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 South African Sign Language Machine Translation Project

This project aimed to build a machine translation system from South African Sign Language (SASL) to English text. Prior research has mainly focused on translation of English text to a sign language through the use of avatar systems. However, recent advances in technology in gesture recognition and animation can accurately capture and render facial, manual and body gestures.  Thus, the pieces required to build a fully-fledged translation system are in place, however synthesizing the output of gesture recognition into translatable linguistic text is needed.  This project focused on creating an annotation schema suitable for text translation, as well as building a sign language to English bilingual corpus that can be used for machine learning of linguistic patterns. Creating an annotation schema, building and annotating a corpus requires linguistic knowledge, knowledge of gesture recognition systems, and sign language proficiency.

This project was a collaboration between the Computer Science and English Language & Linguistics departments at Rhodes. It built on the Introduction to Sign Language Linguistics module taught in English Language and Linguistics 2 and built on the gesture recognition and rendering work done by the Integration of Signed and Verbal Communication: South African Sign Language Recognition, Animation and Translation Group (SASL Group) at the University of the Western Cape.

This project provided research opportunities for students in Linguistics as well as Computer Science. Scholarly impact included further knowledge about SASL grammar on which there is very little published research. 

Contact Ian Siebörger for more details at ian.sieborger@ru.ac.za

Last Modified :Tue, 05 Jan 2016 14:01:19 SAST