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IEEE Workshop on Automatic Speech recognition and Understanding

作者： David Rybach Stefan Hahn Christian Gollan Ralf Schluter Hermann Ney Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Germany

ISBN: (纸本)9781424413690;1424413699

This paper describes the RWTH speech recognition system for Arabic. Several design aspects of the system, including cross-adaptation, multiple system design and combination, are analyzed. We summarize the semi-automatic lexicon generation for Arabic using a statistical approach to grapheme-to-phoneme conversion and pronunciation statistics. Furthermore, a novel ASR-based audio segmentation algorithm is presented. Finally, we discuss practical approaches for parallelized acoustic training and memory efficient lattice rescoring. Systematic results are reported on recent GALE evaluation corpora.

关键词： Broadcasting Mel frequency cepstral coefficient Hidden Markov models Neural networks Lattices Cepstral analysis Speech recognition humans Natural languages Loudspeakers

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Development of the 2007 RWTH Mandarin LVCSR system

Development of the 2007 RWTH Mandarin LVCSR system

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IEEE Workshop on Automatic Speech recognition and Understanding

作者： Bjorn Hoffmeister Christian Plahl Peter Fritz Georg Heigold Jonas Loof Ralf Schluter Hermann Ney Human Language and Pattern Recognition Computer Science Department 6 RWTH Aachen University Germany

ISBN: (纸本)9781424413690;1424413699

This paper describes the development of the RWTH Mandarin LVCSR system. Different acoustic front-ends together with multiple system cross-adaptation are used in a two stage decoding framework. We describe the system in detail and present systematic recognition results. Especially, we compare a variety of approaches for cross-adapting to multiple systems. During the development we did a comparative study on different methods for integrating tone and phoneme posterior features. Furthermore, we apply lattice based consensus decoding and system combination methods. In these methods, the effect of minimizing character instead of word errors is compared. The final system obtains a character error rate of 17.7% on the GALE 2006 evaluation data.

关键词： Decoding Error analysis Dictionaries Neural networks Natural languages Lattices Vocabulary humans pattern recognition computer science

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The RWTH Arabic-to-English spoken language translation system

The RWTH Arabic-to-English spoken language translation syste...

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IEEE Workshop on Automatic Speech recognition and Understanding

作者： Oliver Bender Evgeny Matusov Stefan Hahn Sasa Hasan Shahram Khadivi Hermann Ney Human Language Technology and Pattern Recognition Lehrstuhl für Informatik 6-Computer Science Department RWTH Aachen University Aachen Germany

ISBN: (纸本)9781424413690;1424413699

We present the RWTH phrase-based statistical machine translation system designed for the translation of Arabic speech into English text. This system was used in the Global Autonomous language Exploitation (GALE) Go/No-Go Translation Evaluation 2007. Using a two-pass approach, we first generate n-best translation candidates and then rerank these candidates using additional models. We give a short review of the decoder as well as of the models used in both passes. We stress the difficulties of spoken language translation, i.e. how to combine the recognition and translation systems and how to compensate for missing punctuation. In addition, we cover our work on domain adaptation for the applied language models. We present translation results for the official GALE 2006 evaluation set and the GALE 2007 development set.

关键词： Natural languages Automatic speech recognition Surface-mount technology Vocabulary Decoding Speech analysis Broadcasting humans pattern recognition computer science

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Editorial: Knowledge engineering, semantics, and signal processing in audio - visual information retrieval

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IEEE Transactions on Circuits and Systems for Video technology 2007年第3期17卷 257-258页

作者： Izquierdo, Ebroul Zhang, Jian Sikora, Thomas Huang, Thomas S. Department of Electrical Engineering Queen Mary University of London London El 4NS United Kingdom Sydney Australia School of Computer Science and Engineering University of New South Wales Australia Communication Systems Group Technical University Berlin Berlin Germany ITG University of Illinois at Urbana-Champaign United States Department of Electrical and Computer Engineering Coordinated Science Laboratory United States Image Formation and Processing Group Beckman Institute for Advanced Science and Technology United States Institute's Major Research Theme Human Computer Intelligent Interaction National Academy of Engineering Chinese Academies of Engineering and Sciences China International Association of Pattern Recognition Optical Society of American United States

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关键词： Special issues and sections Knowledge engineering Information retrieval Audio-visual systems

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The RWTH Statistical Machine Translation System for the IWSLT 2006 Evaluation 3

The RWTH Statistical Machine Translation System for the IWSL...

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3rd International Workshop on Spoken language Translation, IWSLT 2006

作者： Mauser, Arne Zens, Richard Matusov, Evgeny Hasan, Saša Ney, Hermann Human Language Technology and Pattern Recognition Lehrstuhl für Informatik 6 Computer Science Department RWTH Aachen University AachenD-52056 Germany

We give an overview of the RWTH phrase-based statistical machine translation system that was used in the evaluation campaign of the International Workshop on Spoken language Translation (IWSLT) 2006. The system was ranked first with respect to the BLEU measure in all language pairs it was used Using a two-pass aproach, we first generate the N best translation candidates. The second pass consists of rescoring and reranking these candidates. We will give a description of the search algorithm as well as of the models used in each pass. We will also describe our method for dealing with punctuation restoration, in order to overcome the difficulties of spoken language translation. This work also includes a brief description of the system combination done by the partners participating in the European TC-Star project. © 2006 International Workshop on Spoken language Translation, IWSLT 2006. All rights reserved.

关键词： computer aided language translation

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N-Gram posterior probabilities for statistical machine translation

N-Gram posterior probabilities for statistical machine trans...

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2006 Workshop on Statistical Machine Translation, WMT 2006, collocated with the HLT-NAACL 2006

作者： Zens, Richard Ney, Hermann Human Language Technology and Pattern Recognition Lehrstuhl für Informatik 6 - Computer Science Department RWTH Aachen University AachenD-52056 Germany

Word posterior probabilities are a common approach for confidence estimation in automatic speech recognition and machine translation. We will generalize this idea and introduce n-gram posterior probabilities and show how these can be used to improve translation quality. Additionally, we will introduce a sentence length model based on posterior probabilities. We will show significant improvements on the Chinese-English NIST task. The absolute improvements of the BLEU score is between 1.1% and 1.6%. © HLT-NAACL *** right reserved.

关键词： Machine translation

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Discriminative reordering models for statistical machine translation

Discriminative reordering models for statistical machine tra...

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2006 Workshop on Statistical Machine Translation, WMT 2006, collocated with the HLT-NAACL 2006

作者： Zens, Richard Ney, Hermann Human Language Technology and Pattern Recognition Lehrstuhl für Informatik 6 Computer Science Department RWTH Aachen University AachenD-52056 Germany

We present discriminative reordering models for phrase-based statistical machine translation. The models are trained using the maximum entropy principle. We use several types of features: based on words, based on word classes, based on the local context. We evaluate the overall performance of the reordering models as well as the contribution of the individual feature types on a word-aligned corpus. Additionally, we show improved translation performance using these reordering models compared to a state-of-the-art baseline system. © HLT-NAACL *** right reserved.

关键词： computer aided language translation

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Acoustic feature combination for robust speech recognition

Acoustic feature combination for robust speech recognition

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2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '05

作者： Zolnay, András Schlüter, Ralf Ney, Hermann Computer Science Department Human Language Technology and Pattern Recognition RWTH Aachen University 52056 Aachen Germany

ISBN: (纸本)0780388747

In this paper, we consider the use of multiple acoustic features of the speech signal for robust speech recognition. We investigate the combination of various auditory based (Mel Frequency Cepstrum Coefficients, Perceptual Linear Prediction, etc.) and articulatory based (voicedness) features. Features are combined by a Linear Discriminant Analysis based and by a log-linear model combination based techniques. We describe the two feature combination techniques and compare the experimental results. Experiments performed on the large-vocabulary task VerbMobil II (German conversational speech) show that the accuracy of automatic speech recognition systems can be improved by the combination of different acoustic features. © 2005 IEEE.

关键词： Feature extraction

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The RWTH Phrase-based Statistical Machine Translation System 2

The RWTH Phrase-based Statistical Machine Translation System

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2nd International Workshop on Spoken language Translation, IWSLT 2005

作者： Zens, Richard Bender, Oliver Hasan, Saša Khadivi, Shahram Matusov, Evgeny Xu, Jia Zhang, Yuqi Ney, Hermann Human Language Technology and Pattern Recognition Lehrstuhl für Informatik VI Computer Science Department RWTH Aachen University AachenD-52056 Germany

We give an overview of the RWTH phrase-based statistical machine translation system that was used in the evaluation campaign of the International Workshop on Spoken language Translation 2005. We use a two pass approach. In the first pass, we generate a list of the N best translation candidates. The second pass consists of rescoring and reranking this N-best list. We will give a description of the search algorithm as well as the models that are used in each pass. We participated in the supplied data tracks for manual transcriptions for the following translation directions: Arabic-English, Chinese-English, English-Chinese and Japanese-English. For Japanese-English, we also participated in the C-Star track. In addition, we performed translations of automatic speech recognition output for Chinese-English and Japanese-English. For both language pairs, we translated the single-best ASR hypotheses. Additionally, we translated Chinese ASR lattices. © 2005 International Workshop on Spoken language Translation, IWSLT 2005.

关键词： Speech recognition

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Articulatory motivated acoustic features for speech recognition

Articulatory motivated acoustic features for speech recognit...

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9th European Conference on Speech Communication and technology

作者： Kocharov, Daniil Zolnay, András Schlüter, Ralf Ney, Hermann Department of Phonetics Faculty of Philology Saint-Petersburg State University 199034 Saint Petersburg Russia Human Language Technology and Pattern Recognition Lehrstuhl für Informatik VI Computer Science Department RWTH Aachen University 52056 Aachen Germany

In this paper, we consider the use of multiple acoustic features of the speech signal for continuous speech recognition. A novel articulatory motivated acoustic feature is introduced, namely the spectrum derivative feature. The new feature is tested in combination with the standard Mel Frequency Cepstral Coefficients (MFCC) and the voicedness features. Linear Discriminant Analysis is applied to find the optimal combination of different acoustic features. Experiments have been performed on small and large vocabulary tasks. Significant improvements in word error rate have been obtained by combining the MFCC feature with the articulatory motivated voicedness and spectrum derivative features: improvements of up to 25% on the small-vocabulary task and improvements of up to 4% on the large-vocabulary task relative to using MFCC alone with the same overall number of parameters in the system.

关键词： Speech recognition

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