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International Conference on Document Analysis and recognition

作者： Philippe Dreuw David Rybach Christian Gollan Hermann Ney Human Language Technology and Pattern Recognition RWTH Aachen University Aachen Germany

We present a writer adaptive training and writer clustering approach for an HMM based Arabic handwriting recognition system to handle different handwriting styles and their variations. Additionally, a writing variant model refinement for specific writing variants is proposed. Current approaches try to compensate the impact of different writing styles during preprocessing and normalization steps. Writer adaptive training with a CMLLR based feature adaptation is used to train writer dependent models. An unsupervised writer clustering with Bayesian information criterion based stopping condition for a CMLLR based feature adaptation during a two-pass decoding process is used to cluster different handwriting styles of unknown test writers. The proposed methods are evaluated on the IFN/ENIT Arabic handwriting database.

关键词： Writing Handwriting recognition Maximum likelihood decoding Hidden Markov models Automatic speech recognition Maximum likelihood linear regression pattern recognition Bayesian methods Databases Text analysis

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Audio segmentation for speech recognition using segment features

Audio segmentation for speech recognition using segment feat...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

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

Audio segmentation is an essential preprocessing step in several audio processing applications with a significant impact e.g. on speech recognition performance. We introduce a novel framework which combines the advantages of different well known segmentation methods. An automatically estimated log-linear segment model is used to determine the segmentation of an audio stream in a holistic way by a maximum a posteriori decoding strategy, instead of classifying change points locally. A comparison to other segmentation techniques in terms of speech recognition performance is presented, showing a promising segmentation quality of our approach.

关键词： Speech recognition Streaming media Decoding Broadcasting Loudspeakers Automatic speech recognition Bayesian methods humans Natural languages pattern recognition

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Demonstration of Joshua: An open source toolkit for parsing-based machine translation

Demonstration of Joshua: An open source toolkit for parsing-...

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Joint Conference of the 47th Annual Meeting of the Association for Computational Linguistics and 4th International Joint Conference on Natural language Processing of the AFNLP, ACL-IJCNLP 2009

作者： Li, Zhifei Callison-Burch, Chris Dyer, Chris Ganitkevitch, Juri Khudanpur, Sanjeev Schwartz, Lane Thornton, Wren N.G. Weese, Jonathan Zaidan, Omar F. Center for Language and Speech Processing Johns Hopkins University United States Computational Linguistics and Information Processing Lab. University of Maryland United States Human Language Technology and Pattern Recognition Group RWTH Aachen University Germany Natural Language Processing Lab. University of Minnesota United States

ISBN: (纸本)9781617382581

We describe Joshua (Li et al., 2009a)1, an open source toolkit for statistical machine translation. Joshua implements all of the algorithms required for translation via synchronous context free grammars (SCFGs): chart-parsing, n-gram language model integration, beam- and cubepruning, and k-best extraction. The toolkit also implements suffix-array grammar extraction and minimum error rate training. It uses parallel and distributed computing techniques for scalability. We also provide a demonstration outline for illustrating the toolkit's features to potential users, whether they be newcomers to the field or power users interested in extending the toolkit. © 2009 ACL and AFNLP.

关键词： Distributed computer systems

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Joshua: An Open Source Toolkit for Parsing-based Machine Translation 4

Joshua: An Open Source Toolkit for Parsing-based Machine Tra...

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4th Workshop on Statistical Machine Translation, WMT 2009, immediately preceding the 12th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2009

作者： Li, Zhifei Callison-Burch, Chris Dyer, Chris Ganitkevitch, Juri Khudanpur, Sanjeev Schwartz, Lane Thornton, Wren N. G. Weese, Jonathan Zaidan, Omar F. Center For Language And Speech Processing Johns Hopkins University BaltimoreMD United States Computational Linguistics And Information Processing Lab University of Maryland College ParkMD United States Human Language Technology And Pattern Recognition Group RWTH Aachen University Germany Natural Language Processing Lab University of Minnesota MinneapolisMN United States

We describe Joshua, an open source toolkit for statistical machine translation. Joshua implements all of the algorithms required for synchronous context free grammars (SCFGs): chart-parsing, ngram language model integration, beamand cube-pruning, and k-best extraction. The toolkit also implements suffix-array grammar extraction and minimum error rate training. It uses parallel and distributed computing techniques for scalability. We demonstrate that the toolkit achieves state of the art translation performance on the WMT09 French-English translation task. ©2009 Association for Computational Linguistics.

关键词： Distributed computer systems

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White-space models for offline Arabic handwriting recognition

White-space models for offline Arabic handwriting recognitio...

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作者： Dreuw, Philippe Jonas, Stephan Ney, Hermann Human Language Technology and Pattern Recognition RWTH Aachen University Germany

ISBN: (纸本)9781424421756

We propose to explicitly model white-spaces for Arabic handwriting recognition within different writing variants. Position-dependent character shapes in Arabic handwriting allow for large white-spaces between characters even within words. Here, a separate character model for white-spaces in combination with a lexicon using different writing variants and character model length adaptation is proposed. Current handwriting recognition systems model the white-spaces implicitly within the character models leading to possibly degraded models, or try to explicitly segment the Arabic words into pieces of Arabic words being prone to segmentation errors. Several white-space modeling approaches are analyzed on the well known IFN/ENIT database and outperform the best reported error rates. © 2008 IEEE.

关键词： Character recognition

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Statistical pattern recognition and machine translation from 1988 to 1998: What has happened?

Statistical pattern recognition and machine translation from...

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8th International Workshop on pattern recognition in Information Systems, PRIS 2008;In Conjunction with ICEIS 2008

作者： Ney, Hermann Human Language Technology and Pattern Recognition RWTH Aachen University Aachen Germany

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Spoken language processing techniques for sign language recognition and translation

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technology and Disability 2008年第2期20卷 121-133页

作者： Dreuw, Philippe Stein, Daniel Deselaers, Thomas Rybach, David Zahedi, Morteza Bungeroth, Jan Ney, Hermann Human Language Technology and Pattern Recognition Computer Science Department 6 RWTH Aachen University Aachen Germany

We present an approach to automatically recognize sign language and translate it into a spoken language. A system to address these tasks is created based on state-of-the-art techniques from statistical machine translation, speech recognition, and image processing research. Such a system is necessary for communication between deaf and hearing people. The communication is otherwise nearly impossible due to missing sign language skills on the hearing side, and the low reading and writing skills on the deaf side. As opposed to most current approaches, which focus on the recognition of isolated signs only, we present a system that recognizes complete sentences in sign language. Similar to speech recognition, we have to deal with temporal sequences. Instead of the acoustic signal in speech recognition, we process a video signal as input. Therefore, we use a speech recognition system to obtain a textual representation of the signed sentences. This intermediate representation is then fed into a statistical machine translation system to create a translation into a spoken language. To achieve good results, some particularities of sign languages are considered in both systems. We use a publicly available corpus to show the performance of the proposed system and report very promising results. © 2008 IOS Press. All rights reserved.

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Visual modeling and feature adaptation in sign language recognition

Visual modeling and feature adaptation in sign language reco...

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8. ITG-Fachtagung Sprachkommunikation 2008 - 8th ITG Conference on Speech Communication 2008

作者： Dreuw, Philippe Ney, Hermann Human Language Technology and Pattern Recognition RWTH Aachen University AachenD-52056 Germany

ISBN: (纸本)9783800731206

We propose a tracking adaptation to recover from early tracking errors in sign language recognition by optimizing the obtained tracking paths w.r.t. the hypothesized word sequences of an automatic sign language recognition system. Hand or head tracking is usually only optimized according to a tracking criterion. As a consequence, methods which depend on accurate detection and tracking of body parts lead to recognition errors in gesture and sign language processing. Similar to speaker dependent feature adaptation methods in automatic speech recognition, we propose an automatic visual alignment of signers for vision-based sign language recognition. Furthermore, the generation of additional virtual training samples is proposed to reduce the lack of data problem in sign language processing, which often leads to "one-shot" trained models. Most state-of-the-art systems are speaker dependent, and consider tracking as a preprocessing feature extraction part. Experiments on a publicly available benchmark database show that the proposed methods strongly improve the recognition accuracy of the system. © Sprachkommunikation 2008 - 8. ITG-Fachtagung.

关键词： Modeling languages

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The RWTH Machine Translation System for IWSLT 2008 5

The RWTH Machine Translation System for IWSLT 2008

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5th International Workshop on Spoken language Translation, IWSLT 2008

作者： Vilar, David Stein, Daniel Zhang, Yuqi Matusov, Evgeny Mauser, Arne Bender, Oliver Mansour, Saab Ney, Hermann Human Language Technology and Pattern Recognition Lehrstuhl für Informatik 6 Computer Science Department RWTH Aachen University AachenD-52056 Germany

RWTH's system for the 2008 IWSLT evaluation consists of a combination of different phrase-based and hierarchical statistical machine translation systems. We participated in the translation tasks for the Chinese-to-English and Arabic-to-English language pairs. We investigated different preprocessing techniques, reordering methods for the phrase-based system, including reordering of speech lattices, and syntax-based enhancements for the hierarchical systems. We also tried the combination of the Arabic-to-English and Chinese-to-English outputs as an additional submission. © 2008 International Workshop on Spoken language Translation, IWSLT 2008. All rights reserved.

关键词： Hierarchical systems

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Bag-of-visual-words models for adult image classification and filtering

Bag-of-visual-words models for adult image classification an...

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作者： Deselaers, Thomas Pimenidis, Lexi Ney, Hermann Computer Science Department Human Language Technology and Pattern Recognition RWTH Aachen University Aachen Germany Security and Privacy Research RWTH Aachen University Aachen Germany

ISBN: (纸本)9781424421756

We present a method to classify images into different categories of pornographic content to create a system for filtering pornographic images from network traffic. Although different systems for this application were presented in the past, most of these systems are based on simple skin colour features and have rather poor performance. Recent advances in the image recognition field in particular for the classification of objects have shown that bag-of-visual-words-approaches are a good method for many image classification problems. The system we present here, is based on this approach, uses a task-specific visual vocabulary and is trained and evaluated on an image database of 8500 images from different categories. It is shown that it clearly outperforms earlier systems on this dataset and further evaluation on two novel web-traffic collections shows the good performance of the proposed system. © 2008 IEEE.

关键词： Image classification

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