In parallel, young developers was creating the mobile application and the designers designing and rigging the animation avatar. The young researchers also conducted some research on a new way to translate Arabic to a sign gloss. We collected data of Moroccan Sign language from governmental, non-governmental sources and form the web. As a team, we conducted many reviews of research papers about language translation to glosses and sign languages in general and for Modern Standard Arabic in particular. This project brings up young researchers, developers and designers. Our main focus in this current work is to perform Text-to-MSL translation. The translation could be divided into two big parts, the speech-to-text part and the text-to-MSL part. At Laboratoire d’Informatique de Mathématique Appliquée d’Intelligence Artificielle et de Reconnaissance des Formes (LIMIARF ) of Faculty of Sciences of Mohammed V University in Rabat, the Deep Learning Team (DLT) proposed the development of an Arabic Speech-to-MSL translator. Otherwise, teachers use graphics and captioned videos to learn the mappings to signs, but lack tools that translate written or spoken words and concepts into signs.Īround the world, many efforts by different countries have been done to create Machine translations systems from their Language into Sign language. Schools recruit interpreters to help the student understand what is being taught and said in class. However, the involved teachers are mostly hearing, have limited command of MSL and lack resources and tools to teach deaf to learn from written or spoken text. In April 2019, the government standardized the Moroccan Sign Language (MSL) and initiated programs to support the education of deaf children. For many years, they were learning the local variety of sign language from Arabic, French, and American Sign Languages. In Morocco, deaf children receive very little education assistance. Sign language can be represented by a form of annotation called Gloss. However, they are not universal although they have striking similarities. Sign languages are full-fledged natural languages with their own grammar and lexicon. Hand shapes, lip patterns, and facial expressions are used to express emotions and to deliver meanings. Over the world, deaf people use sign language to interact in their community. This leads to a negative impact in their lives and the lives of the people surrounding them. Consequently, they cannot equally access public services, mostly education and health and have no equal rights in participating in an active and democratic life. The main impact of deaf people is on the individual’s ability to communicate with others in addition to the emotional feelings of loneliness and isolation in society. Deaf people mostly have profound hearing loss, which implies very little or no hearing. Hard of hearing people usually communicate through spoken language and can benefit from assistive devices like cochlear implants. Over 5% of the world’s population (466 million people) has disabling hearing loss. The collected corpora of data will train Deep Learning Models to analyze and map Arabic words and sentences against MSL encodings. Project description: To develop an Arabic text to Moroccan Sign Language (MSL) translation product through building two corpora of data on Arabic texts for the use of translation into MSL. Project name: Arabic Speech-to-MSL Translator: ‘Learning for Deaf’ Abdelhak Mahmoudi, Mohammed V University of Rabat, Morocco
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