双级门控分段式多模态情绪识别方法

马飞; 李树志; 杨飞霞; 徐光宪

doi:10.11959/j.issn.2096-6652.202514

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双级门控分段式多模态情绪识别方法

学术论文 | 更新时间：2025-07-21

- 双级门控分段式多模态情绪识别方法
- Dual-stage gated segmented multimodal emotion recognition method
- 智能科学与技术学报 2025年7卷第2期页码：257-267
- 作者机构：
  
  1.辽宁工程技术大学电子与信息工程学院，辽宁葫芦岛 125105
  2.辽宁工程技术大学电气与控制工程学院，辽宁葫芦岛 125105
- 作者简介：
  
  [ "马飞（1978- ），男，博士，辽宁工程技术大学电子与信息工程学院副教授，主要研究方向为多模态数据融合、情感计算、教育人工智能、遥感图像处理等。" ]
  [ "李树志（1999- ），男，辽宁工程技术大学电子与信息工程学院硕士生，主要研究方向为多模态数据融合、情绪识别与深度学习。" ]
  [ "杨飞霞（1979- ），女，博士，辽宁工程技术大学电气与控制工程学院副教授，主要研究方向为多模态数据处理、图像处理与模式识别、深度学习与最优化。" ]
  [ "徐光宪（1977- ），男，博士，辽宁工程技术大学电子与信息工程学院教授，主要研究方向为多模态数据处理、图像处理与模式识别、数据处理与网络编码、机器视觉。" ]
- 基金信息：
  
  辽宁省教育科学“十四五”规划课题(JG24DB219);辽宁省科技厅自然科学基金计划面上项目(2023-MS-314);辽宁省教育厅高校科研业务经费项目(LJ242410147006);辽宁工程技术大学GPU资源支持项目
- DOI：10.11959/j.issn.2096-6652.202514
  中图分类号： TP391
- 收稿：2025-01-20，
  
  修回：2025-03-19，
  
  纸质出版：2025-06-15
- 稿件说明：
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马飞,李树志,杨飞霞等.双级门控分段式多模态情绪识别方法[J].智能科学与技术学报,2025,07(02):257-267.

MA Fei,LI Shuzhi,YANG Feixia,et al.Dual-stage gated segmented multimodal emotion recognition method[J].Chinese Journal of Intelligent Science and Technology,2025,07(02):257-267.
马飞,李树志,杨飞霞等.双级门控分段式多模态情绪识别方法[J].智能科学与技术学报,2025,07(02):257-267. DOI： 10.11959/j.issn.2096-6652.202514.

MA Fei,LI Shuzhi,YANG Feixia,et al.Dual-stage gated segmented multimodal emotion recognition method[J].Chinese Journal of Intelligent Science and Technology,2025,07(02):257-267. DOI： 10.11959/j.issn.2096-6652.202514.

摘要

多模态情绪识别技术在心理健康检测与机器情感分析中应用广泛，但现有方法多依赖全局或局部特征，忽略了二者的联合建模，限制了情绪识别性能。为此，提出了一种基于Transformer的双级门控分段式多模态情绪识别模型（dual-stage gated segmented multimodal emotion recognition method，DGM）。DGM采用分段式融合架构，包括交互阶段与双级门控阶段。交互阶段采用OAGL融合策略建模全局-局部跨模态交互，优化特征融合效率；双级门控阶段整合局部与全局特征，充分利用情绪信息。此外，针对模态间局部时序特征不对齐问题，设计了基于缩放点积的序列对齐方法以提升融合精度。在CMU-MOSI、CMU-MOSEI和CH-SIMS 3个基准数据集上的实验表明，DGM在多数据集上的识别效果优于现有算法，验证了其捕捉情绪细节的能力与泛化性能。

Abstract

Multimodal emotion recognition has broad applications in mental health detection and affective computing. However

most existing methods rely on either global or local features

neglecting the joint modeling of both

which limits emotion recognition performance. To address this

a Transformer-based dual-stage gated segmented multimodal emotion recognition method (DGM). DGM adopts a segmented fusion architecture was proposed

consisting of an interaction stage and a dual-stage gating stage. In the interaction stage

the OAGL fusion strategy was employed to model global-local cross-modal interactions

improving the efficiency of feature fusion. The dual-stage gating stage integrates local and global features was designed to fully utilize emotional information. Additionally

to resolve the misalignment of local temporal features across modalities

a scaled dot-product-based sequence alignment method was developed to enhance fusion accuracy. Experimental were conducted on three benchmark datasets (CMU-MOSI

CMU-MOSEI

and CH-SIMS)

and the results demonstrate that DGM outperforms representative algorithms on multiple datasets

validating its ability to capture emotional details and its strong generalization capability.

关键词

Keywords

references

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