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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...

引用

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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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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A convergence analysis of log-linear training and its application to speech recognition

A convergence analysis of log-linear training and its applic...

引用

2011 IEEE Workshop on Automatic Speech recognition and Understanding, ASRU 2011

作者： Wiesler, S. Schluter, R. Ney, H. Human Language Technology and Pattern Recognition RWTH Aachen University of Technology 52056 Aachen Germany

ISBN: (纸本)9781467303675

Log-linear models are a promising approach for speech recognition. Typically, log-linear models are trained according to a strictly convex criterion. Optimization algorithms are guaranteed to converge to the unique global optimum of the objective function from any initialization. For large-scale applications, considerations in the limit of infinite iterations are not sufficient. We show that log-linear training can be a highly ill-conditioned optimization problem, resulting in extremely slow convergence. Conversely, the optimization problem can be preconditioned by feature transformations. Making use of our convergence analysis, we improve our log-linear speech recognition system and achieve a strong reduction of its training time. In addition, we validate our analysis on a continuous handwriting recognition task. © 2011 IEEE.

关键词： Speech recognition

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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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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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Local system voting feature for machine translation system combination 10

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

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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： 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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Diving Deep into Context-Aware Neural Machine Translation 5

Diving Deep into Context-Aware Neural Machine Translation

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5th Conference on Machine Translation, WMT 2020

作者： Huo, Jingjing Herold, Christian Gao, Yingbo Dahlmann, Leonard Khadivi, Shahram Ney, Hermann eBay Inc. Aachen Germany Human Language Technology and Pattern Recognition Group RWTH Aachen University Aachen Germany

ISBN: (纸本)9781948087810

Context-aware neural machine translation (NMT) is a promising direction to improve the translation quality by making use of the additional context, e.g., document-level translation, or having meta-information. Although there exist various architectures and analyses, the effectiveness of different context-aware NMT models is not well explored yet. This paper analyzes the performance of document-level NMT models on four diverse domains with a varied amount of parallel document-level bilingual data. We conduct a comprehensive set of experiments to investigate the impact of document-level NMT. We find that there is no single best approach to document-level NMT, but rather that different architectures come out on top on different tasks. Looking at task-specific problems, such as pronoun resolution or headline translation, we find improvements in the context-aware systems, even in cases where the corpus-level metrics like BLEU show no significant improvement. We also show that document-level back-translation significantly helps to compensate for the lack of document-level bi-texts. © 2020 Association for Computational Linguistics

关键词： Neural 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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The RWTH Aachen University English-German and German-English Unsupervised Neural Machine Translation Systems for WMT 2018 3

The RWTH Aachen University English-German and German-English...

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3rd Conference on Machine Translation, WMT 2018 at the Conference on Empirical Methods in Natural language Processing, EMNLP 2018

作者： Graça, Miguel Kim, Yunsu Schamper, Julian Geng, Jiahui Ney, Hermann Human Language Technology and Pattern Recognition Group Computer Science Department RWTH Aachen University AachenD-52056 Germany

ISBN: (纸本)9781948087810

This paper describes the unsupervised neural machine translation (NMT) systems of the RWTH Aachen University developed for the English ↔ German news translation task of the EMNLP 2018 Third Conference on Machine Translation (WMT 2018). Our work is based on iterative back-translation using a shared encoder-decoder NMT model. We extensively compare different vocabulary types, word embedding initialization schemes and optimization methods for our model. We also investigate gating and weight normalization for the word embedding layer. ©2018 Association for Computational Linguistics

关键词： Neural machine translation

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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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