block spectrum features based on local binary pattern (GBLBP) is proposed in this paper. Firstly, the logarithmic energy spectrum of emotion speech is obtained. Then, the Gabor spectrums are obtained through computing convolution between Gabor wavelet with logarithmic energy spectrum. Thirdly, the local energy distribution information of each block from Gabor spectrums is extracted by using local binary pattern. Finally, the GBLBP features are obtained by jointing all the features from Gabor spectrums. The experimental results on Berlin database shows that the average weighted recall rate of GBLBP 9% higher than that of MFCC, and the recognition performance is significantly better than most of spectral features, and has a better fusion performance with the existing acoustic features.