This paper describes ATR’s hybrid approach to spoken language translation and it’s application to the IWSLT 2005 translation task. Multiple corpus-based translation engines are used to translate the same input, whereby the best translation among the element MT outputs is selected according to statistical models. The evaluation results of the Japanese-to-English and Chinese-to-English translation tasks for different training data conditions showed the potential of the proposed hybrid approach and revealed new directions in how to improve the current system performance. 1.

Abstract. This paper investigates the combination of word-alignments computed with the competitive linking algorithm and well-established IBM models. New training methods for phrase-based statistical translation are proposed, which have been evaluated on a popular traveling domain task, with English as target language, and Chinese, Japanese, Arabic and Italian as source languages. Experiments were performed with a highly competitive phrase-based translation system, which ranked at the top in the 2005 IWSLT evaluation campaign. By applying the proposed techniques, even under very different data-sparseness conditions, consistent improvements in BLEU and NIST scores were obtained on all considered language pairs. 1

Nowadays official documents have to be made available in many languages, like for example in the EU with its 20 official languages. Therefore, the need for effective tools to aid the multitude of human translators in their work becomes easily apparent. An ASR system, enabling the human translator to speak his translation in an unrestricted manner, instead of typing it, constitutes such a tool. In this work we improve the recognition performance of such an ASR system on the target language of the human translator by taking advantage of an either written or spoken source language representation. To do so, machine translation techniques are used to translate between the different languages and then the involved ASR systems are biased towards the gained knowledge. We present an iterative approach for ASR improvement and outperform our baseline system by a relative word error rate reduction of 35.8% / 29.9% in the case of a written / spoken source language representation. Further, we show how multiple target languages, as for example provided by different simultaneous translators during European Parliament debates, can be incorporated into our system design for an improvement of all involved ASR systems.

An important issue in speech translation is to minimize the negative effect of speech recognition errors on machine translation. We propose a novel statistical machine translation decoding algorithm for speech translation to improve speech translation quality. The algorithm can translate the speech recognition word lattice, where more hypotheses are utilized to bypass the misrecognized single-best hypothesis. The decoding involves converting the recognition word lattice to a translation word graph by a graph-based search, followed by a fine rescoring by an A * search. We show that a speech recognition confidence measure implemented by posterior probability is effective to improve speech translation. The proposed techniques were tested in a Japanese-to-English speech translation task, in which we measured the translation results in terms of a number of automatic evaluation metrics. The experimental results demonstrate a consistent and significant improvement in speech translation achieved by the proposed techniques. 1

Abstract. In 2008 the Olympics Games will be held in Beijing. For this

We describe the TÜBITAK-UEKAE system that participated in the Arabic-to-English and Japanese-to-English translation tasks of the IWSLT 2007 evaluation campaign. Our system is built on the open-source phrasebased statistical machine translation software Moses. Among available corpora and linguistic resources, only the supplied training data and an Arabic morphological analyzer are used in the system. We present the run-time lexical approximation method to cope with out-of-vocabulary words during decoding. We tested our system under both automatic speech recognition (ASR) and clean transcript (clean) input conditions. Our system was ranked first in both Arabic-to-English and Japanese-to-English tasks under the “clean” condition. 1.

This paper reports on activities carried out under the European project PF-STAR and within the CSTAR consortium, which aim at evaluating speech translation technologies. In PF-STAR, speech translation baselines developed by the partners and off-the-shelf commercial systems will be compared systematically on several language pairs and application scenarios. In CSTAR, evaluation campaigns will be organized, on a regular basis, to compare research baselines developed by the members of the consortium. The first evaluation campaign, which will take place in 2003, will focus on written language translation by exploiting a large phrase-book parallel corpus covering several European and Asiatic languages.

This paper describes the latest version of speech-to-speech translation systems developed by the team of NICT-ATR for over twenty years. The system is now ready to be deployed for the travel domain. A new noise-suppression technique notably improves speech recognition performance. Corpus-based approaches of recognition, translation, and synthesis enable coverage of a wide variety of topics and portability to other languages. 1

We intended to demonstrate the effect of using N-best speech recognition hypotheses for improving speech translation performance. A log-linear model, which integrated features from speech recognition and statistical machine translation, was used to rescore the translation candidates. Model parameters were estimated by optimizing an objectively measurable but subjectively relevant translation quality metric. Experimental results have shown that the proposed N-best approach improved translation quality over the conventional single-best approach. The improvements were confirmed consistently by several automatic translation evaluation metrics. 1.

Assisting in foreign language learning is one of the major areas in which natural language processing technology can contribute. This paper proposes a computerized method of measuring communicative skill in English as a foreign language. The proposed method consists of two parts. The first part involves a test sentence selection part to achieve precise measurement with a small test set. The second part is the actual measurement, which has three steps. Step one asks proficiency-known human subjects to translate Japanese sentences into English. Step two gauges the match between the translations of the subjects and correct translations based on the n-gram overlap or the edit distance between translations. Step three learns the relationship between proficiency and match. By regression it finds a straight-line fitting for the scatter plot representing the proficiency and matches of the subjects. Then, it estimates proficiency of proficiency-unknown users by using