Real-Time Face Emotion, Hand Gesture Recognition and Voice Conversion System for Deaf and Dumb

Abstract: This research addresses the profound communication barrier faced by hundreds of millions of individuals globally who rely on sign language as their primary means of expression. Traditional assistive technologies have often relied on expensive and cumbersome wearable hardware, which can be intrusive and impractical for daily use. We present a non-intrusive, real-time assistive system that utilizes a standard webcam and advanced computer vision to bridge this gap. Our approach goes beyond simple word-for-word translation by integrating a dual-layered neural network architecture that simultaneously tracks complex hand movements and analyses subtle facial micro-expressions. By capturing these emotional cues, the system moves beyond robotic, monotone outputs to generate synthesized speech that reflects the user’s true intent—whether it be urgency, joy, or concern. Experimental results demonstrate high classification accuracy across a diverse vocabulary of signs with minimal processing delay, even in varied environmental conditions. This study offers a human-centric solution designed to restore the personality of the speaker and foster more natural, inclusive interactions in critical settings such as healthcare, education, and public services

Keywords: Artificial Intelligence, Assistive Technology, Computer Vision, Deep Learning, Emotion Detection, Gesture Recognition, Human-Computer Interaction, Sign Language Translation, Speech Synthesis.