Amharic Sentence to Ethiopian Sign Language Translator

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Addis Ababa University


Sign languages that exist around the world are usually identified by the country where they are used such as Ethiopian sign language. Mostly, the communication among the hearing impaired people involves signs that stand for words by themselves. However, to make a sign language complete as a spoken language, the hearing impaired community around the world use manual alphabets for names, technical terms, and sometimes for emphasis. As there are different alphabets for different spoken languages such as Amharic, there are manual alphabets or finger spellings used by the deaf people. Therefore sign language in general is a tool that deaf communities use to communicate with each other. There is no problem when the communication is limited between the deaf, but they struggle to communicate with hearing people due to the language barrier. Using translators was the solution for filling the communication gap especially in Ethiopia, even if it has its own draw backs with respect to economy or privacy issue. Consequently, developing software which fills the communication gap between the deaf and hearing people is a best solution. This thesis contributes on the development of a model and system for Amharic sentence to Ethiopian sign language translator which accepts Amharic sentences, letters, or numbers, and outputs 3D animation of Ethiopian sign language based on the pre-lingual deaf grammar. The model bases on rule based machine translation approaches and the developed system has three basic components; the interface component, the back-end component, and the database component. The first component (front-end) acts as a bridge between the users and the back-end component. The back-end component has three modules; Amharic text analysis, natural language processing (NLP), and text-to-sign mapping. Amharic text analysis modules analyze Amharic sentence and pass Romanized sentence to the NLP module. The NLP module accepts the Romanized Amharic sentence and performs all language processing and return sentence in EthSL with including of morphological information. Then the final module (text-to-sign mapping) maps each word with the SiGML (sign script) and send to the interface component and the 3D avatar animation display the sign. In addition to enhance the quality of the translator we use a POS tagging which combine the previous work (naïve Byes classifier) and the new created one; using a brill tagging approach. x The translator performance evaluated into three classes; at sentences level, letter level, and number level and the result ranked into three categories; number of correctly translated sentences, number of understandable sentences, and number of wrong translations. All results without any errors were considered as correctly translated sentences. The results that conveyed meaning but not clear sense were considered as understandable sentences. But the results that did not covey meaning as well as sense were considered as wrong translations. Finally the system gave an accuracy of 58.77%, 75.76%, and 84% at sentence, letter, and number level respectively



Ethiopian; Sign; Language