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检索条件"机构=Human Language Technology and Pattern"
385 条 记 录,以下是251-260 订阅
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Improving Continuous Sign language Recognition: Speech Recognition Techniques and System Design  4
Improving Continuous Sign Language Recognition: Speech Recog...
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4th Workshop on Speech and language Processing for Assistive Technologies, SLPAT 2013
作者: Forster, Jens Koller, Oscar Oberdorfer, Christian Gweth, Yannick Ney, Hermann Human Language Technology and Pattern Recognition Group Computer Science Department RWTH Aachen University Aachen Germany
Automatic sign language recognition (ASLR) is a special case of automatic speech recognition (ASR) and computer vision (CV) and is currently evolving from using artificial labgenerated data to using 'real-life'... 详细信息
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The RWTH Aachen Machine Translation System for WMT 2013  8
The RWTH Aachen Machine Translation System for WMT 2013
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8th Workshop on Statistical Machine Translation, WMT 2013
作者: Peitz, Stephan Mansour, Saab Peter, Jan-Thorsten Schmidt, Christoph Wuebker, Joern Huck, Matthias Freitag, Markus Ney, Hermann Human Language Technology and Pattern Recognition Group Computer Science Department RWTH Aachen University AachenD-52056 Germany
This paper describes the statistical machine translation (SMT) systems developed at RWTH Aachen University for the translation task of the ACL 2013 Eighth Workshop on Statistical Machine Translation (WMT 2013). We par... 详细信息
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Morpheme level hierarchical pitman-Yor class-based language models for LVCSR of morphologically rich languages
Morpheme level hierarchical pitman-Yor class-based language ...
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14th Annual Conference of the International Speech Communication Association, INTERSPEECH 2013
作者: El-Desoky Mousa, Amr Shaik, M. Ali Basha Schlüter, Ralf Ney, Hermann Computer Science Department Human Language Technology and Pattern Recognition RWTH Aachen University Aachen Germany Spoken Language Processing Group LIMSI CNRS Paris France
Performing large vocabulary continuous speech recognition (LVCSR) for morphologically rich languages is considered a challenging task. The morphological richness of such languages leads to high out-of-vocabulary (OOV)... 详细信息
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A Phrase Orientation Model for Hierarchical Machine Translation  8
A Phrase Orientation Model for Hierarchical Machine Translat...
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8th Workshop on Statistical Machine Translation, WMT 2013
作者: Huck, Matthias Wuebker, Joern Rietig, Felix Ney, Hermann Human Language Technology and Pattern Recognition Group Computer Science Department RWTH Aachen University AachenD-52056 Germany
We introduce a lexicalized reordering model for hierarchical phrase-based machine translation. The model scores monotone, swap, and discontinuous phrase orientations in the manner of the one presented by Tillmann (200... 详细信息
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Feature-rich sub-lexical language models using a maximum entropy approach for German LVCSR
Feature-rich sub-lexical language models using a maximum ent...
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14th Annual Conference of the International Speech Communication Association, INTERSPEECH 2013
作者: Shaik, M. Ali Basha El-Desoky Mousa, Amr Schlüter, Ralf Ney, Hermann Computer Science Department Human Language Technology and Pattern Recognition RWTH Aachen University Aachen Germany Spoken Language Processing Group LIMSI CNRS Paris France
German is a morphologically rich language having a high degree of word inflections, derivations and compounding. This leads to high out-of-vocabulary (OOV) rates and poor language model (LM) probabilities in the large... 详细信息
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Investigations on hessian-free optimization for cross-entropy training of deep neural networks
Investigations on hessian-free optimization for cross-entrop...
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14th Annual Conference of the International Speech Communication Association, INTERSPEECH 2013
作者: Wiesler, Simon Li, Jinyu Xue, Jian Computer Science Department Human Language Technology and Pattern Recognition RWTH Aachen University 52056 Aachen Germany Microsoft Corporation Redmond WA 98052 United States
Context-dependent deep neural network HMMs have been shown to achieve recognition accuracy superior to Gaussian mixture models in a number of recent works. Typically, neural networks are optimized with stochastic grad... 详细信息
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A Performance Study of Cube Pruning for Large-Scale Hierarchical Machine Translation  7
A Performance Study of Cube Pruning for Large-Scale Hierarch...
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7th Workshop on Syntax, Semantics and Structure in Statistical Translation, SSST 2013
作者: Huck, Matthias Vilar, David Freitag, Markus Ney, Hermann Human Language Technology and Pattern Recognition Group Rwth Aachen University AachenD-52056 Germany Dfki GmbH Alt-Moabit 91c BerlinD-10559 Germany
In this paper, we empirically investigate the impact of critical configuration parameters in the popular cube pruning algorithm for decoding in hierarchical statistical machine translation. Specifically, we study how ... 详细信息
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Efficient nearly error-less LVCSR decoding based on incremental forward and backward passes
Efficient nearly error-less LVCSR decoding based on incremen...
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IEEE Workshop on Automatic Speech Recognition and Understanding
作者: David Nolden Ralf Schlüter Hermann Ney Human Language Technology and Pattern Recognition Group RWTH Aachen University Aachen Germany
We show that most search errors can be identified by aligning the results of a symmetric forward and backward decoding pass. Based on this knowledge, we introduce an efficient high-level decoding architecture which yi... 详细信息
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OPEN VOCABULARY HANDWRITING RECOGNITION USING COMBINED WORD-LEVEL AND CHARACTER-LEVEL language MODELS
OPEN VOCABULARY HANDWRITING RECOGNITION USING COMBINED WORD-...
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IEEE International Conference on Acoustics, Speech, and Signal Processing
作者: Michal Kozielski David Rybach Stefan Hahn Ralf Schluter Hermann Ney Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen Germany Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen Germany
In this paper, we present a unified search strategy for open vocabulary handwriting recognition using weighted finite state transducers. Additionally to a standard word-level language model we introduce a separate n-g... 详细信息
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FEATURE COMBINATION AND STACKING OF RECURRENT AND NON-RECURRENT NEURAL NETWORKS FOR LVCSR
FEATURE COMBINATION AND STACKING OF RECURRENT AND NON-RECURR...
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IEEE International Conference on Acoustics, Speech, and Signal Processing
作者: Christian Plahl Michael Kozielski Ralf Schluter Hermann Ney Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University
This paper investigates the combination of different short-term features and the combination of recurrent and non-recurrent neural networks (NNs) on a Spanish speech recognition task. Several methods exist to combine ... 详细信息
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