A Comprehensive Survey on Multi-modal Conversational Emotion Recognition with Deep Learning

Multi-modal Conversation Emotion Recognition (MCER) aims to recognize and track the speaker’s emotional state using text, speech, and visual information. Compared with traditional single-utterance multi-modal emotion recognition or single-modal conversation emotion recognition, MCER is more challenging. It requires modeling complex emotional interactions and learning consistent and complementary semantics across multiple modalities. Although many deep learning-based approaches have been proposed for MCER, there is still a lack of systematic reviews summarizing existing modeling methods. Therefore, a timely and comprehensive overview of MCER’s recent advances in deep learning is of great significance. In this survey, we provide a comprehensive overview of MCER modeling methods and roughly divide MCER methods into four categories, i.e., context-free modeling, sequential context modeling, speaker-differentiated modeling, and speaker-relationship modeling. Unlike conventional taxonomies based on modality combinations or task-stage decomposition, our framework focuses on how models structurally capture conversational dynamics, speaker roles, and emotional dependencies. In addition, we further discuss MCER’s publicly available popular datasets, multi-modal feature extraction methods, application areas, existing challenges, and future development directions. We hope this review provides valuable insights into the current state of MCER research and inspires the development of more effective models.