Cross-Corpus Speech Emotion Recognition Based on Attention-Driven Feature Refinement and Spatial Reconstruction

In cross-corpus scenarios, inappropriate feature-processing methods tend to cause the loss of key emotional information. Additionally, deep neural networks contain substantial redundancy, which triggers domain shift issues and impairs the generalization ability of emotion recognition systems. To address these challenges, this study proposes a cross-corpus speech emotion recognition model based on attention-driven feature refinement and spatial reconstruction. Specifically, the proposed approach consists of three key components: first, an autoencoder integrated with a multi-head attention mechanism to enhance the model’s ability to focus on the emotional components of acoustic features during the feature compression process of the autoencoder network; second, a feature refinement and spatial reconstruction module designed to further improve the extraction of emotional features, with a gating mechanism employed to optimize the feature reconstruction process; finally, the Charbonnier loss function adopted as the loss metric during training to minimize the difference between features from the source domain and target domain, thereby enhancing the cross-domain robustness of the model. Experimental results demonstrated that the proposed method achieved an average recognition accuracy of 46.75% across six sets of cross-corpus experiments, representing an improvement of 4.17% to 14.33% compared with traditional domain adaptation methods.