Speech Emotion Recognition Based on Sparse Transfer Learning Method
Summary :
In traditional speech emotion recognition systems, when the training and testing utterances are obtained from different corpora, the recognition rates will decrease dramatically. To tackle this problem, in this letter, inspired from the recent developments of sparse coding and transfer learning, a novel sparse transfer learning method is presented for speech emotion recognition. Firstly, a sparse coding algorithm is employed to learn a robust sparse representation of emotional features. Then, a novel sparse transfer learning approach is presented, where the distance between the feature distributions of source and target datasets is considered and used to regularize the objective function of sparse coding. The experimental results demonstrate that, compared with the automatic recognition approach, the proposed method achieves promising improvements on recognition rates and significantly outperforms the classic dimension reduction based transfer learning approach.
- Publication
- IEICE TRANSACTIONS on Information Vol.E98-D No.7 pp.1409-1412
- Publication Date
- 2015/07/01
- Publicized
- 2015/04/10
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2015EDL8028
- Type of Manuscript
- LETTER
- Category
- Speech and Hearing
Authors
Peng SONG
Yantai University
Wenming ZHENG
Southeast University
Ruiyu LIANG
Nanjing Institute of Technology
Keyword
Latest Issue
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Peng SONG, Wenming ZHENG, Ruiyu LIANG, "Speech Emotion Recognition Based on Sparse Transfer Learning Method" in IEICE TRANSACTIONS on Information,
vol. E98-D, no. 7, pp. 1409-1412, July 2015, doi: 10.1587/transinf.2015EDL8028.
Abstract: In traditional speech emotion recognition systems, when the training and testing utterances are obtained from different corpora, the recognition rates will decrease dramatically. To tackle this problem, in this letter, inspired from the recent developments of sparse coding and transfer learning, a novel sparse transfer learning method is presented for speech emotion recognition. Firstly, a sparse coding algorithm is employed to learn a robust sparse representation of emotional features. Then, a novel sparse transfer learning approach is presented, where the distance between the feature distributions of source and target datasets is considered and used to regularize the objective function of sparse coding. The experimental results demonstrate that, compared with the automatic recognition approach, the proposed method achieves promising improvements on recognition rates and significantly outperforms the classic dimension reduction based transfer learning approach.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2015EDL8028/_p
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@ARTICLE{e98-d_7_1409,
author={Peng SONG, Wenming ZHENG, Ruiyu LIANG, },
journal={IEICE TRANSACTIONS on Information},
title={Speech Emotion Recognition Based on Sparse Transfer Learning Method},
year={2015},
volume={E98-D},
number={7},
pages={1409-1412},
abstract={In traditional speech emotion recognition systems, when the training and testing utterances are obtained from different corpora, the recognition rates will decrease dramatically. To tackle this problem, in this letter, inspired from the recent developments of sparse coding and transfer learning, a novel sparse transfer learning method is presented for speech emotion recognition. Firstly, a sparse coding algorithm is employed to learn a robust sparse representation of emotional features. Then, a novel sparse transfer learning approach is presented, where the distance between the feature distributions of source and target datasets is considered and used to regularize the objective function of sparse coding. The experimental results demonstrate that, compared with the automatic recognition approach, the proposed method achieves promising improvements on recognition rates and significantly outperforms the classic dimension reduction based transfer learning approach.},
keywords={},
doi={10.1587/transinf.2015EDL8028},
ISSN={1745-1361},
month={July},}
Copy
TY - JOUR
TI - Speech Emotion Recognition Based on Sparse Transfer Learning Method
T2 - IEICE TRANSACTIONS on Information
SP - 1409
EP - 1412
AU - Peng SONG
AU - Wenming ZHENG
AU - Ruiyu LIANG
PY - 2015
DO - 10.1587/transinf.2015EDL8028
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E98-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2015
AB - In traditional speech emotion recognition systems, when the training and testing utterances are obtained from different corpora, the recognition rates will decrease dramatically. To tackle this problem, in this letter, inspired from the recent developments of sparse coding and transfer learning, a novel sparse transfer learning method is presented for speech emotion recognition. Firstly, a sparse coding algorithm is employed to learn a robust sparse representation of emotional features. Then, a novel sparse transfer learning approach is presented, where the distance between the feature distributions of source and target datasets is considered and used to regularize the objective function of sparse coding. The experimental results demonstrate that, compared with the automatic recognition approach, the proposed method achieves promising improvements on recognition rates and significantly outperforms the classic dimension reduction based transfer learning approach.
ER -
English
