Speech Emotion Recognition Using Transfer Learning

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概要

• 論文の詳細を見る

• A major challenge for speech emotion recognition is that when the training and deployment conditions do not use the same speech corpus, the recognition rates will obviously drop. Transfer learning, which has successfully addressed the cross-domain classification or recognition problem, is presented for cross-corpus speech emotion recognition. First, by using the maximum mean discrepancy embedding (MMDE) optimization and dimension reduction algorithms, two close low-dimensional feature spaces are obtained for source and target speech corpora, respectively. Then, a classifier function is trained using the learned low-dimensional features in the labeled source corpus, and directly applied to the unlabeled target corpus for emotion label recognition. Experimental results demonstrate that the transfer learning method can significantly outperform the traditional automatic recognition technique for cross-corpus speech emotion recognition.

• The Institute of Electronics, Information and Communication Engineersの論文

著者

• Zhao Li

  Key Laboratory of Underwater Acoustic Signal Processing of Ministry of Education, School of Information Science and Engineering, Southeast University, Nanjing, Jiangsu 210096, P. R. China

• SONG Peng

  Key Laboratory of Underwater Acoustic Signal Processing of Ministry of Education, School of Information Science and Engineering, Southeast University

• JIN Yun

  School of Physics and Electronic Engineering, Jiangsu Normal University

• XIN Minghai

  Key Laboratory of Child Development and Learning Science of Ministry of Education, Southeast University

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