Through this paper, we seek to reduce the communication barrier between the hearing-impaired community and the larger society who are usually not familiar with sign language in the sub-Saharan region of Africa with the largest occurrences of hearing disability cases, while using Nigeria as a case study. The dataset is a pioneer dataset for the Nigerian Sign Language and was created in collaboration with relevant stakeholders. We pre-processed the data in readiness for two different object detection models and a classification model and employed diverse evaluation metrics to gauge model performance on sign-language to text conversion tasks. Finally, we convert the predicted sign texts to speech and deploy the best performing model in a lightweight application that works in real-time and achieves impressive results converting sign words/phrases to text and subsequently, into speech.
@article{arxiv.2111.00995,
title = {Sign-to-Speech Model for Sign Language Understanding: A Case Study of Nigerian Sign Language},
author = {Steven Kolawole and Opeyemi Osakuade and Nayan Saxena and Babatunde Kazeem Olorisade},
journal= {arXiv preprint arXiv:2111.00995},
year = {2022}
}