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