Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 40, 2023 No. 8
2286-2291,2310

Speech emotion recognition based on multi-modal fusion of graph neural network

Li Zijing
Chen Ning
School of Information Science & Engineering, East China University of Science & Technology, Shanghai 200237, China
DOI: 10.19734/j.issn.1001-3695.2023.01.0002

Abstract

At present, speech emotion recognition models based on multi-modal fusion generally suffer from the inability to make full use of the commonality and complementarity between multimodal features, the inability to effectively optimize and aggregate sample features by using the topological structure characteristics between sample features, and the high complexity of existing models. Therefore, this paper introduced graph neural network. On the one hand, in the feature optimization stage, it used the text features optimized by the graph neural network as a shared representation to reconstruct the adjacency matrix based on acoustic features, so that the topological structure characteristics of the acoustic features contained text information, thus achieving multi-modal fusion. On the other hand, in the label prediction stage, it used the graph neural network to fully aggregate the similarity information contained in the adjacent nodes of the current node to optimize the characteristics of the current node globally to improve the accuracy of emotion recognition. At the same time, in order to prevent the over-smoothing problem that might occur during the training of the graph neural network, it performed graph augmentation before the graph neural network training. The experimental results on the public datasets IEMOCAP and RAVDESS show that the proposed model achieves higher recognition accuracy and lower model complexity than the baseline models, and each component of the model contributes to the improvement of model performance.

Foundation Support

国家自然科学基金资助项目(61771196)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.01.0002
Publish at: Application Research of Computers Printed Article, Vol. 40, 2023 No. 8
Section: Algorithm Research & Explore
Pages: 2286-2291,2310
Serial Number: 1001-3695(2023)08-007-2286-06

Publish History

[2023-03-10] Accepted Paper
[2023-08-05] Printed Article

Cite This Article

李紫荆, 陈宁. 基于图神经网络多模态融合的语音情感识别模型 [J]. 计算机应用研究, 2023, 40 (8): 2286-2291,2310. (Li Zijing, Chen Ning. Speech emotion recognition based on multi-modal fusion of graph neural network [J]. Application Research of Computers, 2023, 40 (8): 2286-2291,2310. )

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  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
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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.

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