DEAFED-TECH: Using AI-Powered Sign Language Generation, Deep fake GAN Technology, and Rural Edu-cation Insights to Advance Deaf Education and Communication

Abstract

This project seeks to develop innovative Deaf Education Technology (Deaf Ed Tech) model that converts speech or text in to sign language using advanced machine learning techniques, focusing on real-time, multilingual, and seamless communication. The model integrates the latest technologies like GANs and CNNs and produces realistic sign language gestures based on either input speech or text. The multi-layer processing includes speech to text using robust models for converting speech to text input, language detection, text preprocessing, gesture mapping to sign language, and video synthesis. Gesture mapping is done using a lexicon database with fallback mechanisms for OOV words through finger spelling or generative inference. The synthesized sign language videos are generated based on the extracted pose key points by GANs, which guarantees smooth transitions and natural signing. The uniqueness of this project that it handles multilingual inputs (Hindi and English) and produces real-time sign language videos. In addition, the architecture accommodates modularity, scalability, and v high-quality video output, maximizing GANs to be useful for real-time applications. This design aims to make it easier and more accessible for the deaf community to easily communicate without barrier, promoting communication across all walks of life with accuracy in their sign language and cultural considerations.