Towards an Arabic Sign Language (ArSL) corpus for deaf drivers

Sign language is a common language that deaf people around the world use to communicate with others. However, normal people are generally not familiar with sign language (SL) and they do not need to learn their language to communicate with them in everyday life. Several technologies offer possibilit...

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Autores principales:	Samah Abbas, Hassanin Al-Barhamtoshy, Fahad Alotaibi
Formato:	article
Lenguaje:	EN
Publicado:	PeerJ Inc. 2021
Materias:	Arabic sign language Speech recognition Sign language recognition Natural language processing Deaf driver in Saudi Arabia Deaf driver corpus Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/1840b28036cb44f795bed32c5c7aa122
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id	oai:doaj.org-article:1840b28036cb44f795bed32c5c7aa122
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spelling	oai:doaj.org-article:1840b28036cb44f795bed32c5c7aa1222021-11-21T15:05:05ZTowards an Arabic Sign Language (ArSL) corpus for deaf drivers10.7717/peerj-cs.7412376-5992https://doaj.org/article/1840b28036cb44f795bed32c5c7aa1222021-11-01T00:00:00Zhttps://peerj.com/articles/cs-741.pdfhttps://peerj.com/articles/cs-741/https://doaj.org/toc/2376-5992Sign language is a common language that deaf people around the world use to communicate with others. However, normal people are generally not familiar with sign language (SL) and they do not need to learn their language to communicate with them in everyday life. Several technologies offer possibilities for overcoming these barriers to assisting deaf people and facilitating their active lives, including natural language processing (NLP), text understanding, machine translation, and sign language simulation. In this paper, we mainly focus on the problem faced by the deaf community in Saudi Arabia as an important member of the society that needs assistance in communicating with others, especially in the field of work as a driver. Therefore, this community needs a system that facilitates the mechanism of communication with the users using NLP that allows translating Arabic Sign Language (ArSL) into voice and vice versa. Thus, this paper aims to purplish our created dataset dictionary and ArSL corpus videos that were done in our previous work. Furthermore, we illustrate our corpus, data determination (deaf driver terminologies), dataset creation and processing in order to implement the proposed future system. Therefore, the evaluation of the dataset will be presented and simulated using two methods. First, using the evaluation of four expert signers, where the result was 10.23% WER. The second method, using Cohen’s Kappa in order to evaluate the corpus of ArSL videos that was made by three signers from different regions of Saudi Arabia. We found that the agreement between signer 2 and signer 3 is 61%, which is a good agreement. In our future direction, we will use the ArSL video corpus of signer 2 and signer 3 to implement ML techniques for our deaf driver system.Samah AbbasHassanin Al-BarhamtoshyFahad AlotaibiPeerJ Inc.articleArabic sign languageSpeech recognitionSign language recognitionNatural language processingDeaf driver in Saudi ArabiaDeaf driver corpusElectronic computers. Computer scienceQA75.5-76.95ENPeerJ Computer Science, Vol 7, p e741 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Arabic sign language Speech recognition Sign language recognition Natural language processing Deaf driver in Saudi Arabia Deaf driver corpus Electronic computers. Computer science QA75.5-76.95
spellingShingle	Arabic sign language Speech recognition Sign language recognition Natural language processing Deaf driver in Saudi Arabia Deaf driver corpus Electronic computers. Computer science QA75.5-76.95 Samah Abbas Hassanin Al-Barhamtoshy Fahad Alotaibi Towards an Arabic Sign Language (ArSL) corpus for deaf drivers
description	Sign language is a common language that deaf people around the world use to communicate with others. However, normal people are generally not familiar with sign language (SL) and they do not need to learn their language to communicate with them in everyday life. Several technologies offer possibilities for overcoming these barriers to assisting deaf people and facilitating their active lives, including natural language processing (NLP), text understanding, machine translation, and sign language simulation. In this paper, we mainly focus on the problem faced by the deaf community in Saudi Arabia as an important member of the society that needs assistance in communicating with others, especially in the field of work as a driver. Therefore, this community needs a system that facilitates the mechanism of communication with the users using NLP that allows translating Arabic Sign Language (ArSL) into voice and vice versa. Thus, this paper aims to purplish our created dataset dictionary and ArSL corpus videos that were done in our previous work. Furthermore, we illustrate our corpus, data determination (deaf driver terminologies), dataset creation and processing in order to implement the proposed future system. Therefore, the evaluation of the dataset will be presented and simulated using two methods. First, using the evaluation of four expert signers, where the result was 10.23% WER. The second method, using Cohen’s Kappa in order to evaluate the corpus of ArSL videos that was made by three signers from different regions of Saudi Arabia. We found that the agreement between signer 2 and signer 3 is 61%, which is a good agreement. In our future direction, we will use the ArSL video corpus of signer 2 and signer 3 to implement ML techniques for our deaf driver system.
format	article
author	Samah Abbas Hassanin Al-Barhamtoshy Fahad Alotaibi
author_facet	Samah Abbas Hassanin Al-Barhamtoshy Fahad Alotaibi
author_sort	Samah Abbas
title	Towards an Arabic Sign Language (ArSL) corpus for deaf drivers
title_short	Towards an Arabic Sign Language (ArSL) corpus for deaf drivers
title_full	Towards an Arabic Sign Language (ArSL) corpus for deaf drivers
title_fullStr	Towards an Arabic Sign Language (ArSL) corpus for deaf drivers
title_full_unstemmed	Towards an Arabic Sign Language (ArSL) corpus for deaf drivers
title_sort	towards an arabic sign language (arsl) corpus for deaf drivers
publisher	PeerJ Inc.
publishDate	2021
url	https://doaj.org/article/1840b28036cb44f795bed32c5c7aa122
work_keys_str_mv	AT samahabbas towardsanarabicsignlanguagearslcorpusfordeafdrivers AT hassaninalbarhamtoshy towardsanarabicsignlanguagearslcorpusfordeafdrivers AT fahadalotaibi towardsanarabicsignlanguagearslcorpusfordeafdrivers
_version_	1718418809443844096

Towards an Arabic Sign Language (ArSL) corpus for deaf drivers

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