Sign Language Conversion To Text and Speech Using Machine Learning
Chilakala Venkata Shritha Reddy , Anitha M, Annanya P, Hargun Kour, Mangala H S
Sign Language, Machine Learning, Hand gestures, Hearing-Impaired, Convolutional Neural Networks, User-Centric Experience, Scalability, Sustainability.
The "Sign Language Conversion to text and speech using machine learning" initiative tackles the urgent need for improved communication within this community by introducing an innovative system designed to convert sign language into text and speech. Leveraging state-of-the-art technologies, the system integrates computer vision techniques for real-time sign language gesture detection, employing Convolutional Neural Networks (CNNs) to adapt to diverse signing styles. The multi-stage methodology involves precise gesture recognition, mapping gestures to linguistic components using sequence-to-sequence models, and refining textual output through Natural Language Processing (NLP) techniques. The system further incorporates a sophisticated text-to-speech synthesis module, prioritizing prosody, intonation, and emotion to convey the expressive nature of sign language. The overall methodology ensures not only accuracy and real-time processing but also adaptability to different sign language variants. Promising outcomes are observed, with the system proving effective in overcoming communication barriers, as affirmed by user feedback and evaluations from the Deaf and Hard of Hearing community. The results highlight the system's accuracy, real-time processing capabilities, and its potential to foster inclusive interactions, making it a significant advancement in addressing the communication needs of this community.
Article Details
Unique Paper ID: 164433

Publication Volume & Issue: Volume 10, Issue 12

Page(s): 2846 - 2851
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