  Emotion Recognition in Conversation (ERC) plays a crucial role in enabling
dialogue systems to effectively respond to user requests. The emotions in a
conversation can be identified by the representations from various modalities,
such as audio, visual, and text. However, due to the weak contribution of
non-verbal modalities to recognize emotions, multimodal ERC has always been
considered a challenging task. In this paper, we propose Teacher-leading
Multimodal fusion network for ERC (TelME). TelME incorporates cross-modal
knowledge distillation to transfer information from a language model acting as
the teacher to the non-verbal students, thereby optimizing the efficacy of the
weak modalities. We then combine multimodal features using a shifting fusion
approach in which student networks support the teacher. TelME achieves
state-of-the-art performance in MELD, a multi-speaker conversation dataset for
ERC. Finally, we demonstrate the effectiveness of our components through
additional experiments.
