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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 42, 2025 No. 4
1065-1072

KCF: knowledge-enhanced cross-modal fusion network for emotion recognition in conversation

Gan Xinyi1
Huang Xianying1
Zou Shihao2
Shen Xudong1
1. College of Computer Science & Engineering, Chongqing University of Technology, Chongqing 400054, China
2. School of Computer Science & Technology, Huazhong University of Science & Technology, Wuhan 430074, China
DOI: 10.19734/j.issn.1001-3695.2024.08.0322

Abstract

To address the underutilization of differences in modal representation capabilities and speaker emotional cues, this paper proposed a knowledge-enhanced cross-modal fusion network model. This model incorporated a cross-modal module enhanced by external knowledge, which systematically integrated weaker modal features with multi-level text and external know-ledge, embedding them into the multi-head attention layer. This approach fully extracted valuable information from the weaker modalities, ensuring feature complementarity and consistency across modalities. Additionally, the model introduced an emotion clue enhancement module based on a directed graph, which leveraged external knowledge linked to the speaker's emotional cues to strengthen the fused features. This module also constructed a directed graph to capture contextual information, allowing for a deeper exploration and utilization of the speaker's emotional states. Experimental results on two benchmark datasets de-monstrate that the model effectively harnesses both modal representation differences and speaker emotional cues, achieving significantly improved emotion recognition performance compared to existing methods, thereby validating the model's feasibility and effectiveness.

Foundation Support

国家自然科学基金资助项目(62141201)
重庆市自然科学基金资助项目(CSTB2022NSCQ-MSX1672)
重庆市研究生科研创新资助项目(CYS23675)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.08.0322
Publish at: Application Research of Computers Printed Article, Vol. 42, 2025 No. 4
Section: Algorithm Research & Explore
Pages: 1065-1072
Serial Number: 1001-3695(2025)04-013-1065-08

Publish History

[2025-04-05] Printed Article

Cite This Article

干欣怡, 黄贤英, 邹世豪, 等. 基于知识增强的跨模态融合网络的多模态对话情绪识别模型 [J]. 计算机应用研究, 2025, 42 (4): 1065-1072. (Gan Xinyi, Huang Xianying, Zou Shihao, et al. KCF: knowledge-enhanced cross-modal fusion network for emotion recognition in conversation [J]. Application Research of Computers, 2025, 42 (4): 1065-1072. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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