Open AccessA Translation System That Converts English Text to American Sign Language Enhanced with Deep Learning Modules
Author(s) -
Lalitha Natraj*,
Sujala D. Shetty
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.l3781.1081219
Subject(s) - computer science , machine translation , artificial intelligence , natural language processing , deep learning , sign language , computer assisted translation , example based machine translation , translation (biology) , linguistics , philosophy , biochemistry , chemistry , messenger rna , gene
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.