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Hierarchical Hybrid language models for Open Vocabulary Continuous Speech recognition using WFST

Hierarchical Hybrid Language models for Open Vocabulary Cont...

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2012 SAPA-SCALE Conference

作者： Shaik, M. Ali Basha Rybach, David Hahn, Stefan Schlüter, Ralf Ney, Hermann Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen52056 Germany

One of the main challenges in automatic speech recognition is recognizing an open, partly unseen vocabulary. To implicitly reduce the out-of-vocabulary (OOV) rate, hybrid vocabularies consisting of full-words and sub-words are used. Nevertheless, when using sub-words, OOV rates are not necessarily zero. In this work, we propose the use of separate character level graphones (orthography and phoneme sequence pair) as sub-words to effectively obtain zero OOV rate. To minimize negative effects on the core vocabulary of the most frequent words, a hierarchical language modeling approach is proposed. We augment the first level hybrid language model with an OOV word class, which is replaced by character level graphone sequences using a second-level graphone based character language and acoustic model during search. This approach is realized on-the-fly using weighted finite state transducers. We recognize a significant fraction of OOVs on the Wall Street Journal corpus, compared to the full-word and former hybrid language model based approaches. © SAPA-SCALE 2012. All rights reserved.

关键词： Modeling languages

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The RWTH Aachen Speech recognition and Machine Translation System for IWSLT 2012 9

The RWTH Aachen Speech Recognition and Machine Translation S...

引用

9th International Workshop on Spoken language Translation, IWSLT 2012

作者： Peitz, Stephan Mansour, Saab Freitag, Markus Feng, Minwei Huck, Matthias Wuebker, Joern Nuhn, Malte Nußbaum-Thom, Markus Ney, Hermann Human Language Technology and Pattern Recognition Group Computer Science Department RWTH Aachen University Aachen Germany

In this paper, the automatic speech recognition (ASR) and statistical machine translation (SMT) systems of RWTH Aachen University developed for the evaluation campaign of the International Workshop on Spoken language Translation (IWSLT) 2012 are presented. We participated in the ASR (English), MT (English-French, Arabic-English, Chinese-English, German-English) and SLT (English-French) tracks. For the MT track both hierarchical and phrase-based SMT decoders are applied. A number of different techniques are evaluated in the MT and SLT tracks, including domain adaptation via data selection, translation model interpolation, phrase training for hierarchical and phrase-based systems, additional reordering model, word class language model, various Arabic and Chinese segmentation methods, postprocessing of speech recognition output with an SMT system, and system combination. By application of these methods we can show considerable improvements over the respective baseline systems. © IWSLT 2012. All rights reserved.

关键词： Speech recognition

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Non-Stationary Signal Processing and its Application in Speech recognition

Non-Stationary Signal Processing and its Application in Spee...

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2012 SAPA-SCALE Conference

作者： Tüske, Zoltán Drepper, Friedhelm R. Schlüter, Ralf Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen52056 Germany

The most widely used acoustic feature extraction methods of current automatic speech recognition (ASR) systems are based on the assumption of stationarity. In this paper we extensively evaluate a recently introduced filter stable, non-stationary signal processing method, which relies on an adaptive part-tone decomposition of voiced speech to obtain alternative feature vectors for ASR. The non-stationary filterbank allows for more noise robust amplitude based features by suppressing the between-harmonics regions. Furthermore, by adapting the center filter frequencies to the underlying acoustic modes, it is possible to obtain useful phase features which can be interpreted in terms of the non-stationary dynamics within the vocal tract. The features are evaluated on different tasks ranging from vowel classification up to large vocabulary continuous speech recognition. © SAPA-SCALE 2012. All rights reserved.

关键词： Adaptive filters

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Sequence Labeling-based Reordering Model for Phrase-based SMT 9

Sequence Labeling-based Reordering Model for Phrase-based SM...

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9th International Workshop on Spoken language Translation, IWSLT 2012

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

For current statistical machine translation system, reordering is still a major problem for language pairs like Chinese-English, where the source and target language have significant word order differences. 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. For the given source sentence, we assign each source token a label which contains the reordering information for that token. We also design an unaligned word tag so that the unaligned word phenomenon is automatically implanted in the proposed model. Our reordering model is conditioned on the whole source sentence. Hence it is able to catch the long dependency in the source sentence. Although the learning on large scale task requests notably amounts of computational resources, the decoder makes use of the tagging information as soft constraints. Therefore, the training procedure of our model is computationally expensive for large task while in the test phase (during translation) our model is very efficient. We carried out experiments on five Chinese-English NIST tasks trained with BOLT data. Results show that our model improves the baseline system by 1.32 BLEU 1.53 TER on average. © IWSLT 2012. All rights reserved.

关键词： computer aided language translation

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The RWTH Aachen Machine Translation System for WMT 2012 7

The RWTH Aachen Machine Translation System for WMT 2012

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7th Workshop on Statistical Machine Translation, WMT 2012, immediately following the Conference of the North-American Chapter of the Association for Computational Linguistics - human language Technologies, NAACL HLT 2012

作者： Huck, Matthias Peitz, Stephan Freitag, Markus Nuhn, Malte Ney, Hermann Computer Science Department Human Language Technology And Pattern Recognition Group Rwth Aachen University AachenD-52056 Germany

ISBN: (纸本)9781937284206

This paper describes the statistical machine translation (SMT) systems developed at RWTH Aachen University for the translation task of the NAACL 2012 Seventh Workshop on Statistical Machine Translation (WMT 2012). 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 an insertion model, different lexical smoothing methods, a discriminative reordering extension for the hierarchical system, reverse translation, and system combination. By application of these methods we achieve considerable improvements over the respective baseline systems. ©2012 Association for Computational Linguistics.

关键词： Hierarchical systems

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Insertion and deletion models for statistical machine translation

Insertion and deletion models for statistical machine transl...

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2012 Conference of the North American Chapter of the Association for Computational Linguistics: human language Technologies, NAACL HLT 2012

作者： Huck, Matthias Ney, Hermann Human Language Technology and Pattern Recognition Group Computer Science Department RWTH Aachen University AachenD-52056 Germany

ISBN: (纸本)1937284204

We investigate insertion and deletion models for hierarchical phrase-based statistical machine translation. Insertion and deletion models are designed as a means to avoid the omission of content words in the hypotheses. In our case, they are implemented as phrase-level feature functions which count the number of inserted or deleted words. An English word is considered inserted or deleted based on lexical probabilities with the words on the foreign language side of the phrase. Related techniques have been employed before by Och et al. (2003) in an n-best reranking framework and by Mauser et al. (2006) and Zens (2008) in a standard phrase-based translation system. We propose novel thresholding methods in this work and study insertion and deletion features which are based on two different types of lexicon models. We give an extensive experimental evaluation of all these variants on the NIST Chinese → English translation task. © 2012 Association for Computational Linguistics.

关键词： computer aided language translation

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Pivot lightly-supervised training for statistical machine translation 10

Pivot lightly-supervised training for statistical machine tr...

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10th Conference of the Association for Machine Translation in the Americas, AMTA 2012

作者： Huck, Matthias Ney, Hermann Human Language Technology and Pattern Recognition Group Computer Science Department RWTH Aachen University AachenD-52056 Germany

In this paper, we investigate large-scale lightly-supervised training with a pivot language: We augment a baseline statistical machine translation (SMT) system that has been trained on human-generated parallel training corpora with large amounts of additional unsupervised parallel data;but instead of creating this synthetic data from monolingual source language data with the baseline system itself, or from target language data with a reverse system, we employ a parallel corpus of target language data and data in a pivot language. The pivot language data is automatically translated into the source language, resulting in a trilingual corpus with unsupervised source language side. We augment our baseline system with the unsupervised sourcetarget parallel data. Experiments are conducted for the German- French language pair using the standard WMT newstest sets for development and testing. We obtain the unsupervised data by translating the English side of the English-French 109 corpus to German. With careful system design, we are able to achieve improvements of up to +0.4 points BLEU / -0.7 points TER over the baseline.

关键词： computer aided language translation

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Discriminative reordering extensions for hierarchical phrase-based machine translation 16

Discriminative reordering extensions for hierarchical phrase...

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16th Annual Conference of the European Association for Machine Translation, EAMT 2012

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

In this paper, we propose novel extensions of hierarchical phrase-based systems with a discriminative lexicalized reordering model. We compare different feature sets for the discriminative reordering model and investigate combinations with three types of non-lexicalized reordering rules which are added to the hierarchical grammar in order to allow for more reordering flexibility during decoding. All extensions are evaluated in standard hierarchical setups as well as in setups where the hierarchical recursion depth is restricted. We achieve improvements of up to +1.2 %BLEU on a large-scale Chinese→English translation task. © 2012 European Association for Machine Translation.

关键词： Hierarchical systems

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Semantic cohesion model for phrase-based SMT

Semantic cohesion model for phrase-based SMT

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24th International Conference on Computational Linguistics, COLING 2012

作者： Feng, Minwei Sun, Weiwei Ney, Hermann Computer Science Department Human Language Technology and Pattern Recognition Group RWTH Aachen University Aachen Germany MOE Key Laboratory of Computational Linguistics Institute of Computer Science and Technology Peking University Beijing China

In this paper, we propose a novel semantic cohesion model. Our model utilizes the predicateargument structures as soft constraints and plays the role as a reordering model in the phrasebased statistical machine translation system. We build a translation system with GALE data. Experimental results on the NIST02, NIST03, NIST04, NIST05 and NIST08 Chinese-English tasks show that our model improves the baseline system by 0.93 BLEU 0.98 TER on average. We also compare our method with a syntax-augmented model (Cherry, 2008), and demonstrate the importance of predicate-argument semantics in machine translation. © 2012 The COLING.

关键词： Semantics

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Fast and scalable decoding with language model look-ahead for phrase-based statistical machine translation

Fast and scalable decoding with language model look-ahead fo...

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50th Annual Meeting of the Association for Computational Linguistics, ACL 2012

作者： Wuebker, Joern Ney, Hermann Zens, Richard Computer Science Department Human Language Technology and Pattern Recognition Group RWTH Aachen University Germany Google Inc. 1600 Amphitheatre Parkway Mountain View CA 94043 United States

ISBN: (纸本)9781937284251

In this work we present two extensions to the well-known dynamic programming beam search in phrase-based statistical machine translation (SMT), aiming at increased efficiency of decoding by minimizing the number of language model computations and hypothesis expansions. Our results show that language model based pre-sorting yields a small improvement in translation quality and a speedup by a factor of 2. Two look-ahead methods are shown to further increase translation speed by a factor of 2 without changing the search space and a factor of 4 with the side-effect of some additional search errors. We compare our approach with Moses and observe the same performance, but a substantially better trade-off between translation quality and speed. At a speed of roughly 70 words per second, Moses reaches 17.2% BLEU, whereas our approach yields 20.0% with identical models. © 2012 Association for Computational Linguistics.

关键词： Decoding

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