Domain-Invariant Feature Learning for Cross Corpus Speech Emotion Recognition

To deal with speech emotion recognition (SER) in real-life applications, researchers have to focus on cross corpus SER, where the feature distribution of source and target datasets are different. In this paper, we propose an efficient domain adversarial training method to cope with the non-affective information during feature extraction. Through the proposed domain-adversarial learning, we can reduce the domain divergence between train and test data. Furthermore, we incorporate center loss with the emotion classifier to reduce the intra-class variation of features learned from the same emotion. We conduct experiments on four emotional benchmark datasets to verify the performance of the proposed method. The experimental results demonstrate that our proposed model outperform the baseline system in both cross-corpus and multi-corpus evaluation.