A real-time Arabic avatar for deaf–mute community using attention mechanism

Abstract

Speech-impaired people use Sign Language (SL), an efficient natural form of communication, all over the world. This paper aims to use deep learning technology in the realm of SL translation and identification. In order to ease communication between hearing-impaired and sighted individuals and to enable the social inclusion of hearing-impaired people in their daily lives, it presents a transformer as a neural machine translation model. The article details the creation of a machine translation system that converts Arabic audio and text into Arabic Sign Language (ArSL) automatically. It does this by utilizing an animated character to produce the correct sign for each spoken word. Since Arabic has few resources, it was challenging to obtain an Arabic-Sign dataset, so we created our own Arabic–Arabic sign gloss, which consists of 12,187 pairs, to train the model. We use bidirectional encoder representations from transformers as an embedding layer to interpret input text tokens and represent an appropriate natural language vector space for deep learning models. To represent the structure of each Arabic word, the Ferasa Part-of-Speech Tagging module was used and then the extracted rules from the ArSL structure were applied. This paper shows a detailed description of a natural language translator (for converting an Arabic word sequence into a sequence of signs belonging to the ArSL) and a 2D avatar animation module (for playing back the signs). In our prototype, we train the software-based module using the attention mechanism. The evaluation was carried out in our developed Arabic sentences with the corresponding Arabic gloss. The proposed model achieves promising results and indicates significant improvements to direct communication between hearing and deaf people, with a training accuracy of 94.71% and an 87.04% testing accuracy for Arabic–Arabic sign gloss translation.