We present a novel approach for the automatic translation of written text into sign language. A new corpus focussing on the weather report domain for the language pair German and German Sign Language is introduced. We apply phrase-based statistical machine translation, enhanced by pre- and post-processing steps based on the morpho-syntactical analysis of German. Detailed results are given based on automatic and manual evaluation. 1

Abstract: This paper discusses the development of an automatic machine translation (MT) system for translating spoken language text into signed languages (SLs). The motivation for our work is the improvement of accessibility to airport information announcements for D/deaf and hard of hearing people. This paper demonstrates the involvement of Deaf colleagues and members of the D/deaf community in Ireland in three areas of our research: the choice of a domain for automatic translation that has a practical use for the D/deaf community; the human translation of English text into Irish Sign Language (ISL) as well as advice on ISL grammar and linguistics; and the importance of native ISL signers as manual evaluators of our translated output. Keywords: sign language, machine translation, D/deaf accessibility 1.

Abstract. This paper describes progress in a development of the human-human dialogue corpus for machine translation of spoken language. We have chosen a semantically annotated corpus of phone calls to a train timetable information center. The phone calls consist of inquiries regarding their train traveler plans. Corpus dialogue act tags incorporate abstract semantic meaning. We have enriched a part of the corpus with Sign Speech translation and we have proposed methods how to do automatic machine translation from Czech to Sign Speech using semantic annotation contained in the corpus.

This paper evaluates different approaches on speech to sign language machine translation. The framework of the application focuses on assisting deaf people to apply for the passport or related information. In this context, the main aim is to automatically translate the spontaneous speech, uttered by an officer, into Spanish Sign Language (SSL). In order to get the best translation quality, three alternative techniques have been evaluated: a rule-based approach, a phrase-based statistical approach, and a approach that makes use of stochastic finite state transducers. The best speech translation experiments have reported a 32.0 % SER (Sign Error Rate) and a 7.1 BLEU (BiLingual Evaluation Understudy) including speech recognition errors.

This paper presents a successful technique for creating a new language model (LM) that adapts the original target LM used by a machine translation (MT) system. This technique is especially useful for situations where there are very scarce resources for training the target side (Spanish Sign Language (LSE) in our case) in order to properly estimate the target LM, the Sign Language Model (SLM), used by the MT system. The technique uses information from the source language, Spanish in our task, and from the phrase-based translation matrix in order to create a new LM, estimated using web frequencies, which adapts the counts of the SLM through the Maximum A Posteriori method (MAP). The corpus consists of common used sentences spoken by an officer when assisting people in applying for, or renewing, the National Identification Document. The proposed technique allows relative reductions of 15.5 % on perplexity and 2.7% on WER for translation, which are close to half the maximum performance obtainable when only the LM is optimized. Index Terms: language model adaptation, machine translation, sign language, web counts.

Abstract—This work is first attempt towards English to Urdu Machine Translation (MT) using example based approach. We have developed an interactive MT system to facilitate the user to customize the translation to his needs, thereby improving the performance of the translation. Our MT system supports idioms, homographs, and some other features in addition to the ability of the bilingual corpus to evolve. In the end, we compare the features of the MT system developed by the center for research in Urdu Language Processing (CRULP) with those of our MT system. Keywords-Machine Translation; Bilingual Corpus I.

Abstract: The SignSpeak project will be the first step to approach sign language recognition and translation at levels already obtained in similar technologies such as automatic speech recognition or statistical machine translation of spoken languages. Deaf communities revolve around sign languages as they are their natural means of communication. Although deaf, hard of hearing and hearing signers can communicate without boundaries amongst themselves, there is a serious challenge for the deaf community in trying to integrate into educational, social and work environments. The overall goal of SignSpeak is to develop a new vision-based technology for recognizing and translating continuous sign language to text. New knowledge about the nature of sign language structure from the perspective of machine recognition of continuous sign language will allow a subsequent breakthrough in the development of a new vision-based technology for continuous sign language recognition and translation. Existing and new publicly available corpora will be used to evaluate the research progress throughout the whole project. 1