arXiv 2505.205112025-09-09cs.CL

对话中的多模态情感识别：方法、趋势、挑战与前景综述

Multimodal Emotion Recognition in Conversations: A Survey of Methods, Trends, Challenges and Prospects

Guangdong Provincial Key Laboratory of Quantum Engineering and Quantum Materials（广东省量子工程与量子材料重点实验室）；School of Electronic Science and Engineering (School of Microelectronics), South China Normal University（华南师范大学电子科学学院（微电子学院））；North Carolina Central University（北卡罗来纳中央大学）；Georgia Institute of Technology（佐治亚理工学院）；Columbia University（哥伦比亚大学）；LMU Munich & Munich Center for Machine Learning（慕尼黑大学及慕尼黑机器学习中心）

Chengyan Wu, Yiqiang Cai, Yang Liu, Pengxu Zhu, Yun Xue, Ziwei Gong, Julia Hirschberg, Bolei Ma

备注：: EMNLP 2025 Findings

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AI总结：

本文系统综述对话中的多模态情感识别（MERC），涵盖动机、任务、方法、评估、趋势与挑战，为情感智能研究提供指导。

摘要：

虽然基于文本的情感识别方法已取得显著成功，但现实世界中的对话系统往往需要比任何单一模态所能提供的更为细致的情感理解。因此，对话中的多模态情感识别（MERC）已成为提升人机交互自然性和情感理解能力的关键方向。其目标是通过整合文本、语音和视觉信号等多种模态的信息来准确识别情感。本综述对MERC进行了系统性概述，包括其动机、核心任务、代表性方法和评估策略。我们进一步审视了近期趋势，强调了关键挑战，并展望了未来方向。随着人们对情感智能系统的兴趣日益增长，本综述为推进MERC研究提供了及时的指导。

英文摘要：

While text-based emotion recognition methods have achieved notable success, real-world dialogue systems often demand a more nuanced emotional understanding than any single modality can offer. Multimodal Emotion Recognition in Conversations (MERC) has thus emerged as a crucial direction for enhancing the naturalness and emotional understanding of human-computer interaction. Its goal is to accurately recognize emotions by integrating information from various modalities such as text, speech, and visual signals. This survey offers a systematic overview of MERC, including its motivations, core tasks, representative methods, and evaluation strategies. We further examine recent trends, highlight key challenges, and outline future directions. As interest in emotionally intelligent systems grows, this survey provides timely guidance for advancing MERC research.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。