A Novel Approach for Real-Time Audio-to-Sign Language Translation using Naive Bayes classifier and Natural Language Processing Technique

Abstract

Real-time translation from spoken language to sign language, crucial for the deaf and hard of hearing, is difficult employing machine learning advances, this research suggests employing Naive Bayes to instantaneously translate audio input into sign language motions. The initiative provides precise translations between spoken and visual language to improve accessibility and inclusion. The system generates sign language animations from audio inputs using natural language processing and deep learning. Language extraction, tense and context classification, and proficient sign language synthesis are key aspects. The research innovates by using a Naive Bayes model trained on varied spoken language and sign language gesture datasets to ensure robust performance across contexts and user situations. To enhance realism and comprehension, the system integrates a 3D animated avatar to render the translated signs visually. A custom-built gesture mapping mechanism aligns each keyword with its corresponding sign language motion. The tool operates in real-time, supporting both sentence-level and word-level translations. This approach contributes to bridging communication gaps and supports inclusive digital interaction for the hearing-impaired community.