Spectral Features Based on Local Hu Moments of Gabor Spectrograms for Speech Emotion Recognition

Huawei TAO  Ruiyu LIANG  Cheng ZHA  Xinran ZHANG  Li ZHAO  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E99-D   No.8   pp.2186-2189
Publication Date: 2016/08/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2015EDL8258
Type of Manuscript: LETTER
Category: Pattern Recognition
Keyword: 
Gabor wavelet,  Hu invariant moment,  speech emotion recognition,  spectral features,  

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Summary: 
To improve the recognition rate of the speech emotion, new spectral features based on local Hu moments of Gabor spectrograms are proposed, denoted by GSLHu-PCA. Firstly, the logarithmic energy spectrum of the emotional speech is computed. Secondly, the Gabor spectrograms are obtained by convoluting logarithmic energy spectrum with Gabor wavelet. Thirdly, Gabor local Hu moments(GLHu) spectrograms are obtained through block Hu strategy, then discrete cosine transform (DCT) is used to eliminate correlation among components of GLHu spectrograms. Fourthly, statistical features are extracted from cepstral coefficients of GLHu spectrograms, then all the statistical features form a feature vector. Finally, principal component analysis (PCA) is used to reduce redundancy of features. The experimental results on EmoDB and ABC databases validate the effectiveness of GSLHu-PCA.