Unsupervised Cross-Lingual Speech Emotion Recognition Using DomainAdversarial Neural Network

• URL: http://arxiv.org/abs/2012.11174v1
• Date: Mon, 21 Dec 2020 08:21:11 GMT
• Title: Unsupervised Cross-Lingual Speech Emotion Recognition Using DomainAdversarial Neural Network
• Authors: Xiong Cai, Zhiyong Wu, Kuo Zhong, Bin Su, Dongyang Dai, Helen Meng
• Abstract summary: Cross-domain Speech Emotion Recog-nition (SER) is still a challenging taskdue to the distribution shift between source and target domains. We propose a Domain Adversarial Neural Net-work (DANN) based approach to mitigate this distribution shiftproblem for cross-lingual SER.
• Score: 48.1535353007371
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: By using deep learning approaches, Speech Emotion Recog-nition (SER) on a single domain has achieved many excellentresults. However, cross-domain SER is still a challenging taskdue to the distribution shift between source and target domains.In this work, we propose a Domain Adversarial Neural Net-work (DANN) based approach to mitigate this distribution shiftproblem for cross-lingual SER. Specifically, we add a languageclassifier and gradient reversal layer after the feature extractor toforce the learned representation both language-independent andemotion-meaningful. Our method is unsupervised, i. e., labelson target language are not required, which makes it easier to ap-ply our method to other languages. Experimental results showthe proposed method provides an average absolute improve-ment of 3.91% over the baseline system for arousal and valenceclassification task. Furthermore, we find that batch normaliza-tion is beneficial to the performance gain of DANN. Thereforewe also explore the effect of different ways of data combinationfor batch normalization.

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