Sign-to-Speech Model for Sign Language Understanding: A Case Study of Nigerian Sign Language

TL;DR

This paper presents a sign-to-speech system for Nigerian Sign Language, utilizing a new dataset, object detection, and classification models to convert signs into speech in real-time, aiming to bridge communication gaps.

Contribution

It introduces a pioneering Nigerian Sign Language dataset and develops a real-time sign-to-speech model with effective conversion from signs to spoken language.

Findings

High accuracy in sign-to-text conversion

Effective real-time sign language interpretation

Successful deployment in a lightweight application

Abstract

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.