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检索条件"机构=Pattern Recognition and Human Language"

共 401 条记录，以下是211-220 订阅

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Local system voting feature for machine translation system combination 10

Local system voting feature for machine translation system c...

引用

10th Workshop on Statistical Machine Translation, WMT 2015 at the 2015 Conference on Empirical Methods in Natural language Processing, EMNLP 2015

作者： Freitag, Markus Peter, Jan-Thorsten Peitz, Stephan Feng, Minwei Ney, Hermann Human Language Technology and Pattern Recognition Group Computer Science Department Rwth Aachen University AachenD-52056 Germany

ISBN: (纸本)9781941643327

In this paper, we enhance the traditional confusion network system combination approach with an additional model trained by a neural network. This work is motivated by the fact that the commonly used binary system voting models only assign each input system a global weight which is responsible for the global impact of each input system on all translations. This prevents individual systems with low system weights from having influence on the system combination output, although in some situations this could be helpful. Further, words which have only been seen by one or few systems rarely have a chance of being present in the combined output. We train a local system voting model by a neural network which is based on the words themselves and the combinatorial occurrences of the different system outputs. This gives system combination the option to prefer other systems at different word positions even for the same sentence. © EMNLP 2015. All rights reserved.

关键词： Natural language processing systems

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The rwth aachen german-english machine translation system for wmt 2015 10

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

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10th Workshop on Statistical Machine Translation, WMT 2015 at the 2015 Conference on Empirical Methods in Natural language Processing, EMNLP 2015

作者： Peter, Jan-Thorsten Toutounchi, Farzad Wuebker, Joern Ney, Hermann Human Language Technology and Pattern Recognition Group Computer Science Department Rwth Aachen University AachenD-52056 Germany

ISBN: (纸本)9781941643327

This paper describes the statistical machine translation system developed at RWTH Aachen University for the German!English translation task of the EMNLP 2015 Tenth Workshop on Statistical Machine Translation (WMT 2015). A phrase-based machine translation system was applied and augmented with hierarchical phrase reordering and word class language models. Further, we ran discriminative maximum expected BLEU training for our system. In addition, we utilized multiple feed-forward neural network language and translation models and a recurrent neural network language model for reranking. © EMNLP 2015. All rights reserved.

关键词： Feedforward neural networks

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Investigation of mixture splitting concept for training linear bottlenecks of deep neural network acoustic models 40

Investigation of mixture splitting concept for training line...

引用

40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015

作者： Tahir, Muhammad Ali Wiesler, Simon Schluter, Ralf Ney, Hermann Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Germany Spoken Language Processing Group LIMSI CNRS Paris France

ISBN: (纸本)9781467369978

A Gaussian or log-linear mixture model trained by maximum likelihood may be trained further using discriminative training. It is desirable that the mixture splitting is also done during the discriminative training, to achieve better mixture density distribution. In previous work such a discriminative splitting approach was presented. Similarly, the resolution of a deep neural network may also be increased by splitting. In this paper, discriminative splitting is applied as a way of initializing a linear bottleneck between two layers of a DNN. Experiments for a single hidden layer and six hidden layer cases show the potential of this approach as an alternative method of pre-training for linear bottlenecks for MLP hidden layers. © 2015 IEEE.

关键词： deep neural network linear bottleneck mixture splitting

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On the Benefits of Convolutional Neural Network Combinations in Offline Handwriting recognition

On the Benefits of Convolutional Neural Network Combinations...

引用

International Workshop on Frontiers in Handwriting recognition

作者： Dewi Suryani Patrick Doetsch Hermann Ney King Mongkut's University of Technology North Bangkok The Sirindhorn International Thai-German Graduate School of Engineering Bangkok Thailand Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen Germany

ISBN: (纸本)9781509009824

In this paper, we elaborate the advantages of combining two neural network methodologies, convolutional neural networks (CNN) and long short-term memory (LSTM) recurrent neural networks, with the framework of hybrid hidden Markov models (HMM) for recognizing offline handwriting text. CNNs employ shift-invariant filters to generate discriminative features within neural networks. We show that CNNs are powerful tools to extract general purpose features that even work well for unknown classes. We evaluate our system on a Chinese handwritten text database and provide a GPU-based implementation that can be used to reproduce the experiments. All experiments were conducted with RWTH OCR, an open-source system developed at our institute.

关键词： Hidden Markov models Neurons Training Handwriting recognition Biological neural networks Computer architecture Logic gates

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Improved strategies for a zero oov rate LVCSR system 40

Improved strategies for a zero oov rate LVCSR system

引用

40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015

作者： Shaik, M. Ali Basha Mousa, Amr El-Desoky Hahn, Stefan Schluter, 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

ISBN: (纸本)9781467369978

In this work, multiple hierarchical language modeling strategies for a zero OOV rate large vocabulary continuous speech recognition system are investigated. In our previously proposed hierarchical approach, a full-word language model and a context independent character-level LM (CLM) are directly used during search. The novelty of this work is to jointly model the character-level prior and the pronunciation probabilities, to introduce across-word context into the characterlevel LM, and to properly normalize the character-level LM using prefix-tree based normalization for the hierarchical approach. Significant reductions in-terms of word error rates (WER) on the best full-word Quaero Polish LVCSR system are reported. © 2015 IEEE.

关键词： hierarchical LVCSR OOV prefix-tree

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Investigations on sequence training of neural networks 40

Investigations on sequence training of neural networks

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40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015

作者： Wiesler, Simon Golik, Pavel Schluter, Ralf Ney, Hermann Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen Germany LIMSI CNRS Spoken Language Processing Group Paris France

ISBN: (纸本)9781467369978

In this paper we present an investigation of sequence-discriminative training of deep neural networks for automatic speech recognition. We evaluate different sequence-discriminative training criteria (MMI and MPE) and optimization algorithms (including SGD and Rprop) using the RASR toolkit. Further, we compare the training of the whole network with that of the output layer only. Technical details necessary for a robust training are studied, since there is no consensus yet on the ultimate training recipe. The investigation extends our previous work on training linear bottleneck networks from scratch showing the consistently positive effect of sequence training. © 2015 IEEE.

关键词： deep neural networks optimization sequence training speech recognition

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Sequence-discriminative training of recurrent neural networks 40

Sequence-discriminative training of recurrent neural network...

引用

40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015

作者： Voigtlaender, Paul Doetsch, Patrick Wiesler, Simon Schluter, Ralf Ney, Hermann Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen Germany LIMSI CNRS Spoken Language Processing Group Paris France

ISBN: (纸本)9781467369978

We investigate sequence-discriminative training of long shortterm memory recurrent neural networks using the maximum mutual information criterion. We show that although recurrent neural networks already make use of the whole observation sequence and are able to incorporate more contextual information than feed forward networks, their performance can be improved with sequence-discriminative training. Experiments are performed on two publicly available handwriting recognition tasks containing English and French handwriting. On the English corpus, we obtain a relative improvement in WER of over 11% with maximum mutual information (MMI) training compared to cross-entropy training. On the French corpus, we observed that it is necessary to interpolate the MMI objective function with cross-entropy. © 2015 IEEE.

关键词： handwriting recognition long shortterm memory recurrent neural networks sequence-discriminative training

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Integrating Gaussian mixtures into deep neural networks: Softmax layer with hidden variables 40

Integrating Gaussian mixtures into deep neural networks: Sof...

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40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015

作者： Tuske, Zoltan Tahir, Muhammad Ali Schluter, 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

ISBN: (纸本)9781467369978

In the hybrid approach, neural network output directly serves as hidden Markov model (HMM) state posterior probability estimates. In contrast to this, in the tandem approach neural network output is used as input features to improve classic Gaussian mixture model (GMM) based emission probability estimates. This paper shows that GMM can be easily integrated into the deep neural network framework. By exploiting its equivalence with the log-linear mixture model (LMM), GMM can be transformed to a large softmax layer followed by a summation pooling layer. Theoretical and experimental results indicate that the jointly trained and optimally chosen GMM and bottleneck tandem features cannot perform worse than a hybrid model. Thus, the question 'hybrid vs. tandem' simplifies to optimizing the output layer of a neural network. Speech recognition experiments are carried out on a broadcast news and conversations task using up to 12 feed-forward hidden layers with sigmoid and rectified linear unit activation functions. The evaluation of the LMM layer shows recognition gains over the classic softmax output. © 2015 IEEE.

关键词： ASR bottleneck DNN GMM hybrid LMM Log-linear mixture model neural network tandem

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ValenTo: Sentiment Analysis of Figurative language Tweets with Irony and Sarcasm 9

ValenTo: Sentiment Analysis of Figurative Language Tweets wi...

引用

9th International Workshop on Semantic Evaluation, SemEval 2015

作者： Farías, Delia Irazú Hernández Sulis, Emilio Patti, Viviana Ruffo, Giancarlo Bosco, Cristina Universitat Politècnica de València Pattern Recognition and Human Language Technology Spain University of Turin Dipartimento di Informatica Italy

ISBN: (纸本)9781941643402

This paper describes the system used by the ValenTo team in the Task 11, Sentiment Analysis of Figurative language in Twitter, at SemEval 2015. Our system used a regression model and additional external resources to assign polarity values. A distinctive feature of our approach is that we used not only word-sentiment lexicons providing polarity annotations, but also novel resources for dealing with emotions and psycholinguistic information. These are important aspects to tackle in figurative language such as irony and sarcasm, which were represented in the dataset. The system also exploited novel and standard structural features of tweets. Considering the different kinds of figurative language in the dataset our submission obtained good results in recognizing sentiment polarity in both ironic and sarcastic tweets. © 2015 Association for Computational Linguistics

关键词： Sentiment analysis

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ICFHR2016 Handwritten Keyword Spotting Competition (H-KWS 2016)

ICFHR2016 Handwritten Keyword Spotting Competition (H-KWS 20...

引用

International Workshop on Frontiers in Handwriting recognition

作者： Ioannis Pratikakis Konstantinos Zagoris Basilis Gatos Joan Puigcerver Alejandro H. Toselli Enrique Vidal Visual Computing Group Democritus University of Thrace Xanthi Greece Institute of Informatics and Telecommunications National Center for Scientific Research “Demokritos” Athens Greece Pattern Recognition and Human Language Technology Research Center Universitat Politècnica de València Valencia Spain

ISBN: (纸本)9781509009824

The H-KWS 2016, organized in the context of the ICFHR 2016 conference aims at setting up an evaluation framework for benchmarking handwritten keyword spotting (KWS) examining both the Query by Example (QbE) and the Query by String (QbS) approaches. Both KWS approaches were hosted into two different tracks, which in turn were split into two distinct challenges, namely, a segmentation-based and a segmentation-free to accommodate different perspectives adopted by researchers in the KWS field. In addition, the competition aims to evaluate the submitted training-based methods under different amounts of training data. Four participants submitted at least one solution to one of the challenges, according to the capabilities and/or restrictions of their systems. The data used in the competition consisted of historical German and English documents with their own characteristics and complexities. This paper presents the details of the competition, including the data, evaluation metrics and results of the best run of each participating methods.

关键词： Image segmentation Training data Training Measurement Histograms Handwriting recognition Visualization

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