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Bridging communication gaps: An AI-powered real-time system for sign language, speech and text translation

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  • Bridging communication gaps: An AI-powered real-time system for sign language, speech and text translation

Naga Sasha Lakshmi Pokanati, Monika Devi Imandi, Yamini Sariki *, Sivaram Sangula and Nagendra Vasamsetti

Department of CSE Aditya College of Engineering and Technology, Surampalem, Andhra Pradesh, India – 533437

Review Article

International Journal of Science and Research Archive, 2025, 14(03), 1331-1336

Article DOI: 10.30574/ijsra.2025.14.3.0807

DOI url: https://doi.org/10.30574/ijsra.2025.14.3.0807

Received on 13 February 2025; revised on 21 March 2025; accepted on 24 March 2025

Communication effectiveness poses an essential challenge to the millions who have hearing or speech impairments in their lives. HandSpeak provides a real-time AI interface through which users can interact easily because it transforms sign language into written messages and spoken words. The Sign-to-Speech module performs its functions through the integration of 3D cameras with Convolutional Neural Networks (CNNs) as well as Long Short-Term Memory (LSTM) networks to detect hand landmarks while tracking hand movements and interpreting sign gesture temporal sequences. Speech input flows through the Speech-to-Sign module to produce text output that gets processed into animated sign language expressions under AI avatar operations. Transformers boost linguistic precision and the integration between Django and Flask provides users with an optimized web-based interface. The application uses SQLite for optimized data storage together with Blender for producing sign animations. The technology serves to create an barrier-free environment for natural communication which enhances inclusivity while providing better accessibility to hearing and speech disabled people in various social and professional environments.

Sign Language Translation; Real-Time Communication; Bidirectional; Tracking hand movements; Convolutional Neural Networks (CNN); Long Short-Term Memory (LSTM)

https://journalijsra.com/sites/default/files/fulltext_pdf/IJSRA-2025-0807.pdf

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Naga Sasha Lakshmi Pokanati, Monika Devi Imandi, Yamini Sariki, Sivaram Sangula and Nagendra Vasamsetti. Bridging communication gaps: An AI-powered real-time system for sign language, speech and text translation. International Journal of Science and Research Archive, 2025, 14(03), 1331-1336. Article DOI: https://doi.org/10.30574/ijsra.2025.14.3.0807.

Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0

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