What type of study is this?

This is a Experimental Study study (also classified as: Quantitative Study).

October 2, 2025

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

Puntos clave

The model significantly improves emotion classification accuracy in children with the use of multimodal fusion techniques.
Key performance metrics from experiments on the CH-SIMS dataset indicate that it surpasses baseline models in both classification and regression tasks.
Feature extraction through BERT and audio-video integration demonstrates enhanced recognition capabilities via a self-attention mechanism.
Robustness and generalization ability highlight the model's potential for broader applications beyond preschool education.

Resumen

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.

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