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Returnn: The RWTH extensible training framework for universal recurrent neural networks

Returnn: The RWTH extensible training framework for universa...

引用

2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017

作者： Doetsch, Patrick Zeyer, Albert Voigtlaender, Paul Kulikov, Ilia Schlüter, Ralf Ney, Hermann Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen52062 Germany

ISBN: (纸本)9781509041176

In this work we release our extensible and easily configurable neural network training software. It provides a rich set of functional layers with a particular focus on efficient training of recurrent neural network topologies on multiple GPUS. The source of the software package is public and freely available for academic research purposes and can be used as a framework or as a standalone tool which supports a flexible configuration. The software allows to train state-of-the-art deep bidirectional long short-term memory (LSTM) models on both one dimensional data like speech or two dimensional data like handwritten text and was used to develop successful submission systems in several evaluation campaigns. © 2017 IEEE.

关键词： lstm multi-GPU recurrent neural networks rnn software package speech recognition

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Morpheme based Factored language Models for German LVCSR

Morpheme based Factored Language Models for German LVCSR

引用

12th Annual Conference of the International Speech Communication Association, INTERSPEECH 2011

作者： El-Desoky Mousa, Amr Ali Basha Shaik, M. Schlüter, Ralf Ney, Hermann Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University 52056 Aachen Germany

German is a highly inflectional language, where a large number of words can be generated from the same root. It makes a liberal use of compounding leading to high Out-of-vocabulary (OOV) rates, and poor language Model (LM) probability estimates. Therefore, the use of morphemes for language modeling is considered a better choice for Large Vocabulary Continuous Speech Recognition (LVCSR) than the full-words. Thereby, better lexical coverage and less LM perplexities are achieved. On the other side, the use of Factored language Models (FLMs) is considered a successful approach that allows the integration of many information sources to get better LM probability estimates. In this paper, we try a combined methodology for language modeling where both morphological decomposition and factored language modeling are used in one model called morpheme based FLM. Finally, we obtain around 2.5% relative reduction in Word Error Rate (WER) with respect to a traditional full-words system. Copyright © 2011 ISCA.

关键词： Modeling languages

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Using morpheme and syllable based sub-words for polish LVCSR

Using morpheme and syllable based sub-words for polish LVCSR

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36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011

作者： Shaik, M. Ali Basha El-Desoky Mousa, Amr Schlüter, Ralf Ney, Hermann Human Language Technology and Pattern Recognition - Computer Science Department RWTH Aachen University 52056 Aachen Germany

ISBN: (纸本)9781457705397

Polish is a synthetic language with a high morpheme-per-word ratio. It makes use of a high degree of inflection leading to high out-of-vocabulary (OOV) rates, and high language Model (LM) perplexities. This poses a challenge for Large Vocabulary and Continuous Speech Recognition (LVCSR) systems. Here, the use of morpheme and syllable based units is investigated for building sub-lexical LMs. A different type of sub-lexical units is proposed based on combining morphemic or syllabic units with corresponding pronunciations. Thereby, a set of grapheme-phoneme pairs called graphones are used for building LMs. A relative reduction of 3.5% in Word Error Rate (WER) is obtained with respect to a traditional system based on full-words. © 2011 IEEE.

关键词： Continuous speech recognition

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Confidence-based discriminative training for model adaptation in offline Arabic handwriting recognition

Confidence-based discriminative training for model adaptatio...

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ICDAR2009 - 10th International Conference on Document Analysis and Recognition

作者： Dreuw, Philippe Heigold, Georg Ney, Hermann RWTH Aachen University Human Language Technology and Pattern Recognition Ahornstr 55 D-52056 Aachen Germany

ISBN: (纸本)9780769537252

We present a novel confidence-based discriminative training for model adaptation approach for an HMM based Arabic handwriting recognition system to handle different handwriting styles and their variations. Most current approaches are maximum-likelihood trained HMM systems and try to adapt their models to different writing styles using writer adaptive training, unsupervised clustering, or additional writer specific data. Discriminative training based on the Maximum Mutual Information criterion is used to train writer independent handwriting models. For model adaptation during decoding, an unsupervised confidence-based discriminative training on a word and frame level within a two-pass decoding process is proposed. Additionally, the training criterion is extended to incorporate a margin term. The proposed methods are evaluated on the IFN/ENIT Arabic handwriting database, where the proposed novel adaptation approach can decrease the word-error-rate by 33% relative. © 2009 IEEE.

关键词： Decoding

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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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The RWTH Aachen Machine Translation System for IWSLT 2011 8

The RWTH Aachen Machine Translation System for IWSLT 2011

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8th International Workshop on Spoken language Translation, IWSLT 2011

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

In this paper the statistical machine translation (SMT) systems of RWTH Aachen University developed for the evaluation campaign of the International Workshop on Spoken language Translation (IWSLT) 2011 is presented. We participated in the MT (English-French, Arabic-English, Chinese-English) and SLT (English-French) tracks. Both hierarchical and phrase-based SMT decoders are applied. A number of different techniques are evaluated, including domain adaptation via monolingual and bilingual data selection, phrase training, different lexical smoothing methods, additional reordering models for the hierarchical system, various Arabic and Chinese segmentation methods, punctuation prediction for speech recognition output, and system combination. By application of these methods we can show considerable improvements over the respective baseline systems. © IWSLT 2011. All rights reserved.

关键词： Hierarchical systems

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Writer adaptive training and writing variant model refinement for offline Arabic handwriting recognition

Writer adaptive training and writing variant model refinemen...

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ICDAR2009 - 10th International Conference on Document Analysis and Recognition

作者： Dreuw, Philippe Rybach, David Gollan, Christian Ney, Hermann RWTH Aachen University Human Language Technology and Pattern Recognition Ahornstr 55 D-52056 Aachen Germany

ISBN: (纸本)9780769537252

We present a writer adaptive training and writer clustering approach for an HMM based Arabic handwriting recognition system to handle different handwriting styles and their variations. Additionally, a writing variant model refinement for specific writing variants is proposed. Current approaches try to compensate the impact of different writing styles during preprocessing and normalization steps. Writer adaptive training with a CMLLR based feature adaptation is used to train writer dependent models. An unsupervised writer clustering with Bayesian information criterion based stopping condition for a CMLLR based feature adaptation during a two-pass decoding process is used to cluster different handwriting styles of unknown test writers. The proposed methods are evaluated on the IFN/ENIT Arabic handwriting database. © 2009 IEEE.

关键词： Character recognition

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Hybrid language models using mixed types of sub-lexical units for open vocabulary German LVCSR

Hybrid language models using mixed types of sub-lexical unit...

引用

12th Annual Conference of the International Speech Communication Association, INTERSPEECH 2011

作者： Ali Basha Shaik, M. El-Desoky Mousa, Amr Schlüter, Ralf Ney, Hermann Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University 52056 Aachen Germany

German is a highly inflected language with a large number of words derived from the same root. It makes use of a high degree of word compounding leading to high Out-of-vocabulary (OOV) rates, and language Model (LM) perplexities. For such languages the use of sub-lexical units for Large Vocabulary Continuous Speech Recognition (LVCSR) becomes a natural choice. In this paper, we investigate the use of mixed types of sub-lexical units in the same recognition lexicon. Namely, morphemic or syllabic units combined with pronunciations called graphones, normal graphemic morphemes or syllables along with full-words. This mixture of units is used for building hybrid LMs suitable for open vocabulary LVCSR where the system operates over an open, constantly changing vocabulary like in broadcast news, political debates, etc. A relative reduction of around 5.0% in Word Error Rate (WER) is obtained compared to a traditional full-words system. Moreover, around 40% of the OOVs are recognized. Copyright © 2011 ISCA.

关键词： Computational linguistics

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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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Acoustic feature combination for robust speech recognition

Acoustic feature combination for robust speech recognition

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2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '05

作者： Zolnay, András Schlüter, Ralf Ney, Hermann Computer Science Department Human Language Technology and Pattern Recognition RWTH Aachen University 52056 Aachen Germany

ISBN: (纸本)0780388747

In this paper, we consider the use of multiple acoustic features of the speech signal for robust speech recognition. We investigate the combination of various auditory based (Mel Frequency Cepstrum Coefficients, Perceptual Linear Prediction, etc.) and articulatory based (voicedness) features. Features are combined by a Linear Discriminant Analysis based and by a log-linear model combination based techniques. We describe the two feature combination techniques and compare the experimental results. Experiments performed on the large-vocabulary task VerbMobil II (German conversational speech) show that the accuracy of automatic speech recognition systems can be improved by the combination of different acoustic features. © 2005 IEEE.

关键词： Feature extraction

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