Bridging Silence with AI: A Novel AI Approach for Real-Time ASL Interpretation to Speech Conversion

The ability to bridge communication gaps for the deaf and hard of hearing is a pressing need in an increasingly digital world. This paper introduces an AI -driven ASL Interpreter that translates American Sign Language (ASL) into both text and speech, enhancing accessibility and communication for those hard of hearing. Our approach leverages Convolutional Neural Networks (CNNs) for image recognition, Mediapipe for real-time hand tracking, and Pyttsx3 for text-to-speech conversion. The novelty of our approach lies in the integration of Mediapipe with deep learning models, leading to a highly efficient and accurate real-time system. We provide a detailed methodology, implementation specifics, and performance evaluation using precision, recall, and F1-score metrics. Experimental results indicate a robust performance with 99% accuracy, overcoming common issues in gesture misclassification. Future improvements include enhancing model generalization, support for multilingual sign languages, and real-world deployment optimizations.