Shanketik: Revolutionizing Communication for the Deaf and Mute - A Reinforcement Learning-Enhanced Bangla AAC Application

This research addresses the communication challenges faced by the deaf and mute community, leveraging advanced technology to enhance augmentative and alternative communication (AAC). In light of the limitations of existing methods, we propose a novel approach, ‘Shanketik’, that integrates Reinforcement Learning (RL) into an AAC mobile application in Bangla language. This system bridges the gap in accessible communication tools, particularly in languages like Bangla, where options are limited. The ‘Shanketik’ system functions as an agent, interacting with users through symbols in AAC. It incorporates a dynamic feedback loop, learning from positive and negative user responses. The system’s core architecture involves the innovative use of the Deep Q-Network (DQN) algorithm, a fusion of Q-learning and deep neural networks. The ‘Shanketik’ application not only refines text and speech generation for accuracy and contextual relevance but also offers a user-friendly interface, ensuring navigation flexibility and enhanced efficiency in usability. The research presents a significant advancement in AAC technology, marking a transformative step toward inclusive communication for the deaf and mute community.