A Collation of Machine Translation Approaches with Exemplified Comparison of Google and Bing Translators

Pradhan, Ipsita; Mishra, Smita; Nayak, Ajit Kumar

doi:10.1007/978-981-13-8461-5_97

Cited by 3 publications

(1 citation statement)

References 7 publications

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“…It is used for text and speech using artificial intelligence (AI) and natural language processing (NLP). For example, translating from source "English" to target "Arabic" (Pradhan, Mishra& Nayak, 2020;Verma, Srivastava& Kumar, 2015). MT is also used to translate text or speech into video sign language or avatar.…”

Section: Machine Translationmentioning

confidence: 99%

Peer Review #2 of "Towards an Arabic Sign Language (ArSL) corpus for deaf drivers (v0.1)"

Nagumothu¹

2021

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Section: Machine Translationmentioning

confidence: 99%

Peer Review #2 of "Towards an Arabic Sign Language (ArSL) corpus for deaf drivers (v0.1)"

Nagumothu¹

2021

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Effect of Machine Translation on Authorship Attribution

Ouamour,

Sayoud

2023

Smart Innovation, Systems and Technologies

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

Abbas¹,

Al-Barhamtoshy²,

Alotaibi³

2021

PeerJ Computer Science

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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.

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A Collation of Machine Translation Approaches with Exemplified Comparison of Google and Bing Translators

Cited by 3 publications

References 7 publications

Peer Review #2 of "Towards an Arabic Sign Language (ArSL) corpus for deaf drivers (v0.1)"

Peer Review #2 of "Towards an Arabic Sign Language (ArSL) corpus for deaf drivers (v0.1)"

Effect of Machine Translation on Authorship Attribution

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

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