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检索条件"机构=Human Language Technology and Pattern Recognition Group RWTH Aachen University Aachen"

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Framewise and CTC training of Neural Networks for handwriting recognition

Framewise and CTC training of Neural Networks for handwritin...

引用

International Conference on Document Analysis and recognition

作者： Théodore Bluche Hermann Ney Jérôme Louradour Christopher Kermorvant LIMSI CNRS Spoken Language Processing Group Orsay France A2iA SA Paris France RWTH Aachen University Human Language Technology and Pattern Recognition Aachen Germany Teklia SAS Paris France

In recent years, Long Short-Term Memory Recurrent Neural Networks (LSTM-RNNs) trained with the Connectionist Temporal Classification (CTC) objective won many international handwriting recognition evaluations. The CTC algorithm is based on a forward-backward procedure, avoiding the need of a segmentation of the input before training. The network outputs are characters labels, and a special non-character label. On the other hand, in the hybrid Neural Network / Hidden Markov Models (NN/HMM) framework, networks are trained with framewise criteria to predict state labels. In this paper, we show that CTC training is close to forward-backward training of NN/HMMs, and can be extended to more standard HMM topologies. We apply this method to Multi-Layer Perceptrons (MLPs), and investigate the properties of CTC, namely the modeling of character by single labels and the role of the special label.

关键词： Hidden Markov models Artificial neural networks Continuous wavelet transforms Training Topology Labeling Text recognition

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The LIMSI handwriting recognition system for the HTRtS 2014 contest

The LIMSI handwriting recognition system for the HTRtS 2014 ...

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International Conference on Document Analysis and recognition

作者： Théodore Bluche Hermann Ney Christopher Kermorvant LIMSI CNRS Spoken Language Processing Group Orsay France A2iA SA Paris France RWTH Aachen University Human Language Technology and Pattern Recognition Aachen Germany Teklia SAS Paris France

In this paper we present the handwriting recognition systems submitted by the LIMSI to the HTRtS 2014 contest. The systems for both the restricted and unrestricted tracks consisted of combination of several optical models. We extracted handcrafted features as well as pixels values with a sliding window. We trained Deep Neural Networks (DNNs) and Bidirectional Long Short-Term Memory Recurrent Neural Networks (BLSTM-RNNs), which where plugged as the optical model in Hidden Markov Models (HMMs). We propose a novel method to build language models that can cope with hyphenation in the text. The combination was performed from lattices generated from the different systems. We were the only team participating in both tracks and ranked second in each. The final Word Error Rates were 15.0% and 11.0% for the restricted (resp. unrestricted) track. We studied the impact of adding data for optical and language modeling. After the evaluation, we also used the same corpus for the language model as the winning team and obtained comparable results.

关键词： Hidden Markov models Lattices RNA Analytical models

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Extensions of the Sign language recognition and Translation Corpus rwth-PHOENIX-Weather 9

Extensions of the Sign Language Recognition and Translation ...

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9th International Conference on language Resources and Evaluation (LREC)

作者： Forster, Jens Schmidt, Christoph Koller, Oscar Bellgardt, Martin Ney, Hermann Human Language Technology and Pattern Recognition RWTH Aachen University Germany

ISBN: (纸本)9782951740884

This paper introduces the rwth-PHOENIX-Weather 2014, a video-based, large vocabulary, German sign language corpus which has been extended over the last two years, tripling the size of the original corpus. The corpus contains weather forecasts simultaneously interpreted into sign language which were recorded from German public TV and manually annotated using glosses on the sentence level and semi-automatically transcribed spoken German extracted from the videos using the open-source speech recognition system RASR. Spatial annotations of the signers' hands as well as shape and orientation annotations of the dominant hand have been added for more than 40k respectively 10k video frames creating one of the largest corpora allowing for quantitative evaluation of object tracking algorithms. Further, over 2k signs have been annotated using the SignWriting annotation system, focusing on the shape, orientation, movement as well as spatial contacts of both hands. Finally, extended recognition and translation setups are defined, and baseline results are presented.

关键词： Sign language Corpus Automatic recognition and Translation

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Translation model based weighting for phrase extraction 17

Translation model based weighting for phrase extraction

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17th Annual Conference of the European Association for Machine Translation, EAMT 2014

作者： Mansour, Saab Ney, Hermann Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen Germany

ISBN: (纸本)9789535537533

Domain adaptation for statistical machine translation is the task of altering general models to improve performance on the test domain. In this work, we suggest several novel weighting schemes based on translation models for adapted phrase extraction. To calculate the weights, we first phrase align the general bilingual training data, then, using domain specific translation models, the aligned data is scored and weights are defined over these scores. Experiments are performed on two translation tasks, German-to-English and Arabic-to-English translation with lectures as the target domain. Different weighting schemes based on translation models are compared, and significant improvements over automatic translation quality are reported. In addition, we compare our work to previous methods for adaptation and show significant gains. © 2014 The authors.

关键词： Extraction

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Improved decipherment of homophonic ciphers

Improved decipherment of homophonic ciphers

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2014 Conference on Empirical Methods in Natural language Processing, EMNLP 2014

作者： Nuhn, Malte Schamper, Julian Ney, Hermann Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen Germany

ISBN: (纸本)9781937284961

In this paper, we present two improvements to the beam search approach for solving homophonic substitution ciphers presented in Nuhn et al. (2013): An improved rest cost estimation together with an optimized strategy for obtaining the order in which the symbols of the cipher are deciphered reduces the beam size needed to successfully decipher the Zodiac-408 cipher from several million down to less than one hundred: The search effort is reduced from several hours of computation time to just a few seconds on a single CPU. These improvements allow us to successfully decipher the second part of the famous Beale cipher (see (Ward et al., 1885) and e.g. (King, 1993)): Having 182 different cipher symbols while having a length of just 762 symbols, the decipherment is way more challenging than the decipherment of the previously deciphered Zodiac- 408 cipher (length 408, 54 different symbols). To the best of our knowledge, this cipher has not been deciphered automatically before. © 2014 Association for Computational Linguistics.

关键词： Cryptography

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Unsupervised adaptation for statistical machine translation 9

Unsupervised adaptation for statistical machine translation

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9th Workshop on Statistical Machine Translation, WMT 2014 at the 52nd Conference of the Associationfor Computational Linguistics, ACL 2014

作者： Mansour, Saab Ney, Hermann Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen Germany

ISBN: (纸本)9781941643174

In this work, we tackle the problem of language and translation models domainadaptation without explicit bilingual indomain training data. In such a scenario, the only information about the domain can be induced from the source-language test corpus. We explore unsupervised adaptation, where the source-language test corpus is combined with the corresponding hypotheses generated by the translation system to perform adaptation. We compare unsupervised adaptation to supervised and pseudo supervised adaptation. Our results show that the choice of the adaptation (target) set is crucial for successful application of adaptation methods. Evaluation is conducted over the German-To-English WMT newswire translation task. The experiments show that the unsupervised adaptation method generates the best translation quality as well as generalizes well to unseen test sets. © Proceedings of the Annual Meeting of the Association for Computational Linguistics 2014.

关键词： Machine translation

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The rwth aachen german-english machine translation system for WMT 2014 9

The rwth aachen german-english machine translation system fo...

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9th Workshop on Statistical Machine Translation, WMT 2014 at the 52nd Conference of the Associationfor Computational Linguistics, ACL 2014

作者： Peitz, Stephan Wuebker, Joern Freitag, Markus Ney, Hermann Human Language Technology and Pattern Recognition Group Computer Science Department RWTH Aachen University AachenD-52056 Germany

ISBN: (纸本)9781941643174

This paper describes the statistical machine translation (SMT) systems developed at rwth aachen university for the German!English translation task of the ACL 2014 Eighth Workshop on Statistical Machine Translation (WMT 2014). Both hierarchical and phrase-based SMT systems are applied employing hierarchical phrase reordering and word class language models. For the phrase-based system, we run discriminative phrase training. In addition, we describe our preprocessing pipeline for German!English. © Proceedings of the Annual Meeting of the Association for Computational Linguistics 2014.

关键词： Computer aided language translation

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Deep Learning of Mouth Shapes for Sign language

Deep Learning of Mouth Shapes for Sign Language

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International Conference on Computer Vision Workshops (ICCV Workshops)

作者： Oscar Koller Hermann Ney Richard Bowden Human Language Technology & Pattern Recog. RWTH Aachen University Germany Centre for Vision Speech & Signal Processing University of Surrey UK

This paper deals with robust modelling of mouth shapes in the context of sign language recognition using deep convolutional neural networks. Sign language mouth shapes are difficult to annotate and thus hardly any publicly available annotations exist. As such, this work exploits related information sources as weak supervision. humans mainly look at the face during sign language communication, where mouth shapes play an important role and constitute natural patterns with large variability. However, most scientific research on sign language recognition still disregards the face. Hardly any works explicitly focus on mouth shapes. This paper presents our advances in the field of sign language recognition. We contribute in following areas: We present a scheme to learn a convolutional neural network in a weakly supervised fashion without explicit frame labels. We propose a way to incorporate neural network classifier outputs into a HMM approach. Finally, we achieve a significant improvement in classification performance of mouth shapes over the current state of the art.

关键词： Mouth Shape Hidden Markov models Assistive technology Gesture recognition Training Neural networks

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rwthLM - The rwth aachen university neural network language modeling toolkit 15

RWTHLM - The RWTH Aachen University neural network language ...

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15th Annual Conference of the International Speech Communication Association: Celebrating the Diversity of Spoken languages, INTERSPEECH 2014

作者： Sundermeyer, Martin Schlüter, Ralf Ney, Hermann Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen Germany Spoken Language Processing Group LIMSI CNRS Paris France

We present a novel toolkit that implements the long short-term memory (LSTM) neural network concept for language modeling. The main goal is to provide a software which is easy to use, and which allows fast training of standard recurrent and LSTM neural network language models. The toolkit obtains state-of-the-art performance on the standard Treebank corpus. To reduce the training time, BLAS and related libraries are supported, and it is possible to evaluate multiple word sequences in parallel. In addition, arbitrary word classes can be used to speed up the computation in case of large vocabulary sizes. Finally, the software allows easy integration with SRILM, and it supports direct decoding and rescoring of HTK lattices. The toolkit is available for download under an open source license. Copyright © 2014 ISCA.

关键词： Long short-term memory

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Vector space models for phrase-based machine translation 8

Vector space models for phrase-based machine translation

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8th Workshop on Syntax, Semantics and Structure in Statistical Translation, SSST 2014

作者： Alkhouli, Tamer Guta, Andreas Ney, Hermann Human Language Technology and Pattern Recognition Group RWTH Aachen University Aachen Germany Spoken Language Processing Group Univ. Paris-Sud France LIMSI/CNRS Orsay France

ISBN: (纸本)9781937284961

This paper investigates the application of vector space models (VSMs) to the standard phrase-based machine translation pipeline. VSMs are models based on continuous word representations embedded in a vector space. We exploit word vectors to augment the phrase table with new inferred phrase pairs. This helps reduce out-of-vocabulary (OOV) words. In addition, we present a simple way to learn bilingually-constrained phrase vectors. The phrase vectors are then used to provide additional scoring of phrase pairs, which fits into the standard log-linear framework of phrase-based statistical machine translation. Both methods result in significant improvements over a competitive in-domain baseline applied to the Arabic-to-English task of IWSLT 2013. © 2014 Association for Computational Linguistics

关键词： Vectors

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