Gesture-to-Speech System for Enhanced Communication Among Deaf and Mute Individuals

Abstract

Communication barriers significantly impact the daily lives of deaf and mute individuals, limiting their interactions with the hearing community. This paper presents a Gesture-to-Speech System designed to bridge this gap by converting sign language gestures into spoken words. The system leverages sensor-based or computer vision techniques to capture hand movements and interpret them using machine learning algorithms. These interpreted gestures are then converted into speech output, enabling seamless communication. The proposed system incorporates gesture recognition models trained on a dataset of commonly used sign language gestures. Advanced technologies such as deep learning, natural language processing (NLP), and speech synthesis are employed to enhance accuracy and fluency. The system aims to provide real-time translation, ensuring an efficient and natural conversation experience.

This technology benefits not only deaf and mute individuals but also improves accessibility in education, healthcare, and social interactions. By fostering inclusivity, the Gesture-to-Speech System promotes independence and integration into mainstream society. Future enhancements may include multilingual support, enhanced gesture recognition accuracy, and portable device compatibility. With continuous advancements, this system holds the potential to revolutionize assistive communication technologies, empowering individuals with speech and hearing disabilities.