LGFormer: A Local-Global Dynamic Attention Window Transformer for Speech Emotion Recognition

Speech emotion recognition is important in intelligent human-computer interaction, but modeling to handle long-range dependencies and local emotional cues remains challenging. This paper proposes a Local-Global Dynamic Attention Window-based Transformer model (LGFormer). The Local module dynamically divides the window based on temporal significance, capturing fine-grained localized information and synthesizing features using the Global module. The model introduces a novel attention mechanism to optimize computational efficiency, making it particularly suitable for scenarios with limited computational resources. We evaluate the method on the IEMOCAP and MELD datasets, achieving 2.8% and 1.1% improvements in weighted accuracy and F1 score, respectively. Comparison experiments with multiple benchmark algorithms validate the effectiveness of the model.