面向语音情感识别的Gabor分块局部二值模式特征

谱特征在语音情感识别中起到了重要的作用，然而现有的谱特征仍未能充分表达谱图中的语音情感信息.为研究语音情感与谱图之间的联系,提出了一种面向语音情感识别的Gabor分块局部二值模式特征（GBLBP）。首先，获取情感语音的对数能量谱；然后，采用多尺度，多方向的Gabor小波对对数能量谱进行处理，得到Gabor谱图；再次，对每张Gabor谱图进行分块，采用局部二值模式提取每个块的局部能量分布信息；最后，将提取到的所有特征级联，得到GBLBP特征。Berlin库上的实验结果表明:GBLBP特征的平均加权召回率比MFCC高了9%，识别性能显著优于众多谱特征，且与现有声学特征有较好的融合性。 

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.