A CONVERSION OF SPEECH LANGUAGE INTO SIGN LANGUAGE

Abstract: This research introduces an AI-driven communication platform designed to bridge the communication gap between hearing and deaf individuals. The system employs Temporal Convolutional Networks (TCNs) for precise recognition of Indian Sign Language (ISL) gestures. To handle spoken language input, it incorporates a Speech Recognition and Synthesis Module (SRSM) that utilizes Hidden Markov Models (HMMs) to transcribe speech into text. A 3D avatar module subsequently interprets the transcribed speech into ISL visual gestures, allowing for seamless real-time interaction. The gesture recognition model, trained on the MNIST ISL dataset, achieved a high accuracy rate of 98.5%, ensuring dependable performance in both gesture-to-text and speech-to-sign translation tasks. Designed for inclusivity, the system caters to deaf, mute, and non-signing users. Additionally, a user-friendly web interface enhances accessibility and ease of use across platforms.

Keywords: Sign Language, Temporal Convolutional Network, Speech Recognition, Indian Sign Language, 3D Avatar, Accessibility, Deep Learning, Inclusive Communication.