IEICE TRANSACTIONS on Information
Spectral Features Based on Local Hu Moments of Gabor Spectrograms for Speech Emotion Recognition
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.
- Publication
- IEICE TRANSACTIONS on Information 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
Authors
Huawei TAO
Southeast University
Ruiyu LIANG
Nanjing Institute of Technology
Cheng ZHA
Southeast University
Xinran ZHANG
Southeast University
Li ZHAO
Southeast University
Keyword
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Huawei TAO, Ruiyu LIANG, Cheng ZHA, Xinran ZHANG, Li ZHAO, "Spectral Features Based on Local Hu Moments of Gabor Spectrograms for Speech Emotion Recognition" in IEICE TRANSACTIONS on Information,
vol. E99-D, no. 8, pp. 2186-2189, August 2016, doi: 10.1587/transinf.2015EDL8258.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2015EDL8258/_p
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@ARTICLE{e99-d_8_2186,
author={Huawei TAO, Ruiyu LIANG, Cheng ZHA, Xinran ZHANG, Li ZHAO, },
journal={IEICE TRANSACTIONS on Information},
title={Spectral Features Based on Local Hu Moments of Gabor Spectrograms for Speech Emotion Recognition},
year={2016},
volume={E99-D},
number={8},
pages={2186-2189},
abstract={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.},
keywords={},
doi={10.1587/transinf.2015EDL8258},
ISSN={1745-1361},
month={August},}
Copy
TY - JOUR
TI - Spectral Features Based on Local Hu Moments of Gabor Spectrograms for Speech Emotion Recognition
T2 - IEICE TRANSACTIONS on Information
SP - 2186
EP - 2189
AU - Huawei TAO
AU - Ruiyu LIANG
AU - Cheng ZHA
AU - Xinran ZHANG
AU - Li ZHAO
PY - 2016
DO - 10.1587/transinf.2015EDL8258
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E99-D
IS - 8
JA - IEICE TRANSACTIONS on Information
Y1 - August 2016
AB - 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.
ER -
English
