  Speech emotion recognition is crucial to human-computer interaction. The
temporal regions that represent different emotions scatter in different parts
of the speech locally. Moreover, the temporal scales of important information
may vary over a large range within and across speech segments. Although
transformer-based models have made progress in this field, the existing models
could not precisely locate important regions at different temporal scales. To
address the issue, we propose Dynamic Window transFormer (DWFormer), a new
architecture that leverages temporal importance by dynamically splitting
samples into windows. Self-attention mechanism is applied within windows for
capturing temporal important information locally in a fine-grained way.
Cross-window information interaction is also taken into account for global
communication. DWFormer is evaluated on both the IEMOCAP and the MELD datasets.
Experimental results show that the proposed model achieves better performance
than the previous state-of-the-art methods.
