Speech Emotion Recognition Using RA-Gmlp Model on Time–Frequency Domain Features Extracted by TFCM

Abstract

Speech emotion recognition (SER) is a key branch in the field of artificial intelligence, focusing on the analysis and understanding of emotional content in human speech. It involves a multidisciplinary knowledge of acoustics, phonetics, linguistics, pattern recognition, and neurobiology, aiming to establish a connection between human speech and emotional expression. This technology has shown broad application prospects in the medical, educational, and customer service fields. With the evolution of deep learning and neural network technologies, SER research has shifted from relying on manually designed low-level descriptors (LLDs) to utilizing complex neural network models for extracting high-dimensional features. A perennial challenge for researchers has been how to comprehensively capture the rich emotional features. Given that emotional information is present in both time and frequency domains, our study introduces a novel time–frequency domain convolution module (TFCM) based on Mel-frequency cepstral coefficient (MFCC) features to deeply mine the time–frequency information of MFCCs. In the deep feature extraction phase, for the first time, we have introduced hybrid dilated convolution (HDC) into the SER field, significantly expanding the receptive field of neurons, thereby enhancing feature richness and diversity. Furthermore, we innovatively propose the residual attention-gated multilayer perceptron (RA-GMLP) structure, which combines the global feature recognition ability of GMLP with the concentrated weighting function of the multihead attention mechanism, effectively focusing on the key emotional information within the speech sequence. Through extensive experimental validation, we have demonstrated that TFCM, HDC, and RA-GMLP surpass existing advanced technologies in enhancing the accuracy of SER tasks, fully showcasing the powerful advantages of the modules we proposed.