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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...

引用

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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OPEN VOCABULARY HANDWRITING recognition USING COMBINED WORD-LEVEL AND CHARACTER-LEVEL language MODELS

OPEN VOCABULARY HANDWRITING RECOGNITION USING COMBINED WORD-...

引用

IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Michal Kozielski David Rybach Stefan Hahn Ralf Schluter Hermann Ney Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen Germany Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen Germany

ISBN: (纸本)9781479903573

In this paper, we present a unified search strategy for open vocabulary handwriting recognition using weighted finite state transducers. Additionally to a standard word-level language model we introduce a separate n-gram character-level language model for out-of-vocabulary word detection and recognition. The probabilities assigned by those two models are combined into one Bayes decision rule. We evaluate the proposed method on the IAM database of English handwriting. An improvement from 22.2% word error rate to 17.3% is achieved comparing to the closed-vocabulary scenario and the best published result.

关键词： Handwriting Vocabulary modelling languages search strategies Error analysis Word Bayes Decision Rule handwriting recognition

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THE RWTH 2010 QUAERO ASR EVALUATION SYSTEM FOR ENGLISH, FRENCH, AND GERMAN

THE RWTH 2010 QUAERO ASR EVALUATION SYSTEM FOR ENGLISH, FREN...

引用

IEEE International Conference on Acoustics, Speech and Signal Processing

作者： M. Sundermeyer M. Nussbaum-Thom S. Wiesler C. Plahl A. El-Desoky Mousa S. Hahn D. Nolden R. Schluter H. Ney Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University

ISBN: (纸本)9781457705380

Recognizing Broadcast Conversational (BC) speech data is a difficult task, which can be regarded as one of the major challenges beyond the recognition of Broadcast News (BN). This paper presents the automatic speech recognition systems developed by RWTH for the English, French, and German language which attained the best word error rates for English and German, and competitive results for the French task in the 2010 Quaero evaluation for BC and BN data. At the same time, the RWTH German system used the least amount of training data among all participants. Large reductions in word error rate were obtained by the incorporation of the new Bottleneck Multilayer Perceptron (MLP) features for all three languages. Additional improvements were obtained for the German system by applying a new language modeling technique, decomposing words into sublexical components.

关键词： automatic speech recognition multilayer perceptrons

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FEATURE COMBINATION AND STACKING OF RECURRENT AND NON-RECURRENT NEURAL NETWORKS FOR LVCSR

FEATURE COMBINATION AND STACKING OF RECURRENT AND NON-RECURR...

引用

IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Christian Plahl Michael Kozielski Ralf Schluter Hermann Ney Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University

ISBN: (纸本)9781479903573

This paper investigates the combination of different short-term features and the combination of recurrent and non-recurrent neural networks (NNs) on a Spanish speech recognition task. Several methods exist to combine different feature sets such as concatenation or linear discriminant analysis (LDA). Even though all these techniques achieve reasonable improvements, feature combination by multi-layer perceptrons (MLPs) outperforms all known approaches. We develop the concept of MLP based feature combination further using recurrent neural networks (RNNs). The phoneme posterior estimates derived from an RNN lead to a significant improvement over the result of the MLPs and achieve a 5% relative better word error rate (WER) with much less parameters. Moreover, we improve the system performance further by combining an MLP and an RNN in a hierarchical framework. The MLP benefits from the preprocessing of the RNN. All NNs are trained on phonemes. Nevertheless, the same concepts could be applied using context-dependent states. In addition to the improvements in recognition performance w.r.t. WER, NN based feature combination methods reduce both, the training and the testing complexity. Overall, the systems are based on a single set of acoustic models, together with the training of different NNs.

关键词： Feature combination Multi-layer perceptron Recurrent neural networks Long-short-term-memory Speech recognition recurrent neural nets Speech recognition CSRP3 gene Stacking Neural network System performance Training

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Direct construction of compact context-dependency transducers from data

Direct construction of compact context-dependency transducer...

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作者： Rybach, David Riley, Michael Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Germany Google Inc. 76 Ninth Avenue New York NY United States

This paper describes a new method for building compact context-dependency transducers for finite-state transducer-based ASR decoders. Instead of the conventional phonetic decision-tree growing followed by FST compilation, this approach incorporates the phonetic context splitting directly into the transducer construction. The objective function of the split optimization is augmented with a regularization term that measures the number of transducer states introduced by a split. We give results on a large spoken-query task for various n-phone orders and other phonetic features that show this method can greatly reduce the size of the resulting context-dependency transducer with no significant impact on recognition accuracy. This permits using context sizes and features that might otherwise be unmanageable. © 2010 ISCA.

关键词： Transducers

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Audio segmentation for speech recognition using segment features

Audio segmentation for speech recognition using segment feat...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： David Rybach Christian Gollan Ralf Schluter Hermann Ney Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Germany

Audio segmentation is an essential preprocessing step in several audio processing applications with a significant impact e.g. on speech recognition performance. We introduce a novel framework which combines the advantages of different well known segmentation methods. An automatically estimated log-linear segment model is used to determine the segmentation of an audio stream in a holistic way by a maximum a posteriori decoding strategy, instead of classifying change points locally. A comparison to other segmentation techniques in terms of speech recognition performance is presented, showing a promising segmentation quality of our approach.

关键词： Speech recognition Streaming media Decoding Broadcasting Loudspeakers Automatic speech recognition Bayesian methods humans Natural languages pattern recognition

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Investigations on byte-level convolutional neural networks for language modeling in low resource speech recognition

Investigations on byte-level convolutional neural networks f...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Kazuki Irie Pavel Golik Ralf Schluter Hermann Ney Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Germany

ISBN: (纸本)9781509041183

In this paper, we present an investigation on technical details of the byte-level convolutional layer which replaces the conventional linear word projection layer in the neural language model. In particular, we discuss and compare the effective filter configurations, pooling types and the use of bytes instead of characters. We carry out experiments on language packs released by the IARPA Babel project and measure the performance in terms of perplexity and word error rate. Introducing a convolutional layer consistently improves the results on all languages. Also, there is no degradation from using raw bytes instead of proper Unicode characters, even on syllabic alphabets like Amharic. In addition, we report improvements in word error rate from rescoring lattices and evaluate keyword search performance on several languages.

关键词： language modeling convolutional neural networks speech recognition keyword search modelling languages Speech recognition Key Search Error analysis language Amharic Byte Word

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Advances in Arabic broadcast news transcription at RWTH

Advances in Arabic broadcast news transcription at RWTH

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IEEE Workshop on Automatic Speech recognition and Understanding

作者： David Rybach Stefan Hahn Christian Gollan Ralf Schluter Hermann Ney Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Germany

ISBN: (纸本)9781424413690;1424413699

This paper describes the RWTH speech recognition system for Arabic. Several design aspects of the system, including cross-adaptation, multiple system design and combination, are analyzed. We summarize the semi-automatic lexicon generation for Arabic using a statistical approach to grapheme-to-phoneme conversion and pronunciation statistics. Furthermore, a novel ASR-based audio segmentation algorithm is presented. Finally, we discuss practical approaches for parallelized acoustic training and memory efficient lattice rescoring. Systematic results are reported on recent GALE evaluation corpora.

关键词： Broadcasting Mel frequency cepstral coefficient Hidden Markov models Neural networks Lattices Cepstral analysis Speech recognition humans Natural languages Loudspeakers

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Morpheme-based feature-rich language models using Deep Neural Networks for LVCSR of Egyptian Arabic

Morpheme-based feature-rich language models using Deep Neura...

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2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013

作者： El-Desoky Mousa, Amr Kuo, Hong-Kwang Jeff Mangu, Lidia Soltau, Hagen Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University 52056 Aachen Germany IBM T. J. Watson Research Center Yorktown Heights NY 10598 United States

ISBN: (纸本)9781479903566

Egyptian Arabic (EA) is a colloquial version of Arabic. It is a low-resource morphologically rich language that causes problems in Large Vocabulary Continuous Speech recognition (LVCSR). Building LMs on morpheme level is considered a better choice to achieve higher lexical coverage and better LM probabilities. Another approach is to utilize information from additional features such as morphological tags. On the other hand, LMs based on Neural Networks (NNs) with a single hidden layer have shown superiority over the conventional n-gram LMs. Recently, Deep Neural Networks (DNNs) with multiple hidden layers have achieved better performance in various tasks. In this paper, we explore the use of feature-rich DNN-LMs, where the inputs to the network are a mixture of words and morphemes along with their features. Significant Word Error Rate (WER) reductions are achieved compared to the traditional word-based LMs. © 2013 IEEE.

关键词： Deep neural networks

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Improvement of Context Dependent Modeling for Arabic Handwriting recognition

Improvement of Context Dependent Modeling for Arabic Handwri...

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International Workshop on Frontiers in Handwriting recognition

作者： Mahdi Hamdani Patrick Doetsch Hermann Ney Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen Germany

This paper proposes the improvement of context dependent modeling for Arabic handwriting recognition. Since the number of parameters in context dependent models is huge, CART trees are used for state tying. This work is based on a new set of questions for the CART tree construction based on a "lossy mapping" categorization of the Arabic shapes. The used system is a combination of Hidden Markov Models and Recurrent Neural Networks using the hybrid approach. A comparison between a Neural network trained using the baseline labels and another one based on the CART tree labels is done. The experimental results show that the use of the CART labels for the Neural Network training beneficial. The lossy mapping based CART tree performed better than the baseline system. An absolute improvement of 2.9% in terms of Word Error Rate is performed on the test set of the Open Hart database.

关键词： Hidden Markov models Context Handwriting recognition Context modeling Training Shape Speech recognition

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