Target-Adapted Subspace Learning for Cross-Corpus Speech Emotion Recognition

Xiuzhen CHEN  Xiaoyan ZHOU  Cheng LU  Yuan ZONG  Wenming ZHENG  Chuangao TANG  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E102-D   No.12   pp.2632-2636
Publication Date: 2019/12/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2019EDL8038
Type of Manuscript: LETTER
Category: Speech and Hearing
Keyword: 
cross-corpus speech emotion recognition,  transfer learning,  domain adaptation,  subspace learning,  

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Summary: 
For cross-corpus speech emotion recognition (SER), how to obtain effective feature representation for the discrepancy elimination of feature distributions between source and target domains is a crucial issue. In this paper, we propose a Target-adapted Subspace Learning (TaSL) method for cross-corpus SER. The TaSL method trys to find a projection subspace, where the feature regress the label more accurately and the gap of feature distributions in target and source domains is bridged effectively. Then, in order to obtain more optimal projection matrix, 1 norm and 2,1 norm penalty terms are added to different regularization terms, respectively. Finally, we conduct extensive experiments on three public corpuses, EmoDB, eNTERFACE and AFEW 4.0. The experimental results show that our proposed method can achieve better performance compared with the state-of-the-art methods in the cross-corpus SER tasks.