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Speech Emotion Recognition (SER) has become a growing focus of research inhuman-computer interaction. Spatiotemporal features play a crucial role in SER,yet current research lacks comprehensive spatiotemporal feature learning. Thispaper focuses on addressing this gap by proposing a novel approach. In thispaper, we employ Convolutional Neural Network (CNN) with varying kernel sizesfor spatial and temporal feature extraction. Additionally, we introduceSqueeze-and-Excitation (SE) modules to capture and fuse multi-scale features,facilitating effective information fusion for improved emotion recognition anda deeper understanding of the temporal evolution of speech emotion. Moreover,we employ skip connections and Spatial Dropout (SD) layers to preventoverfitting and increase the model's depth. Our method outperforms the previousstate-of-the-art method, achieving an average UAR and WAR improvement of 1.62%and 1.32%, respectively, across six benchmark SER datasets. Further experimentsdemonstrated that our method can fully extract spatiotemporal features inlow-resource conditions.

Ms-senet: Enhancing Speech Emotion Recognition Through Multi-scale Feature Fusion With Squeeze-and-excitation Blocks