Arabic Text to Arabic Sign Language Translation System for the Deaf and Hearing-Impaired Community

Abstract

This paper describes a machine translation system that offers many deaf and hearing impaired people the chance to access published information in Arabic by translating text into their first language, Arabic Sign Language (ArSL). The system was created under the close guidance of a team that included three deaf native signers and one ArSL interpreter. We discuss problems inherent in the design and development of such translation systems and review previous ArSL machine translation systems, which all too often demonstrate a lack of collaboration between engineers and the deaf community. We describe and explain in detail both the adapted translation approach chosen for the proposed system and the ArSL corpus that we collected for this purpose. The corpus has 203 signed sentences (with 710 distinct signs) with content restricted to the domain of instructional language as typically used in deaf education. Evaluation shows that the system produces translated sign sentences outputs with an average word error rate of 46.7% and an average position error rate of 29.4% using leave-one out cross validation. The most frequent source of errors is missing signs in the corpus; this could be addressed in future by collecting more corpus material.

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