Comparative study on corpora for speech translation

SUMMARYWhile phrase-based statistical machine translation systems prefer to translate with longer phrases, this may cause errors in a free word order language, such as Japanese, in which the order of the arguments of the predicates is not solely determined by the predicates and the arguments can be placed quite freely in the text. In this paper, we propose to reorder the arguments but not the predicates in Japanese using a dependency structure as a kind of reordering. Instead of a single deterministically given permutation, we generate multiple reordered phrases for each sentence and translate them independently. Then we apply a re-ranking method using a discriminative approach by Ranking Support Vector Machines (SVM) to re-score the multiple reordered phrase translations. In our experiment with the travel domain corpus BTEC, we gain a 1.22% BLEU score improvement when only 1-best is used for re-ranking and 4.12% BLEU score improvement when n-best is used for Japanese-English translation.

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“…For the experiment, the Basic Travel Expression Corpus (BTEC) parallel corpus for Japanese-English is used [21].…”

Section: Experiments Resultsmentioning

confidence: 99%

Japanese Argument Reordering Based on Dependency Structure for Statistical Machine Translation

Goh

Watanabe

Sumita

2012

IEICE Trans. Inf. & Syst.

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“…This clearly shows that both BTEC1 and SLDB are required for handling MAD-type tasks. Further discussion is available in [Kikui et al 2006]. …”

Section: Comparative Analysis and Discussionmentioning

confidence: 99%

“…In the following, we focus on multilingual data collection. In order, Basic Travel Expression Corpus (BTEC) [Takezawa et al 2002;Kikui et al 2003] was planned to cover the varieties of situations and expressions.…”

Section: Overview Of Approachmentioning

confidence: 99%

“…The Basic Travel Expression Corpus (BTEC) [Takezawa et al 2002;Kikui et al 2003] was designed to cover utterances for possible travel conversations topic and their translations. Since it is practically impossible to collect them by transcribing actual conversations or simulated dialogs, we decided to use sentences provided by bilingual travel experts based on their experience.…”

Section: Basic Travel Expression Corpus (Btec)mentioning

confidence: 99%

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Multilingual Spoken Language Corpus Development for Communication Research

Takezawa

2006

Chinese Spoken Language Processing

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Multilingual spoken language corpora are indispensable for research on areas of spoken language communication, such as speech-to-speech translation. The speech and natural language processing essential to multilingual spoken language research requires unified structure and annotation, such as tagging. In this study, we describe an experience with multilingual spoken language corpus development at our research institution, focusing in particular on speech recognition and natural language processing for speech translation of travel conversations. An integrated speech and language database, Spoken Language DataBase (SLDB) was planned and constructed. Basic Travel Expression Corpus (BTEC) was planned and constructed to cover a variety of situations and expressions. BTEC and SLDB are designed to be complementary. BTEC is a collection of Japanese sentences and their translations, and SLDB is a collection of transcriptions of bilingual spoken dialogs. Whereas BTEC covers a wide variety of travel domains, SLDB covers a limited domain, i.e., hotel situations. BTEC contains approximately 588k utterance-style expressions, while SLDB contains about 16k utterances. Machine-aided Dialogs (MAD) was developed as a development corpus, and both BTEC and SLDB can be used to handle MAD-type tasks. Field Experiment Data (FED) was developed as the evaluation corpus. We conducted an experiment, and based on analysis of our follow-up questionnaire, roughly half the subjects of the * ATR Spoken

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“…The used data set consists of 500 parallel English and Spanish sentences in form and content close to the Basic Travel Expression Corpus (BTEC) [4]. The sentences were presented two times, each time read by three different Spanish and five different English speakers.…”

Section: Datamentioning

confidence: 99%

Speech translation enhanced automatic speech recognition

Paulik

Stüker

Fügen

et al. 2005

IEEE Workshop on Automatic Speech Recognition and Understanding, 2005.

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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.

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Comparative study on corpora for speech translation

Cited by 61 publications

References 9 publications

Japanese Argument Reordering Based on Dependency Structure for Statistical Machine Translation

Japanese Argument Reordering Based on Dependency Structure for Statistical Machine Translation

Multilingual Spoken Language Corpus Development for Communication Research

Speech translation enhanced automatic speech recognition

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