Conversational Emotion Recognition

Conversational emotion recognition (CER) aims to automatically identify the emotions expressed within a conversation, leveraging various modalities like text, speech, and visual cues. Current research heavily emphasizes incorporating contextual information, including speaker emotional states and conversation topics, often using graph neural networks or diffusion models to capture complex interactions between utterances and modalities. These advancements are crucial for improving human-computer interaction, enabling more empathetic and emotionally intelligent systems in applications ranging from mental health support to personalized recommendations.