International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 12 | Dec 2025
p-ISSN: 2395-0072
www.irjet.net
HAND GESTURES TO VOICE CONVERTER R Sowjanya1, Mehraan Khaleel2, Akhila B S3, Muhsin S4, Ashwathi H5 1, 2,3,4,Dept .of BME Rajiv Gandhi Institute of Technology Bengaluru, India 5Assistant.
Professor Dept .of BME Rajiv Gandhi Institute of Technology Bengaluru, India -------------------------------------------------------------------------***------------------------------------------------------------------------
Abstract— this project presents a Hand Gestures to Voice Converter designed to assist individuals with speech impairments by enabling seamless communication through intuitive hand gestures. The system captures real-time hand movements using a camera or sensor module and processes them through advanced image recognition and machine learning algorithms. Each recognized gesture is mapped to a specific word or sentence stored in the database. The converted text is then transformed into audible speech using a text-to-speech (TTS) engine. The model ensures high accuracy by employing gesture segmentation, feature extraction, and classification techniques. The solution is lightweight, cost-effective, and easily portable for practical use. It can be customized to support multiple gestures and languages, improving accessibility for diverse users. This project demonstrates how AI-based gesture recognition can significantly enhance communication for people with disabilities and serve as an assistive technology in healthcare and social environments. Keywords— Hand Gesture Recognition, Gesture-to-Speech System, Image Processing, Text-to-Speech (TTS), RealTime Detection, Sign Language Interpretation, Human– Computer Interaction (HCI), Speech Impairment Support
INTRODUCTION Hand gestures play an essential role in human communication, especially for individuals who are unable to speak or have speech-related disabilities. Traditional sign language serves as an effective medium for expression, but it is not universally understood, which often creates communication barriers. To address this challenge, technology-driven solutions are being developed to translate gestures into speech, making interactions more inclusive. A Hand Gestures to Voice Converter is an innovative system that captures hand movements using cameras or sensors and interprets them through image processing and machine learning techniques. The recognized gestures are then converted into text and finally into audible speech using a text-to-speech engine. This system enables real-time communication, enhances accessibility, and provides a practical assistive tool for people with speech impairments. The project combines computer vision, artificial intelligence, and human–computer interaction concepts to create a reliable and user-friendly communication aid. Its flexibility allows customization for different gesture sets, languages, and application environments. Overall, this technology demonstrates how modern AI systems can significantly improve the quality of life for individuals with special communication needs.
OBJECTIVES The main objective of the Hand Gestures to Voice Converter is to create an assistive communication system that translates hand gestures into audible speech, enabling individuals with speech impairments to communicate more effectively. The project aims to develop a reliable gesture recognition model using image processing and machine learning techniques to ensure high accuracy in identifying various hand signs. Another objective is to implement a real-time processing mechanism that captures gestures seamlessly through a camera or sensor. The system also focuses on mapping each gesture to meaningful words or phrases and converting them into clear, natural-sounding voice output using a text-tospeech engine. Additionally, the project seeks to design a user-friendly, portable, and cost-effective solution that can be easily adopted in daily life. It aims to support customization for multiple gestures and languages to enhance usability across diverse user groups. Overall, the objective is to contribute to inclusive communication by providing an efficient tool for people with speech and hearing challenges.
1. RELATED WORK Research on gesture-based communication systems has gained significant attention in recent years due to advancements in computer vision, machine learning, and assistive technology. Several studies have explored the use of hand gestures to support communication for individuals with speech and hearing impairments. Early systems relied heavily on wired gloves and wearable sensors to capture finger positions and hand orientations. Although these devices provided accurate results,
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