Emotion Recognition in Conversation

Emotion recognition in conversation (ERC) aims to automatically identify the emotions expressed in spoken and written dialogue, leveraging multimodal data (text, audio, video) to build more empathetic and responsive AI systems. Current research heavily utilizes transformer-based models and graph neural networks, often incorporating techniques like multimodal fusion, knowledge distillation, and bias mitigation to improve accuracy and address challenges like weak nonverbal signals and data scarcity. This field is crucial for advancing human-computer interaction, enabling applications such as improved chatbots, mental health monitoring tools, and more effective virtual assistants.