Emotion recognition is a research hotspot in the field of artificial intelligence. If the human-computer interaction system can sense human emotion and express emotion, it will make the interaction between the robot and human more natural. In this paper, a multimodal emotion recognition model based on many-objective optimization algorithm is proposed for the first time. The model integrates voice information and facial information and can simultaneously optimize the accuracy and uniformity of recognition. This paper compares the emotion recognition algorithm based on many-objective algorithm optimization with the single-modal emotion recognition model proposed in this paper and the ISMS_ALA model proposed by recent related research. The experimental results show that compared with the single-mode emotion recognition, the proposed model has a great improvement in each evaluation index. At the same time, the accuracy of emotion recognition is 2.88% higher than that of the ISMS_ALA model. The experimental results show that the many-objective optimization algorithm can effectively improve the performance of the multimodal emotion recognition model.

中文翻译：

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基于改进量子优化算法的支持向量机天气预报技术

情感识别是人工智能领域的研究热点。如果人机交互系统能够感知人类的情感并表达情感，那么机器人与人类的交互就会更加自然。本文首次提出了一种基于多目标优化算法的多模态情感识别模型。该模型融合了语音信息和面部信息，可以同时优化识别的准确性和均匀性。本文将基于多目标算法优化的情感识别算法与本文提出的单模态情感识别模型和近期相关研究提出的ISMS_ALA模型进行了比较。实验结果表明，与单模情感识别相比，所提出的模型在各项评价指标上都有很大的提高。同时，情感识别的准确率比ISMS_ALA模型高2.88%。实验结果表明，多目标优化算法可以有效提高多模态情感识别模型的性能。