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Research on Generative AI Psychological Intervention Models Based on Multimodal Emotion Recognition
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Abstract
A study was conducted on a generative artificial intelligence (AI) psychological intervention model based on multimodal emotion recognition. The research aimed to develop an intelligent system capable of identifying human emotions and generating personalized psychological intervention content to address the limitations of traditional psychological assistance technologies, which often lack nuanced emotional understanding and flexible interaction responses. Methodologically, the model integrates multiple data sources, including speech, facial expressions, and physiological signals, achieving emotion recognition through temporal feature alignment and cross-modal attention mechanisms. A generative dialogue network is introduced to produce intervention texts consistent with emotional semantics. Experimental results show that the model achieves high emotion recognition accuracy and semantic consistency on public datasets such as IEMOCAP and DEAP, verifying the effectiveness of multimodal fusion and generative modeling. The study concludes that the application of generative AI in psychological intervention can enhance system empathy and adaptive responsiveness, providing a technical framework for intelligent counseling and emotional health monitoring.
Keywords
Multimodal emotion recognition; generative artificial intelligence; psychological intervention model; affective computing
References
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How to cite this paper
Research on Generative AI Psychological Intervention Models Based on Multimodal Emotion Recognition
How to cite this paper: Xiaoyu Gu. (2025) Research on Generative AI Psychological Intervention Models Based on Multimodal Emotion Recognition. Advances in Computer and Communication, 6(5), 304-309.
DOI: http://dx.doi.org/10.26855/acc.2025.12.008
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