A Real-Time Speech-to-Glyph Translator for Deaf Accessibility

Abstract — Communication barriers between the hearing and deaf individuals that continue to limit accessibility and social inclusion in an education, healthcare, workplaces and also public environments. This research presents a Real-Time Speech-to-Glyph Translator that converts spoken language into visual glyphs instantly and correctly, enabling deaf and hard-of-hearing individuals to understand the verbal communication more effectively and accurately. The proposed system utilizes Automatic Speech Recognition (ASR) to capture and transcribe speech, followed by Natural Language Processing (NLP) techniques to analyze the recognized text and map it to an appropriate sequence of standardized glyphs. These translated glyphs are displayed through an intuitive graphical interface that ensure fast, accurate and User-friendly communication. This system is designed to minimize the translation latency while maintaining high recognition accuracy under multiple speaking conditions. provides real-time visual representation of spoken language and the proposed solution enhances accessibility and promotes inclusive communication and also reduces dependency on human interpreters. Experimental evaluation demonstrates that the system delivers reliable performance with low response time, making it suitable for educational institutions, public service centers, healthcare facilities and everyday communication. The research contributes to the assistive technology by offering an efficient and scalable solution for that bridges the communication gap between hearing and deaf communities and normal people.

Keywords —

 Real-Time Speech-to-Glyph Translator, Deaf Accessibility, Speech-to-Text Conversion, Glyph Translation, Natural Language Processing (NLP), Artificial Intelligence (AI), Machine Learning (ML), Real-Time Processing, Inclusive Communication, Sign Representation, Hearing-Impaired Assistance, Human-Computer.