Adaptive Domain-Aware Representation Learning for Speech Emotion Recognition

Speech emotion recognition is a crucial part in human-computer interaction. However, representation learning is challenging due to much variability from speech emotion signals across diverse domains, such as gender, age, languages, and social cultural context. Many approaches focus on domain-invariant representation learning which loses the domain-specific knowledge and results in unsatisfactory speech emotion recognition across domains. In this paper, we propose an adaptive domain-aware representation learning that leverages the domain knowledge to extract domain aware features. The proposed approach applies attention model on frequency to embed the domain knowledge in the emotion representation space. Experiments demonstrate that our approach on IEMOCAP achieves the state-of-the-art performance under the same experimental conditions with WA of 73.02% and UA of 65.86%.