Sign Language Translator with Speech Recognition Integration: Bridging the Communication Gap

This paper presents the development of a real-time sign language translator that integrates speech recognition technology to bridge communication gaps between sign language users and non-signers. The system translates spoken or typed words into corresponding American Sign Language (ASL) and Indian Sign Language (ISL) gestures using a predefined database of sign representations. The paper discusses the system’s architecture, implementation challenges, results, and potential applications in education, public spaces, and assistive technology. Additionally, the research explores the integration of AI-driven enhancements to improve translation accuracy, accommodate a broader vocabulary, and incorporate non-manual sign components such as facial expressions and body language. Future work will focus on refining gesture recognition, improving speech processing, and ensuring accessibility across diverse linguistic and cultural contexts.