Learning emotion-discriminative and domain-invariant features for domain adaptation in speech emotion recognition

Conventional approaches for Speech Emotion Recognition (SER) usually assume that the feature distributions between training and test set are identical. However, this assumption does not hold in many real scenarios. Although many Domain Adaptation (DA) methods have been proposed to solve this problem, the conventional emotion discriminative information is ignored. In this paper, we propose a DA based method called Emotion-discriminative and Domain-invariant Feature Learning Method (EDFLM) for SER, in which both the domain divergence and emotion discrimination are considered to learn emotion-discriminative and domain-invariant features by using emotion label constraint and domain label constraint. Furthermore, to disentangle the emotion-related factors from the emotion-unrelated factors, we introduce an orthogonal term to encourage the input to be disentangled into two blocks: emotion-related and emotion-unrelated features. Our method can learn emotion-discriminative and domain-invariant features through a back propagation network which uses the acoustic features of INTERSPEECH 2009 Emotion Challenge as the input rather than raw speech signals. Experiments on the INTERSPEECH 2009 Emotion Challenge two-class task show that the performance of our method is superior to other state-of-the-arts methods.