ATLASLang MTS 1: Arabic Text Language into Arabic Sign Language Machine Translation System

Brour, Mourad; Benabbou, Abderrahim

doi:10.1016/j.procs.2019.01.066

Cited by 32 publications

(15 citation statements)

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“…In comparing the results in this research with others [9] In according of table 9, the current system is easily suitable for arabic users where the input of the system is nonvowelized sentence, and more morphological features are determined like human feminine. The current system depends on Farasa and Stanford which give one result compared with ALKHALIL which gives many results, or maybe doesn't give any result.…”

Section: )mentioning

confidence: 96%

“…A first version of machine translation system ATLASLang MTS 1 is presented in [9] based on rule-based Interlingua and example-based approaches, the system uses SAFAR Platform and ALKHALIL morph to extract the morphological properties of each word of the input sentence, then it generates a video sequence representing the sentence in arabic sign language, the input of the system must be completely vowelized.…”

Section: Introductionmentioning

confidence: 99%

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Support of Arabic Sign Language Machine Translation based on Morphological processing

Asjea¹,

Ismail²,

Khawatmi³

2019

IJCA

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This paper presents a morphological processing system as a part of arabic text to arabic sign language machine translation system. This morphological processing depends on Farasa analyzer tool, Stanford model and Arramooz lexicon. The characteristics of sign language are achieved to get intermediate arabic sign language sentences. Then these sentences are searched in a sign language dictionary word by word to display the related signs images if available, or to display letters of word using finger spelling alphabet images. The proposed system is tested on many non-vowelized arabic sentences, and good results and high accuracy are obtained.

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Section: )mentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Support of Arabic Sign Language Machine Translation based on Morphological processing

Asjea¹,

Ismail²,

Khawatmi³

2019

IJCA

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“…ATLASLang machine translation system makes use of signing avatar to translate Arabic text to Arabic sign language. A morpho-syntactic analysis is performed based on rule-based and example-based approaches for the input sentence and displays the sequence of sign language gif images [16]. A virtual keyboard is designed to translate an Arabic sentence into Arabic sign language in the form of sign images [4].…”

Section: Related Workmentioning

confidence: 99%

An automatic machine translation system for multi-lingual speech to Indian sign language

Dhanjal

Singh

2021

Multimed Tools Appl

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Sign language (SL) is the best suited communication medium for hearing impaired people. Even with the advancement of technology, there is a communication gap between the hearing impaired and hearing people. The aim of this research work is to bridge this gap by developing an automatic system that translates the speech to Indian Sign Language using Avatar (SISLA). The whole system works in three phases: (i) The first phase includes the speech recognition (SR) of isolated words for English, Hindi and Punjabi in speaker independent environment (ii) The second phase translates the source language into Indian Sign Language (ISL) (iii) HamNoSys based 3D avatar represents the ISL gestures. The four major implementation modules for SISLA include: requirement analysis, data collection, technical development and evaluation. The multi-lingual feature makes the system more efficient. The training and testing speech sample files for English (12,660, 4218), Hindi (12,610, 4211) and Punjabi (12,600, 4193) have been used to train and test the SR models. Empirical results of automatic machine translation show that the proposed trained models have achieved the minimum accuracy of 91%, 89% and 89% for English, Punjabi and Hindi respectively. Sign language experts have also been used to evaluate the sign error rate through feedback. Future directions to enhance the proposed system using non-manual SL features along with the sentence level translation has been suggested. Usability testing based on survey results confirm that the proposed SISLA system is suitable for education as well as communication purpose for hearing impaired people.

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“…O projecto ATLASLang (Brour & Benabbou, 2019), um sistema híbrido de tradução de textoárabe (os autores não explicitam a variante doárabe) em língua gestualárabe, baseado em regras e em exemplos de frases (e das suas traduções) definidas num corpus bilingue. Se a frase existir nesse corpus, entãoé diretamente traduzida, caso contrário, a fraseé processada e aplicam-se regras manuais.…”

Section: Trabalho Relacionadounclassified

PE2LGP: tradutor de português europeu para língua gestual portuguesa em glosas

Gonçalves¹,

Coheur

Nicolau³

et al. 2021

Linguamática

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A língua gestual portuguesa, tal como a língua portuguesa, evoluiu de forma natural, adquirindo características gramaticais distintas do português. Assim, o desenvolvimento de um tradutor entre as duas não consiste somente no mapeamento de uma palavra num gesto (português gestuado), mas em garantir que os gestos resultantes satisfazem a gramática da língua gestual portuguesa e que as traduções estejam semanticamente corretas. Trabalhos desenvolvidos anteriormente utilizam exclusivamente regras de tradução manuais, sendo muito limitados na quantidade de fenómenos gramaticais abrangidos, produzindo pouco mais que português gestuado. Neste artigo, apresenta-se o primeiro sistema de tradução de português para a língua gestual portuguesa, o PE2LGP, que, para além de regras manuais, se baseia em regras de tradução construídas automaticamente a partir de um corpus de referência. Dada uma frase em português, o sistema devolve uma sequência de glosas com marcadores que identificam expressões faciais, palavras soletradas, entre outras. Uma avaliação automática e uma avaliação manual são apresentadas, indicando os resultados melhorias na qualidade da tradução de frases simples e pequenas em comparação ao sistema baseline (português gestuado). Este é, também, o primeiro trabalho que lida com as expressões faciais gramaticais que marcam as frases interrogativas e negativas.

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ATLASLang MTS 1: Arabic Text Language into Arabic Sign Language Machine Translation System

Cited by 32 publications

References 1 publication

Support of Arabic Sign Language Machine Translation based on Morphological processing

Support of Arabic Sign Language Machine Translation based on Morphological processing

An automatic machine translation system for multi-lingual speech to Indian sign language

PE2LGP: tradutor de português europeu para língua gestual portuguesa em glosas

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