Research on user intent perception and experience optimization in multimodal intelligent interaction scenarios
Ziyou Ma
None
DOI: https://doi.org/10.59429/esta.v13i1.13395
Keywords: multimodal intelligent interaction; intention perception; feature fusion; experience optimization; human-computer interaction
Abstract
To address the issues of low accuracy in intent recognition and poor user experience in singlemodal interaction in complex scenarios, this paper conducts research on user intent perception and experience optimization in multi-modal intelligent interaction scenarios. The research integrates voice, gesture, facial recognition, and environmental perception technologies, builds a feature fusion model based on Convolutional Neural Network (CNN) Transformer, designs a hierarchical intent understanding mechanism and interaction execution optimization strategy, and realizes precise perception and efficient response of user intentions. Experiments are selected to verify in three typical scenarios: smart home control, immersive entertainment, and personalized services. The results show that this research scheme can achieve an intent recognition accuracy of over 85%, and the system response delay is controlled within 800ms. This effectively improves the naturalness and intelligence level of multi-modal interaction, providing technical references for the design and optimization of intelligent terminal interaction systems.
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