Quaero Speech-to-Text and Text Translation Evaluation Systems

Our laboratory has used the HP XC4000, the high performance computer of the federal state Baden-Wnrttemberg, in order to participate in the second Quaero evaluation for automatic speech recognition (ASR) and Machine Translation (MT). State-of-the-art automatic speech recognition and machine translation systems train use stochastic models which are trained on large amounts of training data using techniques from the field of machine learning. Using these techniques the systems search for the most likely speech recognition hypothesis, translation hypothesis respectively.

[1]  Philip C. Woodland,et al.  Maximum likelihood linear regression for speaker adaptation of continuous density hidden Markov models , 1995, Comput. Speech Lang..

[2]  Tanja Schultz,et al.  Speaker segmentation and clustering in meetings , 2004, INTERSPEECH.

[3]  Daniel Marcu,et al.  Statistical Phrase-Based Translation , 2003, NAACL.

[4]  Wen Wang,et al.  Techniques for effective vocabulary selection , 2003, INTERSPEECH.

[5]  Andreas Stolcke,et al.  SRILM - an extensible language modeling toolkit , 2002, INTERSPEECH.

[6]  Jan Niehues,et al.  The Universität Karlsruhe Translation System for the EACL-WMT 2009 , 2009, WMT@EACL.

[7]  Jan Niehues,et al.  A POS-Based Model for Long-Range Reorderings in SMT , 2009, WMT@EACL.

[8]  Mauro Cettolo,et al.  Language modeling and transcription of the TED corpus lectures , 2003, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)..

[9]  A. Waibel,et al.  A one-pass decoder based on polymorphic linguistic context assignment , 2001, IEEE Workshop on Automatic Speech Recognition and Understanding, 2001. ASRU '01..

[10]  Roland Kuhn,et al.  Phrasetable Smoothing for Statistical Machine Translation , 2006, EMNLP.

[11]  Hermann Ney,et al.  Cross domain automatic transcription on the TC-STAR EPPS corpus , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

[12]  Ashish Venugopal Training and Evaluating Error Minimization Rules for Statistical Machine Translation , 2005 .

[13]  William M. Fisher A statistical text-to-phone function using ngrams and rules , 1999, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258).

[14]  Maria Leonor Pacheco,et al.  of the Association for Computational Linguistics: , 2001 .

[15]  Franz Josef Och,et al.  Minimum Error Rate Training in Statistical Machine Translation , 2003, ACL.

[16]  M. Wolfel,et al.  Minimum variance distortionless response spectral estimation , 2005, IEEE Signal Processing Magazine.

[17]  Stephan Vogel,et al.  Combination of Machine Translation Systems via Hypothesis Selection from Combined N-Best Lists , 2008, AMTA 2008.

[18]  Puming Zhan,et al.  Speaker normalization based on frequency warping , 1997, 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[19]  Sebastian Stüker,et al.  The ISL 2007 English speech transcription system for european parliament speeches , 2007, INTERSPEECH.

[20]  Jan Niehues,et al.  Discriminative Word Alignment via Alignment Matrix Modeling , 2008, WMT@ACL.

[21]  Mark J. F. Gales,et al.  Maximum likelihood linear transformations for HMM-based speech recognition , 1998, Comput. Speech Lang..

[22]  Stephan Vogel,et al.  Parallel Implementations of Word Alignment Tool , 2008, SETQALNLP.

[23]  Mark J. F. Gales,et al.  Semi-tied covariance matrices for hidden Markov models , 1999, IEEE Trans. Speech Audio Process..

[24]  Daniel Povey,et al.  Improved discriminative training techniques for large vocabulary continuous speech recognition , 2001, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221).