VOICEBRIDGE: CONVERTING GESTURES TO SPEECH FOR INCLUSIVE COMMUNICATION

Abstract

Effective communication remains a significant challenge for individuals who are deaf or mute, limiting their interaction with the broader community. This study presents VoiceBridge, an intelligent system that integrates hand gesture recognition with real-time voice synthesis to facilitate seamless communication. By using computer vision techniques and machine learning algorithms, the system accurately identifies predefined hand gestures and converts them into spoken language through a voice output interface. Experimental evaluation demonstrates high recognition accuracy, low latency, and robustness under varying lighting and background conditions. The system empowers users to convey messages naturally, bridging the communication gap between hearing-impaired individuals and the general population. This research highlights the potential of gesture-to-speech technologies in promoting social inclusion, accessibility, and enhanced quality of life for individuals with hearing and speech impairments.