Multimodal Artificial Intelligence for Intelligent Human–Computer Interaction

Marcel Worring

Multimodal Artificial Intelligence for Intelligent Human–Computer Interaction

Author(s)	Marcel Worring
Country	United States
Abstract	Human–computer interaction is progressively moving beyond keyboard-, pointer-, and screen-centered communication toward intelligent interfaces capable of interpreting speech, text, facial behavior, gesture, visual context, and other human-generated signals simultaneously. Multimodal artificial intelligence provides the computational foundation for this transition by integrating heterogeneous information sources and allowing interactive systems to infer user intention from complementary evidence. The present study investigates the role of multimodal artificial intelligence in intelligent human–computer interaction through a conceptual analysis and reproducible synthetic simulation. The proposed framework combines speech, visual, and textual observations using quality-aware probabilistic fusion and evaluates interaction accuracy under clean conditions, acoustic noise, visual occlusion, and combined ambiguity. Twenty thousand synthetic binary interaction trials were generated for each environmental condition using a fixed random seed. Speech-only, vision-only, text-only, and adaptive multimodal configurations were compared. The simulation produced multimodal intent-recognition accuracies of 98.49% under clean conditions, 92.91% during acoustic interference, 92.38% during visual occlusion, and 81.28% under combined ambiguity. The multimodal configuration remained more robust than individual modalities because the fusion mechanism increased the contribution of relatively reliable signals while reducing dependence on degraded channels. The findings demonstrate the potential of multimodal AI to improve interaction robustness, contextual sensitivity, accessibility, and naturalness. Nevertheless, successful deployment requires reliable cross-modal alignment, uncertainty modeling, privacy protection, inclusive training data, efficient inference, and appropriate human control. The study concludes that intelligent HCI should not simply maximize the number of available modalities; rather, it should dynamically integrate those modalities according to their contextual reliability and relevance to the interaction task.
Keywords	multimodal artificial intelligence, human–computer interaction, multimodal fusion, intelligent interfaces, speech recognition, computer vision, natural language processing, adaptive interaction
Field	Engineering
Published In	Volume 1, Issue 3, May-June 2019
Published On	2019-05-14

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American International Journal of Research and Innovation

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Multimodal Artificial Intelligence for Intelligent Human–Computer Interaction

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