Markus Nußbaum-Thom


The RWTH Aachen German and English LVCSR systems for IWSLT-2013
M. Ali Basha Shaik | Zoltan Tüske | Simon Wiesler | Markus Nußbaum-Thom | Stephan Peitz | Ralf Schlüter | Hermann Ney
Proceedings of the 10th International Workshop on Spoken Language Translation: Evaluation Campaign

In this paper, German and English large vocabulary continuous speech recognition (LVCSR) systems developed by the RWTH Aachen University for the IWSLT-2013 evaluation campaign are presented. Good improvements are obtained with state-of-the-art monolingual and multilingual bottleneck features. In addition, an open vocabulary approach using morphemic sub-lexical units is investigated along with the language model adaptation for the German LVCSR. For both the languages, competitive WERs are achieved using system combination.


The RWTH Aachen speech recognition and machine translation system for IWSLT 2012
Stephan Peitz | Saab Mansour | Markus Freitag | Minwei Feng | Matthias Huck | Joern Wuebker | Malte Nuhn | Markus Nußbaum-Thom | Hermann Ney
Proceedings of the 9th International Workshop on Spoken Language Translation: Evaluation Campaign

In this paper, the automatic speech recognition (ASR) and statistical machine translation (SMT) systems of RWTH Aachen University developed for the evaluation campaign of the International Workshop on Spoken Language Translation (IWSLT) 2012 are presented. We participated in the ASR (English), MT (English-French, Arabic-English, Chinese-English, German-English) and SLT (English-French) tracks. For the MT track both hierarchical and phrase-based SMT decoders are applied. A number of different techniques are evaluated in the MT and SLT tracks, including domain adaptation via data selection, translation model interpolation, phrase training for hierarchical and phrase-based systems, additional reordering model, word class language model, various Arabic and Chinese segmentation methods, postprocessing of speech recognition output with an SMT system, and system combination. By application of these methods we can show considerable improvements over the respective baseline systems.

Spoken language translation using automatically transcribed text in training
Stephan Peitz | Simon Wiesler | Markus Nußbaum-Thom | Hermann Ney
Proceedings of the 9th International Workshop on Spoken Language Translation: Papers

In spoken language translation a machine translation system takes speech as input and translates it into another language. A standard machine translation system is trained on written language data and expects written language as input. In this paper we propose an approach to close the gap between the output of automatic speech recognition and the input of machine translation by training the translation system on automatically transcribed speech. In our experiments we show improvements of up to 0.9 BLEU points on the IWSLT 2012 English-to-French speech translation task.


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 ( 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.