Multi-channel Convolution Classroom Speech Emotion Recognition Model Based on Improved Attention Mechanism
LIANG Kejin
ZHANG Haijun
Abstract：Aiming at the situation that increasing the depth and width of the network does not significantly improve the recogni-tion accuracy in speech emotion recognition research,the dual-channel attention mechanism is improved.By combining the chan-nel attention mechanism and the spatial attention mechanism,the spatial attention mechanism is combined.The convolution part is improved to a two-layer convolution in order to extract more valuable contextual semantic information.For a single emotional feature cannot effectively represent speech emotion,multiple single emotional features are fused to increase the emotional representation ability of the feature.The model obtained a recognition accuracy of 85.24%under the Chinese Affective Database of the Institute of Automation,Chinese Academy of Sciences(CASIA),and a recognition accuracy of 86.58%on the Emo-DB dataset,proving the effectiveness of the model.For the real classroom speech data,recall rate,F1 value and accuracy rate of the model in the experi-ment reached 77.77%,80.76%,and 79.24%,respectively,reflecting good practicability.
Keywords：emotion recognitiondeep learningspeech emotion recognitionneural networkunbalanced dataset
Publication Date：2024-09-20
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 2645-2650 )