Improving speech emotion recognition by fusing self-supervised learning and spectral features via mixture of experts

Speech Emotion Recognition (SER) is an important area of research in speech processing that aims to identify and classify emotional states conveyed through speech signals. Recent studies have shown considerable performance in SER by exploiting deep contextualized speech representations from self-supervised learning (SSL) models. However, SSL models pre-trained on clean speech data may not perform well on emotional speech data due to the domain shift problem. To address this problem, this paper proposes a novel approach that simultaneously exploits an SSL model and a domain-agnostic spectral feature (SF) through the Mixture of Experts (MoE) technique. The proposed approach achieves the state-of-the-art performance on weighted accuracy compared to other methods in the IEMOCAP dataset. Moreover, this paper demonstrates the existence of the domain shift problem of SSL models in the SER task.