Speech Emotion Recognition using Channel Attention Mechanism
2023 4th International Conference on Computer Engineering and Application (ICCEA)(2023)
摘要
In order to improve the accuracy of speech emotion recognition, this paper proposes a speech emotion recognition method based on the channel attention mechanism. Firstly, Mel Frequency Ceptral Coefficient(MFCC), speech spectrograms and spectral envelopes are selected as the initial input features; then, multiple depth network models are used to extract feature maps from different angles in parallel; then, weights are assigned and fused to the feature maps output from each sub-depth network model by the channel attention mechanism; finally, the fused feature maps are used to predict emotion categories. The experimental results on CASIA, Emo-DB, and SAVEE emotion datasets show that the method achieves 88.3
%
, 85.1%, and 64.5% recognition accuracy, respectively, with better recognition performance compared to recent comparative literature models.
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关键词
component,emotion recognition,channel attention,feature fusion,feature extraction
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