Abstract
With the acceleration of globalization, cross-cultural communication has become a crucial issue in various fields. Emotion, as an essential component of communication, plays a key role in improving understanding and interaction efficiency across different cultures. However, accurately recognizing emotions across cultural backgrounds remains a major challenge in affective computing, particularly due to limitations in multimodal feature fusion and temporal dependency modeling in traditional approaches. To address this, we propose the TAF-ATRM framework, which integrates Transformer and multi-head attention mechanisms for cross-cultural emotion recognition. Specifically, the framework employs bidirectional encoder representations from transformers (BERT) for semantic feature extraction from text, Mel-frequency Cepstral Coefficients (MFCC) and Residual Neural Network (ResNet) for capturing critical features from speech and facial expressions, respectively, thereby enhancing multimodal emotion recognition capability. To improve the fusion of multimodal data, the Transformer is utilized for temporal feature modeling, while multi-head attention reinforces feature representation by capturing complex inter-modal dependencies. The framework is evaluated on the MOSI and MOSEI datasets, where experimental results demonstrate that TAF-ATRM outperforms traditional methods in emotion classification accuracy and robustness, particularly in cross-cultural emotion recognition tasks. This study provides a strong technical foundation for future advancements in multimodal emotion analysis and cross-cultural affective computing.