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

Abstract

Arabic Sign Language (ArSL) is a visual language used by deaf Arabs and some hard of hearing to translate their thinking. It is a language in its own right as well as spoken languages such as Arabic or English. It is produced by body, face, and gestures of the hands. Hundreds of thousands of deaf people around the world currently practice it. Each country has its own sign language that deaf people use so it is not universal, but deaf people from different countries communicate easily with each other after a short period of adaptation. This communication becomes difficult when a deaf want to communicate with a normal person with no interpreter available. In this way, building a translation system that can generate real-time statements via a signing avatar is helpful. The system to be developed is a machine translation system from Arabic text to the Arabic sign language, it will allow hearing persons to communicate more easily, using a text written in Arabic by normal person, with people suffering from hard hearing and knowing only sign language. The system must perform a morpho-syntactic analysis of the text in the input and convert it to video sequences phrases playing by a 3D human avatar who expresses the usual signs used by deaf people. In this paper, we present a first version of our machine translation system ATLASLang MTS 1 based on rule-based Interlingua and example-based approaches. It uses SAFAR Platform [11] and ALKHALIL morpho system [1] 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 based on well-established translation rules and the database of signs. During the translation, if a word of sentence is a proper noun or does not have a correspondence in the database, it will be finger spelled. The main constraint imposed by our machine translation system is the assumption that the sentence given in the input must be completely vowelized.