Improving the deaf and hard of hearing internet accessibility : JSL, text-into-sign language translator for Arabic

Al-Sarayrah, W ORCID: https://orcid.org/0000-0003-0780-6114, Al-Aiad, A ORCID: https://orcid.org/0000-0002-1769-1253, Habes, M ORCID: https://orcid.org/0000-0003-3790-7303, Elareshi, M ORCID: https://orcid.org/0000-0001-5706-3828 and Salloum, S ORCID: https://orcid.org/0000-0002-6073-3981 2021, Improving the deaf and hard of hearing internet accessibility : JSL, text-into-sign language translator for Arabic , in: International Conference on Advanced Machine Learning Technologies and Applications, 20th-22nd March 2021, Cairo, Egypt.

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Abstract

Our society is more dependent on ICT regardless of our abilities. However, some webpages cannot be accessed by D/HoH people, especially when they lack education skills. Technological experts have offered several solutions over the years e.g., fixed content given to D/HoH users, or videos using SL, which affects the presentation. As a suggested solution, the Jordanian Sign Language browser (JSL) was developed. This allows D/HoH users to choose any word and translate it into SL using videos with translated words appearing on the screen on request without disturbing the website presentation. The JSL acceptance was measured using the usability questionnaire (SUMI). The model was drawn from 100 Jordanian D/HoH users to measure their satisfaction and acceptance and test the following factors: Efficiency, Effect, Helpfulness, and Learnability. The findings revealed that the proposed model was reliable and reinforced the need for including ICT in D/HoH institutions. It is anticipated that it will help online D/HoH people in enhancing their social and educational skills.

Item Type:	Conference or Workshop Item (Paper)
Schools:	Schools > School of Computing, Science and Engineering
Journal or Publication Title:	Advanced Machine Learning Technologies and Applications : proceedings of AMLTA 2021
Publisher:	Springer
Series Name:	Advances in Intelligent Systems and Computing
ISBN:	9783030697167 (print); 9783030697174 (ebook)
ISSN:	2194-5357
Related URLs:	Publication
Depositing User:	Mr. Said Salloum
Date Deposited:	22 Jun 2021 08:20
Last Modified:	16 Feb 2022 07:10
URI:	https://usir.salford.ac.uk/id/eprint/60336

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