Speech Emotion Recognition Based on Sparse Transfer Learning Method

Peng SONG  Wenming ZHENG  Ruiyu LIANG  

Publication
IEICE TRANSACTIONS on Information and Systems   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
Keyword: 
speech emotion recognition,  sparse coding,  transfer learning,  

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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.