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

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Improving Continuous Sign language recognition: Speech recognition Techniques and System Design 4

Improving Continuous Sign Language Recognition: Speech Recog...

引用

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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Handwritten text recognition results on the Bentham collection with improved classical N-Gram-HMM methods 15

Handwritten text recognition results on the Bentham collecti...

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3rd International Workshop on Historical Document Imaging and Processing, HIP 2015

作者： Toselli, Alejandro H. Vidal, Enrique Pattern Recognition and Human Language Technology Research Center Universitat Politècnica de València Spain

ISBN: (纸本)9781450336024

Handwritten Text recognition experiments and results are presented on the historical Bentham text image dataset used in the ICFHR-2014 HTRtS competition. The successful segmentation-free holistic framework is adopted, using traditional modelling approaches based on Hidden Markov optical character models (HMM) and an N-gram language model (LM). Departing from the very basic N-gram-HMM baseline system provided in HTRtS, several improvements are made in text image feature extraction and LM and HMM modeling, including more accurate HMM training by means of discriminative training. As a result, we achieve similar recognition accuracy as some of the best performing (single, uncombined) systems based on (recurrent) Neural Networks (NN), using identical training and testing data. The traditional N-gram/HMM framework offers several advantages over modern approaches based on (hybrid, recurrent) NNs. Perhaps the most important is the much faster training of HMMs and the well understood stability of the results of Baum-Welch training. These advantages become very important when dealing with many historical document collections, which are typically huge and entail very high degrees of variability, making it generally diffcult to re-use models trained on previous collections. © 2015 ACM.

关键词： Recurrent neural networks

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Lightly-Supervised Training for Hierarchical Phrase-Based Machine Translation 1

Lightly-Supervised Training for Hierarchical Phrase-Based Ma...

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1st Workshop on Unsupervised Learning in NLP at the 2011 Conference on Empirical Methods in Natural language Processing, EMNLP 2011

作者： Huck, Matthias Vilar, David Stein, Daniel Ney, Hermann Human Language Technology and Pattern Recognition Group RWTH Aachen University Germany DFKI GmbH Berlin Germany

ISBN: (纸本)1937284131

In this paper we apply lightly-supervised training to a hierarchical phrase-based statistical machine translation system. We employ bitexts that have been built by automatically translating large amounts of monolingual data as additional parallel training corpora. We explore different ways of using this additional data to improve our system. Our results show that integrating a second translation model with only non-hierarchical phrases extracted from the automatically generated bitexts is a reasonable approach. The translation performance matches the result we achieve with a joint extraction on all training bitexts while the system is kept smaller due to a considerably lower overall number of phrases. © 2011 Association for Computational Linguistics

关键词： Computer aided language translation

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Advancements in Arabic-to-English hierarchical machine translation

Advancements in Arabic-to-English hierarchical machine trans...

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15th International Conference of the European Association for Machine Translation, EAMT 2011

作者： Huck, Matthias Vilar, David Stein, Daniel Ney, Hermann Human Language Technology and Pattern Recognition Group RWTH Aachen University Germany DFKI GmbH Berlin Germany

ISBN: (纸本)9789081486118

In this paper we study several advanced techniques and models for Arabic-to-English statistical machine translation. We examine how the challenges imposed by this particular language pair and translation direction can be successfully tackled within the framework of hierarchical phrase-based translation. We extend the state-of-the-art with a novel cross-system and cross-paradigm lightlysupervised training approach. In addition, for following recently developed techniques we provide a concise review, an empirical evaluation, and an in-depth analysis: soft syntactic labels, a discriminative word lexicon model, additional reorderings, and shallow rules. We thus bring together complementary methods that previously have only been investigated in isolation and mostly on different language pairs. Combinations of the methods yield significant improvements over a baseline using a usual set of models. The resulting hierarchical systems perform competitive on the large-scale NIST Arabic-to-English translation task. © 2011 European Association for Machine Translation.

关键词： Hierarchical systems

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The Vicomtech-PRHLT Speech Transcription Systems for the IberSPEECH-RTVE 2018 Speech to Text Transcription Challenge 4

The Vicomtech-PRHLT Speech Transcription Systems for the Ibe...

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4th International Conference on Advances in Speech and language Technologies for Iberian languages, IberSPEECH 2018

作者： Arzelus, Haritz Álvarez, Aitor Bernath, Conrad García, Eneritz Granell, Emilio Martínez-Hinarejos, Carlos D. Vicomtech Human Speech and Language Technology Group Spain Pattern Recognition and Human Language Technologies Research Center Universitat Politècnica de València Spain

This paper describes our joint submission to the IberSPEECH-RTVE Speech to Text Transcription Challenge 2018, which calls for automatic speech transcription systems to be evaluated in realistic TV shows. With the aim of building and evaluating systems, RTVE licensed around 569 hours of different TV programs, which were processed, re-aligned and revised in order to discard segments with imperfect transcriptions. This task reduced the corpus to 136 hours that we considered as nearly perfectly aligned audios and that we employed as in-domain data to train acoustic models. A total of 6 systems were built and presented to the evaluation challenge, three systems per condition. These recognition engines are different versions, evolution and configurations of two main architectures. The first architecture includes an hybrid LSTM-HMM acoustic model, where bidirectional LSTMs were trained to provide posterior probabilities for the HMM states. The language model corresponds to modified Kneser- Ney smoothed 3-gram and 9-gram models used for decoding and re-scoring of the lattices respectively. The second architecture includes an End-To-End based recognition system, which combines 2D convolutional neural networks as spectral feature extractor from spectrograms with bidirectional Gated Recurrent Units as RNN acoustic models. A modified Kneser-Ney smoothed 5-gram model was also integrated to re-score the E2E hypothesis. All the systems' outputs were then punctuated using bidirectional RNN models with attention mechanism and capitalized through recasing techniques. © 4th International Conference, IberSPEECH 2018.

关键词： Speech recognition

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Exploring E2E speech recognition systems for new languages 4

Exploring E2E speech recognition systems for new languages

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4th International Conference on Advances in Speech and language Technologies for Iberian languages, IberSPEECH 2018

作者： Bernath, Conrad Álvarez, Aitor Arzelus, Haritz Martínez-Hinarejos, Carlos-D. Human Speech and Language Technology Group Vicomtech Spain Pattern Recognition and Human Language Technologies Research Center Universitat Politècnica de València Spain

Over the last few years, advances in both machine learning algorithms and computer hardware have led to significant improvements in speech recognition technology, mainly through the use of Deep Learning paradigms. As it was amply demonstrated in different studies, Deep Neural Networks (DNNs) have already outperformed traditional Gaussian Mixture Models (GMMs) at acoustic modeling in combination with Hidden Markov Models (HMMs). More recently, new attempts have focused on building end-to-end (E2E) speech recognition architectures, especially in languages with many resources like English and Chinese, with the aim of overcoming the performance of LSTM-HMM and more conventional systems. The aim of this work is first to present the different techniques that have been applied to enhance state-of-the-art E2E systems for American English using publicly available datasets. Secondly, we describe the construction of E2E systems for Spanish and Basque, and explain the strategies applied to overcome the problem of the limited availability of training data, especially for Basque as a low-resource language. At the evaluation phase, the three E2E systems are also compared with LSTM-HMM based recognition engines built and tested with the same datasets. © 4th International Conference, IberSPEECH 2018.

关键词： Hidden Markov models

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Interactive-Predictive Neural Multimodal Systems 9th

Interactive-Predictive Neural Multimodal Systems

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9th Iberian Conference on pattern recognition and Image Analysis, IbPRIA 2019

作者： Peris, Álvaro Casacuberta, Francisco Pattern Recognition and Human Language Technology Research Center Universitat Politècnica de València Valencia Spain

ISBN: (纸本)9783030313319

Despite the advances achieved by neural models in sequence to sequence learning, exploited in a variety of tasks, they still make errors. In many use cases, these are corrected by a human expert in a posterior revision process. The interactive-predictive framework aims to minimize the human effort spent on this process by considering partial corrections for iteratively refining the hypothesis. In this work, we generalize the interactive-predictive approach, typically applied in to machine translation field, to tackle other multimodal problems namely, image and video captioning. We study the application of this framework to multimodal neural sequence to sequence models. We show that, following this framework, we approximately halve the effort spent for correcting the outputs generated by the automatic systems. Moreover, we deploy our systems in a publicly accessible demonstration, that allows to better understand the behavior of the interactive-predictive framework. © 2019, Springer Nature Switzerland AG.

关键词： pattern recognition

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Interactive-predictive translation based on multiple word-segments 19

Interactive-predictive translation based on multiple word-se...

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19th Annual Conference of the European Association for Machine Translation, EAMT 2016

作者： Domingo, Miguel Peris, Álvaro Casacuberta, Francisco Pattern Recognition and Human Language Technology Research Center Camino de Vera s/n Valencia46022 Spain

Current machine translation systems require human revision to produce high-quality translations. This is achieved through a post-editing process or by means of an interactive human-computer collaboration. Most protocols belonging to the last scenario follow a left-to-right strategy, where the prefix of the translation is iteratively increased by successive validations and corrections made by the user. In this work, we propose a new interactive protocol which allows the user to validate all correct word sequences in the translation generated by the system, breaking the left-to-right barrier. We evaluated our proposal through simulated experiments, obtaining large reductions of the human effort. © 2016, European Association for Machine Translation. All rights reserved.

关键词： Machine translation

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The RWTH Aachen University English-Romanian Machine Translation System for WMT 2016 1

The RWTH Aachen University English-Romanian Machine Translat...

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1st Conference on Machine Translation, WMT 2016, held at the 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016

作者： Peter, Jan-Thorsten Alkhouli, Tamer Guta, Andreas Ney, Hermann Human Language Technology and Pattern Recognition Group Computer Science Department RWTH Aachen University AachenD-52056 Germany

ISBN: (纸本)9781945626104

This paper describes the statistical machine translation system developed at RWTH Aachen University for the English→Romanian translation task of the ACL 2016 First Conference on Machine Translation (WMT 2016). We combined three different state-of the- art systems in a system combination: A phrase-based system, a hierarchical phrase-based system and an attentionbased neural machine translation system. The phrase-based and the hierarchical phrase-based systems make use of a language model trained on all available data, a language model trained on the bilingual data and a word class language model. In addition, we utilized a recurrent neural network language model and a bidirectional recurrent neural network translation model for reranking the output of both systems. The attention-based neural machine translation system was trained using all bilingual data together with the backtranslated data from the News Crawl 2015 corpora. © 2016 Association for Computational Linguistics.

关键词： Neural machine translation

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Revisiting Checkpoint Averaging for Neural Machine Translation 2

Revisiting Checkpoint Averaging for Neural Machine Translati...

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2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural language Processing, AACL-IJCNLP 2022

作者： Gao, Yingbo Herold, Christian Yang, Zijian Ney, Hermann Human Language Technology and Pattern Recognition Group Computer Science Department Rwth Aachen University AachenD-52056 Germany

ISBN: (纸本)9781959429043

Checkpoint averaging is a simple and effectivemethod to boost the performance of convergedneural machine translation models. The calculation is cheap to perform and the fact thatthe translation improvement almost comes forfree, makes it widely adopted in neural machine translation research. Despite the popularity, the method itself simply takes the mean ofthe model parameters from several checkpoints,the selection of which is mostly based on empirical recipes without many justifications. In thiswork, we revisit the concept of checkpoint averaging and consider several extensions. Specifically, we experiment with ideas such as usingdifferent checkpoint selection strategies, calculating weighted average instead of simplemean, making use of gradient information andfine-tuning the interpolation weights on development data. Our results confirm the necessityof applying checkpoint averaging for optimalperformance, but also suggest that the landscape between the converged checkpoints israther flat and not much further improvementcompared to simple averaging is to be obtained. © AACL-IJCNLP *** rights reserved

关键词： Neural machine translation

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