Share Token Tensorized Attention Fusion: Enhancing Audio-Visual Emotion Recognition for Consumer Electronics

In today’s competitive market, smarter and higher-performing consumer electronics are increasingly attractive to the public. These products not only need to perform basic functions but also offer personalized services to enhance user experience and drive sales growth. To solve this problem, this paper proposes a multimodal emotion recognition model based on Share Token Tensorized Attention Fusion (STF), which improves the intelligence of electronic products by providing in-depth understanding of the user’s emotional state.The STF method constrains the modal feature fusion by introducing the share token and combines with the tensorized attention for feature fusion, which significantly improves the accuracy of emotion recognition, improves the robustness of the model, and reduces the learning cost compared to the Transformer-based multimodal emotion recognition model. The STF-based multimodal emotion recognition model can ensure model performance and faster response speed in noisy environments, and it is easier to meet the consumers’ pursuit of consumer electronics experience.