Advancing disability-inclusive communication with DynaFuseNet and transformer models for sign language interpretation

Fostering inclusion and overcoming accessibility barriers necessitates effective communication for individuals with disabilities. This study demonstrates the integration of Transformer models with a proposed deep learning architecture, DynaFuseNet, to interpret sign language. DynaFuseNet is a composite model that is capable of processing both static and dynamic sign representations. It is constructed by combining a FAST-SIFT Feature Fusion Framework (FSF 3 ) with a Dynamic Temporal Convolution Network (DTCN). The Transformer will enhance the system’s ability to capture long-range dependencies in temporal data, while the architecture employs multi-task learning to analyze various sign language structures simultaneously. To further enhance accessibility, the model will integrate natural language processing (NLP) techniques that would seamlessly translate sign languages. To ensure adaptability and inclusivity, rehabilitation centers have collaborated to create a comprehensive dataset that encompasses a range of regional sign languages and signing techniques. The WLASL dataset achieves 99.12% accuracy, while the MS-ASL dataset achieves 98.56% accuracy. The proposed system obtains state-of-the-art performance. The benefits of the DynaFuseNet architecture and its integration with Transformers in the pursuit of new frontiers in sign language recognition are evident in these results. Thus, this research substantiates the transformative potential of artificial intelligence (AI)-driven assistive technologies in fostering inclusive communication and empowering individuals with disabilities, thereby bridging the linguistic and accessibility disparities within and across diverse communities.