Unsupervised Cross-Lingual Speech Emotion Recognition Using Domain Adversarial Neural Network

By using deep learning approaches, Speech Emotion Recognition (SER) on a single domain has achieved many excellent results. However, cross-domain SER is still a challenging task due to the distribution shift between source and target domains. In this work, we propose a Domain Adversarial Neural Network (DANN) based approach to mitigate this distribution shift problem for cross-lingual SER. Specifically, we add a language classifier and gradient reversal layer after the feature extractor to force the learned representation both language-independent and emotion-meaningful. Our method is unsupervised, i. e., labels on target language are not required, which makes it easier to apply our method to other languages. Experimental results show the proposed method provides an average absolute improvement of 3.91% over the baseline system for arousal and valence classification task. Furthermore, we find that batch normalization is beneficial to the performance gain of DANN. Therefore we also explore the effect of different ways of data combination for batch normalization.