Fusing Large Language Models with Cross-Domain Graphs for Multimodal Emotion Recognition in Dialogue

HUANG Chen; MA Haobo; ZHANG Yan; YANG Chao; SONG Jianhua

doi:10.12263/DZXB.20250772

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Fusing Large Language Models with Cross-Domain Graphs for Multimodal Emotion Recognition in Dialogue

PAPERS | 更新时间：2026-06-22

- Fusing Large Language Models with Cross-Domain Graphs for Multimodal Emotion Recognition in Dialogue
- ACTA ELECTRONICA SINICA Vol. 54, Issue 1, Pages: 340-351(2026)
- 作者机构：
  
  1.湖北大学计算机学院，湖北武汉 430062
  2.智能感知系统与安全教育部重点实验室，湖北武汉 430062
  3.大数据智能分析与行业应用湖北省重点实验室，湖北武汉 430062
  4.湖北省高校人文社科重点研究基地-绩效评价信息管理研究中心，湖北武汉 430062
  5.湖北大学网络空间安全学院，湖北武汉 430062
- 作者简介：
- 基金信息：
  
  Major Project of Hubei Province (JD)(2023BAA018);Major Project of Hubei Province (JD)(2024BAA008)
- DOI：10.12263/DZXB.20250772
  CLC： TP391;TP399
- Received：05 September 2025，
  
  Accepted：08 January 2026，
  
  Published：25 January 2026
- 稿件说明：
移动端阅览
黄辰, 马浩博, 张龑, 等. 融合大语言模型与跨域图结构的多模态对话情感识别方法[J]. 电子学报, 2026, 54(01): 340-351.

HUANG Chen, MA Haobo, ZHANG Yan, et al. Fusing Large Language Models with Cross-Domain Graphs for Multimodal Emotion Recognition in Dialogue[J]. Acta Electronica Sinica, 2026, 54(01): 340-351.
黄辰, 马浩博, 张龑, 等. 融合大语言模型与跨域图结构的多模态对话情感识别方法[J]. 电子学报, 2026, 54(01): 340-351. DOI：10.12263/DZXB.20250772

HUANG Chen, MA Haobo, ZHANG Yan, et al. Fusing Large Language Models with Cross-Domain Graphs for Multimodal Emotion Recognition in Dialogue[J]. Acta Electronica Sinica, 2026, 54(01): 340-351. DOI：10.12263/DZXB.20250772

摘要

多模态对话情感识别（Multimodal Emotion Recognition in Conversation， MERC）通过融合文本、语音、视觉等多模态信息来识别对话中的情感状态。随着对话式人工智能和情感计算的快速发展，MERC成为情感计算和人机交互领域的研究热点。相比传统单一模态情感识别，多模态方法能够更全面、精确地捕捉情感的多维特征，如文本传递显性情感内容，语音提供音调、语速等隐性情感线索，视觉信息（如面部表情）则反映情感的非语言表现。这些模态信息相互补充，有助于提高情感识别的准确性和鲁棒性。然而，多模态情感识别面临诸多挑战：首先，不同模态的数据在信息表示上存在显著差异，传统的特征拼接或加权平均方法无法充分捕捉模态间复杂的交互关系，容易导致信息丢失；其次，情感识别任务常常遭遇局部噪声和离群样本干扰，影响模型稳定性；最后，情感识别的准确性与对话上下文的综合利用密切相关，情感往往受到前后文的影响，因此，如何有效提取和利用上下文信息是提高准确性的一大挑战。为应对这些问题，本文结合大语言模型（Large Language Model，LLM）与全局-局部跨域图结构，提出了LLM-EmoGraph方法，旨在实现多模态数据的精确融合与高效建模。该方法引入多模态掩码机制来处理不同模态之间的缺失和不一致信息，确保模型在信息不完整时依然保持较好性能。通过大规模跨域多图预训练，LLM-EmoGraph提升了多模态间及图结构间的迁移能力，增强了模型的鲁棒性。其创新的自适应双尺度特征融合策略实现了文本、语音和视觉信息的高效对齐，提升了情感识别精度，尤其在多模态高度交互的情境下表现优异。此外，结合大语言模型的弱监督层次化情感分类方案，通过逐层引导情感信息提取，有效避免了全局情感模式的干扰，使得即使在有限标注数据下，模型也能准确学习情感特征。实验结果表明，LLM-EmoGraph在多个基准数据集上显著超越现有主流方法，验证了其在多模态情感识别中的有效性和先进性。总体而言，LLM-EmoGraph通过创新的多模态融合策略、大规模预训练和弱监督学习方法，解决了多模态情感识别中的一系列问题，为提升情感识别系统的准确性和稳定性提供了有力支持。

Abstract

Multimodal emotion recognition in conversation (MERC) refers to the identification of emotional states in conversations by integrating various modalities such as text

speech

and visual information. With the rapid development of conversational AI and affective computing

MERC has become a research hotspot in the fields of affective computing and human-computer interaction. Compared to traditional unimodal emotion recognition

multimodal approaches can capture the multifaceted characteristics of emotions more comprehensively and accurately. For instance

text conveys explicit emotional content

speech provides subtle emotional cues like tone

speed

and intonation

while visual information (such as facial expressions) reflects non-verbal emotional expressions. These multimodal signals complement each other

enhancing the accuracy and robustness of emotion recognition. However

multimodal emotion recognition faces several challenges. First

there are significant differences in the representation of information across different modalities

and traditional methods like feature concatenation or weighted averaging fail to fully capture the complex interactions between modalities

which can lead to information loss. Second

emotion recognition tasks often suffer from local noise and outlier samples

which can degrade model stability. Lastly

the accuracy of emotion recognition is closely tied to the effective use of contextual information in a conversation

as emotions are often influenced by preceding and succeeding dialogue. Thus

how to effectively extract and utilize contextual information becomes a major challenge in improving accuracy. To address these issues

this paper proposes a novel emotion recognition method

LLM-EmoGraph

which combines large language model (LLM) with global-local cross-domain graph structures to achieve precise fusion and efficient modeling of multimodal data. This method introduces a multimodal masking mechanism to handle missing and inconsistent information across modalities

ensuring that the model maintains good performance even with incomplete or low-quality information. Through large-scale cross-domain multi-graph pretraining

LLM-EmoGraph enhances the model’s transferability between modalities and graph structures

further improving its robustness. The innovative adaptive dual-scale feature fusion strategy aligns textual

speech

and visual semantic features efficiently

improving emotion recognition accuracy

particularly in scenarios involving high interaction among modalities. Additionally

the paper designs a weakly supervised hierarchical emotion classification scheme based on LLM. This approach guides the extraction of emotional information layer by layer

effectively preventing interference from global emotional patterns

and allows the model to learn emotional features accurately

even with limited annotated data. Experimental results show that LLM-EmoGraph significantly outperforms existing mainstream methods on multiple benchmark datasets

demonstrating its effectiveness and advancement in multimodal emotion recognition tasks. In summary

LLM-EmoGraph

through its innovative multimodal fusion strategies

large-scale pretraining

and weakly supervised learning methods

provides effective solutions to a series of challenges in multimodal emotion recognition

offering strong support for improving the accuracy and stability of emotion recognition systems.

关键词

Keywords

references

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