A Low-rank Matching Attention based Cross-modal Feature Fusion Method for Conversational Emotion Recognition

Understand

Conversational emotion recognition (CER) is an important research topic in human-computer interactions.

• {Although recent advancements in transformer-based cross-modal fusion methods have shown promise in CER tasks, they tend to overlook the crucial intra-modal and inter-modal emotional interaction or suffer from high computational complexity.
• To address this, we introduce a novel and lightweight cross-modal feature fusion method called Low-Rank Matching Attention Method (LMAM).
• LMAM effectively captures contextual emotional semantic information in conversations while mitigating the quadratic complexity issue caused by the self-attention mechanism.