For Full-Text PDF, please login, if you are a member of IEICE,|
or go to Pay Per View on menu list, if you are a nonmember of IEICE.
Target-Adapted Subspace Learning for Cross-Corpus Speech Emotion Recognition
Xiuzhen CHEN Xiaoyan ZHOU Cheng LU Yuan ZONG Wenming ZHENG Chuangao TANG
IEICE TRANSACTIONS on Information and Systems
Publication Date: 2019/12/01
Online ISSN: 1745-1361
Type of Manuscript: LETTER
Category: Speech and Hearing
cross-corpus speech emotion recognition, transfer learning, domain adaptation, subspace learning,
Full Text: PDF>>
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, ℓ1 norm and ℓ2,1 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.