Kevin Kilgour


2020

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Proceedings of the 2nd Workshop on Life-long Learning for Spoken Language Systems
William M. Campbell | Alex Waibel | Dilek Hakkani-Tur | Timothy J. Hazen | Kevin Kilgour | Eunah Cho | Varun Kumar | Hadrien Glaude
Proceedings of the 2nd Workshop on Life-long Learning for Spoken Language Systems

2016

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Lecture Translator - Speech translation framework for simultaneous lecture translation
Markus Müller | Thai Son Nguyen | Jan Niehues | Eunah Cho | Bastian Krüger | Thanh-Le Ha | Kevin Kilgour | Matthias Sperber | Mohammed Mediani | Sebastian Stüker | Alex Waibel
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations

2015

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The 2015 KIT IWSLT speech-to-text systems for English and German
Markus Mueller | Tai Son Nguyen | Matthias Sperber | Kevin Kilgour | Sebastian Stuker | Alex Waibel
Proceedings of the 12th International Workshop on Spoken Language Translation: Evaluation Campaign

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Multifeature modular deep neural network acoustic models
Kevin Kilgour | Alex Waibel
Proceedings of the 12th International Workshop on Spoken Language Translation: Papers

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Punctuation insertion for real-time spoken language translation
Eunah Cho | Jan Niehues | Kevin Kilgour | Alex Waibel
Proceedings of the 12th International Workshop on Spoken Language Translation: Papers

2014

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The 2014 KIT IWSLT speech-to-text systems for English, German and Italian
Kevin Kilgour | Michael Heck | Markus Müller | Matthias Sperber | Sebastian Stüker | Alex Waibel
Proceedings of the 11th International Workshop on Spoken Language Translation: Evaluation Campaign

This paper describes our German, Italian and English Speech-to-Text (STT) systems for the 2014 IWSLT TED ASR track. Our setup uses ROVER and confusion network combination from various subsystems to achieve a good overall performance. The individual subsystems are built by using different front-ends, (e.g., MVDR-MFCC or lMel), acoustic models (GMM or modular DNN) and phone sets and by training on various subsets of the training data. Decoding is performed in two stages, where the GMM systems are adapted in an unsupervised manner on the combination of the first stage outputs using VTLN, MLLR, and cMLLR. The combination setup produces a final hypothesis that has a significantly lower WER than any of the individual subsystems.

2013

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The 2013 KIT IWSLT speech-to-text systems for German and English
Kevin Kilgour | Christian Mohr | Michael Heck | Quoc Bao Nguyen | Van Huy Nguyen | Evgeniy Shin | Igor Tseyzer | Jonas Gehring | Markus Müller | Matthias Sperber | Sebastian Stüker | Alex Waibel
Proceedings of the 10th International Workshop on Spoken Language Translation: Evaluation Campaign

This paper describes our English Speech-to-Text (STT) systems for the 2013 IWSLT TED ASR track. The systems consist of multiple subsystems that are combinations of different front-ends, e.g. MVDR-MFCC based and lMel based ones, GMM and NN acoustic models and different phone sets. The outputs of the subsystems are combined via confusion network combination. Decoding is done in two stages, where the systems of the second stage are adapted in an unsupervised manner on the combination of the first stage outputs using VTLN, MLLR, and cMLLR.

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Maximum entropy language modeling for Russian ASR
Evgeniy Shin | Sebastian Stüker | Kevin Kilgour | Christian Fügen | Alex Waibel
Proceedings of the 10th International Workshop on Spoken Language Translation: Papers

Russian is a challenging language for automatic speech recognition systems due to its rich morphology. This rich morphology stems from Russian’s highly inflectional nature and the frequent use of preand suffixes. Also, Russian has a very free word order, changes in which are used to reflect connotations of the sentences. Dealing with these phenomena is rather difficult for traditional n-gram models. We therefore investigate in this paper the use of a maximum entropy language model for Russian whose features are specifically designed to deal with the inflections in Russian, as well as the loose word order. We combine this with a subword based language model in order to alleviate the problem of large vocabulary sizes necessary for dealing with highly inflecting languages. Applying the maximum entropy language model during re-scoring improves the word error rate of our recognition system by 1.2% absolute, while the use of the sub-word based language model reduces the vocabulary size from 120k to 40k and the OOV rate from 4.8% to 2.1%.

2012

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The 2012 KIT and KIT-NAIST English ASR systems for the IWSLT evaluation
Christian Saam | Christian Mohr | Kevin Kilgour | Michael Heck | Matthias Sperber | Keigo Kubo | Sebatian Stüker | Sakriani Sakri | Graham Neubig | Tomoki Toda | Satoshi Nakamura | Alex Waibel
Proceedings of the 9th International Workshop on Spoken Language Translation: Evaluation Campaign

This paper describes our English Speech-to-Text (STT) systems for the 2012 IWSLT TED ASR track evaluation. The systems consist of 10 subsystems that are combinations of different front-ends, e.g. MVDR based and MFCC based ones, and two different phone sets. The outputs of the subsystems are combined via confusion network combination. Decoding is done in two stages, where the systems of the second stage are adapted in an unsupervised manner on the combination of the first stage outputs using VTLN, MLLR, and cM-LLR.

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The KIT-NAIST (contrastive) English ASR system for IWSLT 2012
Michael Heck | Keigo Kubo | Matthias Sperber | Sakriani Sakti | Sebastian Stüker | Christian Saam | Kevin Kilgour | Christian Mohr | Graham Neubig | Tomoki Toda | Satoshi Nakamura | Alex Waibel
Proceedings of the 9th International Workshop on Spoken Language Translation: Evaluation Campaign

This paper describes the KIT-NAIST (Contrastive) English speech recognition system for the IWSLT 2012 Evaluation Campaign. In particular, we participated in the ASR track of the IWSLT TED task. The system was developed by Karlsruhe Institute of Technology (KIT) and Nara Institute of Science and Technology (NAIST) teams in collaboration within the interACT project. We employ single system decoding with fully continuous and semi-continuous models, as well as a three-stage, multipass system combination framework built with the Janus Recognition Toolkit. On the IWSLT 2010 test set our single system introduced in this work achieves a WER of 17.6%, and our final combination achieves a WER of 14.4%.

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Evaluation of interactive user corrections for lecture transcription
Heinrich Kolkhorst | Kevin Kilgour | Sebastian Stüker | Alex Waibel
Proceedings of the 9th International Workshop on Spoken Language Translation: Papers

In this work, we present and evaluate the usage of an interactive web interface for browsing and correcting lecture transcripts. An experiment performed with potential users without transcription experience provides us with a set of example corrections. On German lecture data, user corrections greatly improve the comprehensibility of the transcripts, yet only reduce the WER to 22%. The precision of user edits is relatively low at 77% and errors in inflection, case and compounds were rarely corrected. Nevertheless, characteristic lecture data errors, such as highly specific terms, were typically corrected, providing valuable additional information.

2011

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The 2011 KIT English ASR system for the IWSLT evaluation
Sebastian Stüker | Kevin Kilgour | Christian Saam | Alex Waibel
Proceedings of the 8th International Workshop on Spoken Language Translation: Evaluation Campaign

This paper describes our English Speech-to-Text (STT) system for the 2011 IWSLT ASR track. The system consists of 2 subsystems with different front-ends—one MVDR based, one MFCC based—which are combined using confusion network combination to provide a base for a second pass speaker adapted MVDR system. We demonstrate that this set-up produces competitive results on the IWSLT 2010 dev and test sets.

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Speech recognition for machine translation in Quaero
Lori Lamel | Sandrine Courcinous | Julien Despres | Jean-Luc Gauvain | Yvan Josse | Kevin Kilgour | Florian Kraft | Viet-Bac Le | Hermann Ney | Markus Nußbaum-Thom | Ilya Oparin | Tim Schlippe | Ralf Schlüter | Tanja Schultz | Thiago Fraga da Silva | Sebastian Stüker | Martin Sundermeyer | Bianca Vieru | Ngoc Thang Vu | Alexander Waibel | Cécile Woehrling
Proceedings of the 8th International Workshop on Spoken Language Translation: Evaluation Campaign

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.

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The 2011 KIT QUAERO speech-to-text system for Spanish
Kevin Kilgour | Christian Saam | Christian Mohr | Sebastian Stüker | Alex Waibel
Proceedings of the 8th International Workshop on Spoken Language Translation: Papers

This paper describes our current Spanish speech-to-text (STT) system with which we participated in the 2011 Quaero STT evaluation that is being developed within the Quaero program. The system consists of 4 separate subsystems, as well as the standard MFCC and MVDR phoneme based subsystems we included a both a phoneme and grapheme based bottleneck subsystem. We carefully evaluate the performance of each subsystem. After including several new techniques we were able to reduce the WER by over 30% from 20.79% to 14.53%.

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Unsupervised vocabulary selection for simultaneous lecture translation
Paul Maergner | Kevin Kilgour | Ian Lane | Alex Waibel
Proceedings of the 8th International Workshop on Spoken Language Translation: Papers

In this work, we propose a novel method for vocabulary selection which enables simultaneous speech recognition systems for lectures to automatically adapt to the diverse topics that occur in educational and scientific lectures. Utilizing materials that are available before the lecture begins, such as lecture slides, our proposed framework iteratively searches for related documents on the World Wide Web and generates a lecture-specific vocabulary and language model based on the resulting documents. In this paper, we introduce a novel method for vocabulary selection where we rank vocabulary that occurs in the collected documents based on a relevance score which is calculated using a combination of word features. Vocabulary selection is a critical component for topic adaptation that has typically been overlooked in prior works. On the interACT German-English simultaneous lecture translation system our proposed approach significantly improved vocabulary coverage, reducing the out-of-vocabulary rate on average by 57.0% and up to 84.9%, compared to a lecture-independent baseline. Furthermore, our approach reduced the word error rate by up to 25.3% (on average 13.2% across all lectures), compared to a lectureindependent baseline.