scholarly journals A multi-path architecture for machine translation of English text into American Sign Language animation

2004 ◽  
Author(s):  
Matt Huenerfauth
Keyword(s):  
American Sign Language ◽  
Sign Language ◽  
Machine Translation ◽  
English Text ◽  
American Sign

scholarly journals A Translation System That Converts English Text to American Sign Language Enhanced with Deep Learning Modules

2019 ◽  
Vol 8 (12) ◽  
pp. 5378-5383
Keyword(s):  
Deep Learning ◽  
American Sign Language ◽  
Sign Language ◽  
Machine Translation ◽  
Language Processing ◽  
Rapid Development ◽  
English Text ◽  
Translation System ◽  
American Sign ◽  
Rule Based

A recent surge in interest to create translation systems inclusive of sign languages is engendered by not only the rapid development of various approaches in the field of machine translation, but also the increased awareness of the struggles of the deaf community to comprehend written English. This paper describes the working of SILANT (SIgn LANguage Translator), a machine translation system that converts English to American Sign Language (ASL) using the principles of Natural Language Processing (NLP) and Deep Learning. The translation of English text is based on transformational rules which generates an intermediate representation which in turn spawns appropriate ASL animations. Although this kind of rule-based translation is notorious for being an accurate yet narrow approach, in this system, we broaden the scope of the translation using a synonym network and paraphrasing module which implements deep learning algorithms. In doing so, we are able to achieve both the accuracy of a rule-based approach and the scale of a deep learning one.

Designing High Accuracy Statistical Machine Translation for Sign Language Using Parallel Corpus

2019 ◽  
Vol 12 (2) ◽  
pp. 134-158 ◽  
Author(s):  
Achraf Othman ◽  
Mohamed Jemni
Keyword(s):  
American Sign Language ◽  
Sign Language ◽  
Machine Translation ◽  
Statistical Machine Translation ◽  
English Text ◽  
Parallel Corpus ◽  
Transcription System ◽  
Novel Approach ◽  
Alignment Algorithms ◽  
Evaluation Metric

In this article, the authors deal with the machine translation of written English text to sign language. They study the existing systems and issues in order to propose an implantation of a statistical machine translation from written English text to American Sign Language (English/ASL) taking care of several features of sign language. The work proposes a novel approach to build artificial corpus using grammatical dependencies rules owing to the lack of resources for sign language. The parallel corpus was the input of the statistical machine translation, which was used for creating statistical memory translation based on IBM alignment algorithms. These algorithms were enhanced and optimized by integrating the Jaro–Winkler distances in order to decrease training process. Subsequently, based on the constructed translation memory, a decoder was implemented for translating English text to the ASL using a novel proposed transcription system based on gloss annotation. The results were evaluated using the BLEU evaluation metric.

Spatial and temporal dynamics of lexico-semantic processing in American Sign Language

2011 ◽  
Author(s):  
M. Leonard ◽  
N. Ferjan Ramirez ◽  
C. Torres ◽  
M. Hatrak ◽  
R. Mayberry ◽  
...  
Keyword(s):  
American Sign Language ◽  
Sign Language ◽  
Semantic Processing ◽  
Temporal Dynamics ◽  
American Sign ◽  
Spatial And Temporal Dynamics

Patient Health Questionnaire-9--American Sign Language Version

2018 ◽  
Author(s):  
Leslie Pertz ◽  
Missy Plegue ◽  
Kathleen Diehl ◽  
Philip Zazove ◽  
Michael McKee
Keyword(s):  
American Sign Language ◽  
Sign Language ◽  
Patient Health Questionnaire ◽  
American Sign ◽  
Health Questionnaire ◽  
Language Version ◽  
Patient Health
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