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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 Publicized: 2016/05/06 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,
Full Text: PDF(752.7KB)>>
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.
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