基于集成学习与聚类联合标注的多模态个体情绪识别OACSTPCD

To address the low recognition accuracy of generic emotion recognition models when faced with different indi-viduals,a multimodal individual emotion recognition technique based on joint labelling with integrated learning and clus-tering was proposed.The method first trained a generic emotion recognition model based on a public dataset,then anal-lysed the distributional differences between the data in the public dataset and the unlabelled data of individuals,and estab-lished a cross-domain model for predicting and labelling pseudo-labels of individual data.At the same time,the individual data were weighted clustered and labelled with cluster labels,and the cluster labels were used to jointly label with pseudo-labels,and high confidence samples were screened to further train the generic model to obtain a personalized emotion rec-ognition model.Using this method to annotate these data with the experimentally collected data of 3 emotions from 3 sub-jects,the final optimized personalized model achieved an average recognition accuracy of more than 80%for the 3 emo-tions,which was at least a 35%improvement compared to the original generic model.