A speech emotion recognition algorithm based on DN-ResNet11

To address the high complexity of network training and improve speech emotion feature extraction, a dual-branch feature extraction model based on DN-ResNet11 and a channel attention residual network (CRNet) was proposed. Firstly, the low-complexity DN-ResNet11 was designed to efficiently extract fused emotional features from spectrograms, enhancing emotion recognition accuracy. Secondly, multiscale guided filtering and the local binary pattern (LBP) algorithm were incorporated to enhance spectrogram details. Finally, the two sets of features were fused for emotion classification, forming a dual-branch weighted fusion model (weighted fusion model based on dual nested residual and channel residual network, WFDN_CRNet), further enhancing emotional representation ability. Experiments on the CASIA, EMO-DB, and IEMOCAP speech emotion datasets show emotion recognition rates of 94.58%, 85.59%, and 65.72%, respectively. The proposed method not only achieves superior emotion recognition rates compared to baseline models such as ResNet18, but also significantly reduces computational cost, demonstrating the model’s effectiveness.