Machine Learning-Based Audio Interface Model for Sign Language Recognition

-3bf97310184a13a8c1e-52d9 20221224060536984 beie:beie director@blueeyesintelligence.org WEB-FORM International Journal of Innovative Technology and Exploring Engineering IJITEE 22783075 10.35940/ijitee https://www.ijitee.org/ 12 30 2022 12 1 Machine Learning-Based Audio Interface Model for Sign Language Recognition Department of Information Technology, Terna Engineering College, Nerul, Navi Mumbai (Maharashtra), India. Akash Rai https://orcid.org/0000-0003-0751-8032 Sujata Kadu https://orcid.org/0000-0001-6346-2104 Department of Information Technology Terna Engineering College, Nerul, Navi Mumbai (Maharashtra), India. Satish Salunkhe https://orcid.org/0000-0002-8395-7101 Department of Computer Engineering, Terna Engineering College, Nerul, Navi Mumbai (Maharashtra), India. Due to the fact that most offices and educational institutions now operate from home, the work-from-home and study-from-home cultures have made it difficult to interact with persons who are deaf or hard of hearing. These people communicate within their society using sign language, which is not widely understood by others. Most of the time, as a result of this, they miss out on the opportunity to express their point in front of every one since they are ignored/passed over without receiving the necessary attention. In real-time, having an independent translator that can process photos and interpret signs quickly at the speed of streaming images is critical. We'll utilize TensorFlow Object Detection and Python to bridge the gap by creating an end-to-end bespoke object detection model that not only translates sign language in real time but also speaks it to others. 12 30 2022 38 42 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true No, I did not receive. No conflicts of interest to the best of our knowledge. No, the article does not require ethical approval and consent to participate with evidence. Not relevant. According to the authors, the contribution to the paper as follows: Study conception and Design: Akash Rai, Dr. Sujata Kadu; Data collection: Akash Rai; Analysis and interpretation of results: Akash Rai; Draft manuscript preparation: Akash Rai, Dr. Sujata Kadu, Dr. Satish Salunkhe. 10.35940/ijitee.A9374.1212122 https://www.ijitee.org/portfolio-item/A93741212122/