Speech emotion recognition based on emotion perception

Speech emotion recognition (SER) is a hot topic in speech signal processing. With the advanced development of the cheap computing power and proliferation of research in data-driven methods, deep learning approaches are prominent solutions to SER nowadays. SER is a challenging task due to the scarcity of datasets and the lack of emotion perception. Most existing networks of SER are based on computer vision and natural language processing, so the applicability for extracting emotion is not strong. Drawing on the research results of brain science on emotion computing and inspired by the emotional perceptive process of the human brain, we propose an approach based on emotional perception, which designs a human-like implicit emotional attribute classification and introduces implicit emotional information through multi-task learning. Preliminary experiments show that the unweighted accuracy (UA) of the proposed method has increased by 2.44%, and weighted accuracy (WA) 3.18% (both absolute values) on the Interactive Emotional Dyadic Motion Capture (IEMOCAP) dataset, which verifies the effectiveness of our method.