Multimodal Fusion Technology for Analyzing Children’s Emotions Based on the Attention Mechanism

To enhance the emotion recognition ability of preschool education dialogue robots, this paper proposes a multimodal fusion model based on the cross-modal Transformer architecture. The model consists of feature extraction, fusion, and output layers. It extracts multi-source data through BERT, audio via AFEU units, and OpenFace toolkit. The multi-head self-attention mechanism is introduced to obtain high-level features, with text as an auxiliary and audio-video as the main modalities. The improved cross-modal Transformer and AVFSM module are used to fuse features and achieve emotion recognition. Experiments show that in the CH-SIMS and self-built Tea datasets, the model outperforms the baseline model in classification and regression metrics, verifying the effectiveness of each component. It has good robustness and generalization ability, and has a good application prospect in preschool education and other fields.