This paper describes the speech-to-text systems used to provide automatic transcriptions used in the Quaero 2010 evaluation of Machine Translation from speech. Quaero (www.quaero.org) is a large research and industrial innovation program focusing on technologies for automatic analysis and classification of multimedia and multilingual documents. The ASR transcript is the result of a Rover combination of systems from three teams ( KIT, RWTH, LIMSI+VR) for the French and German languages. The casesensitive word error rates (WER) of the combined systems were respectively 20.8% and 18.1% on the 2010 evaluation data, relative WER reductions of 14.6% and 17.4% respectively over the best component system.

Cet article présente nos premiers travaux en vue de la construction d’un système de traduction probabiliste pour le couple de langue vietnamien-français. La langue vietnamienne étant considérée comme une langue peu dotée, une des difficultés réside dans la constitution des corpus parallèles, indispensable à l’apprentissage des modèles. Nous nous concentrons sur la constitution d’un grand corpus parallèle vietnamien-français. La méthode d’identification automatique des paires de documents parallèles fondée sur la date de publication, les mots spéciaux et les scores d’alignements des phrases est appliquée. Cet article présente également la construction d’un premier système de traduction automatique probabiliste vietnamienfrançais et français-vietnamien à partir de ce corpus et discute l’opportunité d’utiliser des unités lexicales ou sous-lexicales pour le vietnamien (syllabes, mots, ou leurs combinaisons). Les performances du système sont encourageantes et se comparent avantageusement à celles du système de Google.

This paper is a description of the system presented by the LIG laboratory to the IWSLT07 speech translation evaluation. The LIG participated, for the first time this year, in the Arabic to English speech translation task. For translation, we used a conventional statistical phrase-based system developed using the moses open source decoder. Our baseline MT system is described and we discuss particularly the use of an additional bilingual dictionary which seems useful when few training data is available. The main contribution of this paper concerns the proposal of a lattice decomposition algorithm that allows transforming a word lattice into a sub word lattice compatible with our MT model that uses word segmentation on the Arabic part. The lattice is then transformed into a confusion network which can be directly decoded into moses. The results show that this method outperforms the conventional 1-best translation which consists in translating only the most probable ASR hypothesis. The best BLEU score, from ASR output obtained on IWSLT06 evaluation data is 0.2253. The results confirm the interest of full CN decoding for speech translation, compared to traditional ASR 1-best approach. Our primary system was ranked 7/14 for IWSLT07 AE ASR task with a BLEU score of 0.3804.