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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2014年 8791卷 199-219页

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

Long Short-Term Memory Recurrent Neural Networks are the current state-of-the-art in handwriting recognition. In speech recognition, Deep Multi-Layer Perceptrons (DeepMLPs) have become the standard acoustic model for Hidden Markov Models (HMMs). Although handwriting and speech recognition systems tend to include similar components and techniques, DeepMLPs are not used as optical model in unconstrained large vocabulary handwriting recognition. In this paper, we compare Bidirectional LSTM-RNNs with DeepMLPs for this task. We carried out experiments on two public databases of multi-line handwritten documents: Rimes and IAM. We show that the proposed hybrid systems yield performance comparable to the state-of-the-art, regardless of the type of features (hand-crafted or pixel values) and the neural network optical model (DeepMLP or RNN). © Springer International Publishing Switzerland 2014.

关键词： Deep neural networks

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THE rwth ENGLISH LECTURE recognition SYSTEM

THE RWTH ENGLISH LECTURE RECOGNITION SYSTEM

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Simon Wiesler Kazuki Irie Zoltan Tuske Ralf Schluter Hermann Ney Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen Germany Ecole Centrale Paris Paris France

ISBN: (纸本)9781479928941

In this paper, we describe the rwth speech recognition system for English lectures developed within the Translectures project. A difficulty in the development of an English lectures recognition system, is the high ratio of non-native speakers. We address this problem by using very effective deep bottleneck features trained on multilingual data. The acoustic model is trained on large amounts of data from different domains and with different dialects. Large improvements are obtained from unsupervised acoustic adaptation. Another challenge is the frequent use of technical terms and the wide range of topics. In our recognition system, slides, which are attached to most lectures, are used for improving lexical coverage and language model adaptation.

关键词： Lectures (teaching method) recognition system modelling languages speech recognition system English English language Acoustic models

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Length-incremental Phrase Training for SMT 8

Length-incremental Phrase Training for SMT

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8th Workshop on Statistical Machine Translation, WMT 2013

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

ISBN: (纸本)9781937284572

We present an iterative technique to generate phrase tables for SMT, which is based on force-aligning the training data with a modified translation decoder. Different from previous work, we completely avoid the use of a word alignment or phrase extraction heuristics, moving towards a more principled phrase generation and probability estimation. During training, we allow the decoder to generate new phrases on-the-fly and increment the maximum phrase length in each iteration. Experiments are carried out on the IWSLT 2011 Arabic-English task, where we are able to reach moderate improvements on a state-of-the-art baseline with our training method. The resulting phrase table shows only a small overlap with the heuristically extracted one, which demonstrates the restrictiveness of limiting phrase selection by a word alignment or heuristics. By interpolating the heuristic and the trained phrase table, we can improve over the baseline by 0.5% BLEU and 0.5% TER. © 2013 Association for Computational Linguistics

关键词： Decoding

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Improving statistical machine translation with word class models

Improving statistical machine translation with word class mo...

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2013 Conference on Empirical Methods in Natural language Processing, EMNLP 2013

作者： Wuebker, Joern Peitz, Stephan Rietig, Felix Ney, Hermann Human Language Technology and Pattern Recognition Group RWTH Aachen University Aachen Germany

ISBN: (纸本)9781937284978

Automatically clustering words from a monolingual or bilingual training corpus into classes is a widely used technique in statistical natural language processing. We present a very simple and easy to implement method for using these word classes to improve translation quality. It can be applied across different machine translation paradigms and with arbitrary types of models. We show its efficacy on a small German → English and a larger French → German translation task with both standard phrase-based and hierarchical phrase-based translation systems for a common set of models. Our results show that with word class models, the baseline can be improved by up to 1.4% BLEU and 1.0% TER on the French → German task and 0.3% BLEU and 1.1% TER on the German → English task. © 2013 Association for Computational Linguistics.

关键词： Machine translation

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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

ISBN: (纸本)9781937284930

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' data. Although ASLR still struggles with feature extraction, it can benefit from techniques developed for ASR. We present a large-vocabulary ASLR system that is able to recognize sentences in continuous sign language and uses features extracted from standard single-view video cameras without using additional equipment. ASR techniques such as the multi-layer-perceptron (MLP) tandem approach, speaker adaptation, pronunciation modelling, and parallel hidden Markov models are investigated. We evaluate the influence of each system component on the recognition performance. On two publicly available large vocabulary databases representing lab-data (25 signer, 455 sign vocabulary, 19k sentence) and unconstrained 'real-life' sign language (1 signer, 266 sign vocabulary, 351 sentences) we can achieve 22.1% respectively 38.6% WER. © 2013 Association for Computational Linguistics.

关键词： Vocabulary control

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Advancements in reordering models for statistical machine translation

Advancements in reordering models for statistical machine tr...

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51st Annual Meeting of the Association for Computational Linguistics, ACL 2013

作者： Feng, Minwei Peter, Jan-Thorsten Ney, Hermann Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen Germany

ISBN: (纸本)9781937284503

In this paper, we propose a novel reordering model based on sequence labeling techniques. Our model converts the reordering problem into a sequence labeling problem, i.e. a tagging task. Results on five Chinese-English NIST tasks show that our model improves the baseline system by 1.32 Bleu and 1.53 Ter on average. Results of comparative study with other seven widely used reordering models will also be reported. © 2013 Association for Computational Linguistics.

关键词： Machine translation

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Decipherment complexity in 1:1 substitution ciphers

Decipherment complexity in 1:1 substitution ciphers

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51st Annual Meeting of the Association for Computational Linguistics, ACL 2013

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

ISBN: (纸本)9781937284503

In this paper we show that even for the case of 1:1 substitution ciphers-which encipher plaintext symbols by exchanging them with a unique substitute-finding the optimal decipherment with respect to a bigram language model is NP-hard. We show that in this case the decipherment problem is equivalent to the quadratic assignment problem (QAP). To the best of our knowledge, this connection between the QAP and the decipherment problem has not been known in the literature before. © 2013 Association for Computational Linguistics.

关键词： Combinatorial optimization

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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

ISBN: (纸本)9781937284572

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 participated in the evaluation campaign for the French-English and German-English language pairs in both translation directions. Both hierarchical and phrase-based SMT systems are applied. A number of different techniques are evaluated, including hierarchical phrase reordering, translation model interpolation, domain adaptation techniques, weighted phrase extraction, word class language model, continuous space language model and system combination. By application of these methods we achieve considerable improvements over the respective baseline systems. © 2013 Association for Computational Linguistics

关键词： Machine translation

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Beam search for solving substitution ciphers

Beam search for solving substitution ciphers

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51st Annual Meeting of the Association for Computational Linguistics, ACL 2013

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)9781937284503

In this paper we address the problem of solving substitution ciphers using a beam search approach. We present a conceptually consistent and easy to implement method that improves the current state of the art for decipherment of substitution ciphers and is able to use high order n-gram language models. We show experiments with 1:1 substitution ciphers in which the guaranteed optimal solution for 3-gram language models has 38.6% decipherment error, while our approach achieves 4.13% decipherment error in a fraction of time by using a 6-gram language model. We also apply our approach to the famous Zodiac-408 cipher and obtain slightly better (and near to optimal) results than previously published. Unlike the previous state-of-the-art approach that uses additional word lists to evaluate possible decipherments, our approach only uses a letterbased 6-gram language model. Furthermore we use our algorithm to solve large vocabulary substitution ciphers and improve the best published decipherment error rate based on the Gigaword corpus of 7.8% to 6.0% error rate. © 2013 Association for Computational Linguistics.

关键词： Errors

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Phrase training based adaptation for statistical machine translation

Phrase training based adaptation for statistical machine tra...

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2013 Conference of the North American Chapter of the Association for Computational Linguistics: human language Technologies, NAACL HLT 2013

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

ISBN: (纸本)9781937284473

We present a novel approach for translation model (TM) adaptation using phrase training. The proposed adaptation procedure is initialized with a standard general-domain TM, which is then used to perform phrase training on a smaller in-domain set. This way, we bias the probabilities of the general TM towards the in-domain distribution. Experimental results on two different lectures translation tasks show significant improvements of the adapted systems over the general ones. Additionally, we compare our results to mixture modeling, where we report gains when using the suggested phrase training adaptation method. © 2013 Association for Computational Linguistics.

关键词： Probability distributions

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