Multimodal Transformer-based Approach for Building Adaptive, Interactive, and Inclusive e-Learning Systems

The current challenges in the domain of multimodal learning arise from the difficulties in effectively processing and integrating heterogeneous data types, such as text, speech, and video. Existing methods often fail to accurately fuse these modalities due to the complex nature of each input type and the diverse features. Text data requires semantic understanding, speech needs temporal analysis, and video requires both spatial and temporal feature extraction. Furthermore, the lack of scalable solutions for seamlessly merging these distinct modalities hampers the performance of multimodal systems, especially in applications like sign language interpretation and educational technologies. This work proposes a novel solution through a Multimodal Transformer-based architecture, designed to address the complexities inherent in multimodal data processing. The approach utilizes independent encoders for text, audio, and visual data, each tailored to extract the most relevant features from the respective domains. A cross-attention mechanism is employed to fuse these modality-specific representations, enhancing the integration and ensuring that contextual information across modalities is aligned effectively. The architecture is further optimized through a transformer-based decoder that produces accurate predictions or classifications based on the fused multimodal data. Key features of the proposed work include its ability to handle and merge different types of data seamlessly, its use of advanced attention mechanisms for improved contextual understanding, and its robust performance across noisy and incomplete data scenarios. This methodology enables more accurate and adaptable multimodal systems, with applications ranging from sign language recognition to assistive learning technologies.