• Eswari Lakshmi Devi Voora
• Imandi Kalyan Sai
• Behara Sai Sahitya
• Kosetti Jayachandra
• Togi Chaitanya Kumar

Human beings interact with each other to convey their ideas, thoughts and experiences to the people around them. But this is not the case for deaf-mute people. Sign language paves the way for deaf-mute people to communicate. Through sign language, communication is possible for a deaf-mute people. The aim behind the work is to develop a system for recognizing the sign language, which provides communication between people with speech impairment and normal people, thereby reducing the communication gap between them. Compared to other gestures(arm, face, head and body), hand gestures plays an important role, as it expresses the user’s views in less time. In the current work flex sensor-based gesture recognition module is developed to recognize English alphabets and few words and a Text-to-Speech synthesizer based on Transfer learning, NLP, Activation functions, Image recognition, Neural networks, Recurrent neural networks is built to convert the corresponding text.

Sign Language, Deaf-mute communication, Gesture Recognition, Hand Gesture, Text-to-Speech Synthesizer, Natural Language Processing, Image Recognition, Recurrent Neural Network, Random Forest

"Visual Gesture To Auditory Speech Converter Using Deep-Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 3, page no.c671-c676, March-2025, Available :http://www.jetir.org/papers/JETIR2503280.pdf

"Visual Gesture To Auditory Speech Converter Using Deep-Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 3, page no. ppc671-c676, March-2025, Available at : http://www.jetir.org/papers/JETIR2503280.pdf