The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Untuk pengecaman emosi pertuturan rentas korpus (SER), cara mendapatkan perwakilan ciri yang berkesan untuk penghapusan percanggahan pengedaran ciri antara domain sumber dan sasaran adalah isu penting. Dalam kertas kerja ini, kami mencadangkan kaedah Pembelajaran Subruang Sasaran (TaSL) disesuaikan untuk SER silang korpus. Kaedah TaSL cuba mencari subruang unjuran, di mana ciri itu mengundurkan label dengan lebih tepat dan jurang pengedaran ciri dalam domain sasaran dan sumber dirapatkan dengan berkesan. Kemudian, untuk mendapatkan matriks unjuran yang lebih optimum, ℓ1 norma dan ℓ2,1 terma penalti norma masing-masing ditambahkan pada syarat regularisasi yang berbeza. Akhir sekali, kami menjalankan eksperimen yang meluas pada tiga korpus awam, EmoDB, eNTERFACE dan AFEW 4.0. Keputusan eksperimen menunjukkan bahawa kaedah yang dicadangkan kami boleh mencapai prestasi yang lebih baik berbanding dengan kaedah terkini dalam tugasan SER silang korpus.
Xiuzhen CHEN
Nanjing University of Information Science and Technology
Xiaoyan ZHOU
Nanjing University of Information Science and Technology
Cheng LU
Southeast University
Yuan ZONG
Southeast University
Wenming ZHENG
Southeast University
Chuangao TANG
Southeast University
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Salinan
Xiuzhen CHEN, Xiaoyan ZHOU, Cheng LU, Yuan ZONG, Wenming ZHENG, Chuangao TANG, "Target-Adapted Subspace Learning for Cross-Corpus Speech Emotion Recognition" in IEICE TRANSACTIONS on Information,
vol. E102-D, no. 12, pp. 2632-2636, December 2019, doi: 10.1587/transinf.2019EDL8038.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2019EDL8038/_p
Salinan
@ARTICLE{e102-d_12_2632,
author={Xiuzhen CHEN, Xiaoyan ZHOU, Cheng LU, Yuan ZONG, Wenming ZHENG, Chuangao TANG, },
journal={IEICE TRANSACTIONS on Information},
title={Target-Adapted Subspace Learning for Cross-Corpus Speech Emotion Recognition},
year={2019},
volume={E102-D},
number={12},
pages={2632-2636},
abstract={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.},
keywords={},
doi={10.1587/transinf.2019EDL8038},
ISSN={1745-1361},
month={December},}
Salinan
TY - JOUR
TI - Target-Adapted Subspace Learning for Cross-Corpus Speech Emotion Recognition
T2 - IEICE TRANSACTIONS on Information
SP - 2632
EP - 2636
AU - Xiuzhen CHEN
AU - Xiaoyan ZHOU
AU - Cheng LU
AU - Yuan ZONG
AU - Wenming ZHENG
AU - Chuangao TANG
PY - 2019
DO - 10.1587/transinf.2019EDL8038
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E102-D
IS - 12
JA - IEICE TRANSACTIONS on Information
Y1 - December 2019
AB - 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.
ER -