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

共 385 条记录，以下是261-270 订阅

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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...

引用

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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A CRITICAL EVALUATION OF STOCHASTIC ALGORITHMS FOR CONVEX OPTIMIZATION

A CRITICAL EVALUATION OF STOCHASTIC ALGORITHMS FOR CONVEX OP...

引用

IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Simon Wiesler Alexander Richard Ralf Schluter Hermann Ney Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen Germany

ISBN: (纸本)9781479903573

Log-linear models find a wide range of applications in pattern recognition. The training of log-linear models is a convex optimization problem. In this work, we compare the performance of stochastic and batch optimization algorithms. Stochastic algorithms are fast on large data sets but can not be parallelized well. In our experiments on a broadcast conversations recognition task, stochastic methods yield competitive results after only a short training period, but when spending enough computational resources for parallelization, batch algorithms are competitive with stochastic algorithms. We obtained slight improvements by using a stochastic second order algorithm. Our best log-linear model outperforms the maximum likelihood trained Gaussian mixture model baseline although being ten times smaller.

关键词： discriminative models optimization speech recognition

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DEEP HIERARCHICAL BOTTLENECK MRASTA FEATURES FOR LVCSR

DEEP HIERARCHICAL BOTTLENECK MRASTA FEATURES FOR LVCSR

引用

IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Zoltan Tuske Ralf Schluter Hermann Ney Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University 52056 Aachen Germany

ISBN: (纸本)9781479903573

Hierarchical Multi Layer Perceptron (MLP) based long-term feature extraction is optimized for TANDEM connectionist large vocabulary continuous speech recognition (LVCSR) system within the QUAERO project. Training the bottleneck MLP on multi-resolutional RASTA filtered critical band energies, more than 20% relative word error rate (WER) reduction over standard MFCC system is observed after optimizing the number of target labels. Furthermore, introducing a deeper structure in the hierarchical bottleneck processing the relative gain increases to 25%. The final system based on deep bottleneck TANDEM features clearly outperforms the hybrid approach, even if the long-term features are also presented to the deep MLP acoustic model. The results are also verified on evaluation data of the year 2012, and about 20% relative WER improvement over classical cepstral system is measured even after speaker adaptive training.

关键词： LVCSR MRASTA MLP bottleneck hierarchical deep neural network hybrid TANDEM Traffic Bottlenecks CSRP3 gene Hierarchical HYBRID Tandem relative gain

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INVESTIGATION ON CROSS- AND MULTILINGUAL MLP FEATURES UNDER MATCHED AND MISMATCHED ACOUSTICAL CONDITIONS

INVESTIGATION ON CROSS- AND MULTILINGUAL MLP FEATURES UNDER ...

引用

IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Zoltan Tuske Joel Pinto Daniel Willett Ralf Schluter Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Nuance Communications Deutschland GmbH

ISBN: (纸本)9781479903573

In this paper, Multi Layer Perceptron (MLP) based multilingual bottleneck features are investigated for acoustic modeling in three languages -- German, French, and US English. We use a modified training algorithm to handle the multilingual training scenario without having to explicitly map the phonemes to a common phoneme set. Furthermore, the cross-lingual portability of bottleneck features between the three languages are also investigated. Single pass recognition experiments on large vocabulary SMS dictation task indicate that (1) multilingual bottleneck features yield significantly lower word error rates compared to standard MFCC features (2) multilingual bottleneck features are superior to monolingual bottleneck features trained for the target language with limited training data, and (3) multilingual bottleneck features are beneficial in training acoustic models in a low resource language where only mismatched training data is available - by exploiting the more matched training data from other languages.

关键词： MLP Bottleneck Multilingual Mismatched acoustical condition Traffic Bottlenecks CSRP3 gene Multilingualism Object oriented programming languages language acoustic modeling Training data Phonemes Error analysis

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Improvements in RWTH's System for Off-Line Handwriting Recognition

Improvements in RWTH's System for Off-Line Handwriting Recog...

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International Conference on Document Analysis and Recognition

作者： Michal Michał Kozielski Patrick Doetsch Hermann Ney Human Language Technology and Pattern Recognition Group RWTH Aachen University Aachen Germany Rheinisch-Westfalische Technische Hochschule Aachen Aachen Nordrhein-Westfalen DE Human Language Technol. & Pattern Recognition Group RWTH Aachen Univ. Aachen Germany

ISBN: (纸本)9780769549993

In this paper we describe a novel HMM-based system for off-line handwriting recognition. We adapt successful techniques from the domains of large vocabulary speech recognition and image object recognition: moment-based image normalization, writer adaptation, discriminative feature extraction and training, and open-vocabulary recognition. We evaluate those methods and examine their cumulative effect on the recognition performance. The final system outperforms current state-of-the-art approaches on two standard evaluation corpora for English and French handwriting.

关键词： Hidden Markov models Training Handwriting recognition Feature extraction Databases Error analysis Standards

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COMPARISON OF FEEDFORWARD AND RECURRENT NEURAL NETWORK language MODELS

COMPARISON OF FEEDFORWARD AND RECURRENT NEURAL NETWORK LANGU...

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IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： M. Sundermeyer I. Oparin J.-L. Gauvain B. Freiberg R. Schluter H. Ney Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen Germany Spoken Language Processing Group LIMSI CNRS Paris France

ISBN: (纸本)9781479903573

Research on language modeling for speech recognition has increasingly focused on the application of neural networks. Two competing concepts have been developed: On the one hand, feedforward neural networks representing an ngram approach, on the other hand recurrent neural networks that may learn context dependencies spanning more than a fixed number of predecessor words. To the best of our knowledge, no comparison has been carried out between feedforward and state-of-the-art recurrent networks when applied to speech recognition. This paper analyzes this aspect in detail on a well-tuned French speech recognition task. In addition, we propose a simple and efficient method to normalize language model probabilities across different vocabularies, and we show how to speed up training of recurrent neural networks by parallelization.

关键词： Automatic speech recognition feedforward neural networks recurrent neural networks Speech recognition recurrent neural nets feedforward neural nets FEEDFORWARD modelling languages Neural network parallelization

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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...

引用

IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Amr El-Desoky Mousa Hong-Kwang Jeff Kuo Lidia Mangu Hagen Soltau Human Language Technology and Pattern Recognition - Computer Science Department RWTH Aachen University IBM T. J. Watson Research Center

ISBN: (纸本)9781479903573

关键词： language model Morpheme Feature-rich Deep neural network Egyptian Arabic modelling languages Neural network Arabic language additional features Word Religious Missions Continuous speech recognition

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Open Vocabulary Arabic Handwriting Recognition Using Morphological Decomposition

Open Vocabulary Arabic Handwriting Recognition Using Morphol...

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International Conference on Document Analysis and Recognition

作者： Mahdi Hamdani Amr El-Desoky Mousa Hermann Ney Human Language Technology and Pattern Recognition Group RWTH Aachen University Germany Rheinisch-Westfalische Technische Hochschule Aachen Aachen Nordrhein-Westfalen DE

ISBN: (纸本)9781479901937

The use of language Models (LMs) is a very important component in large and open vocabulary recognition systems. This paper presents an open-vocabulary approach for Arabic handwriting recognition. The proposed approach makes use of Arabic word decomposition based on morphological analysis. The vocabulary is a combination of words and sub-words obtained by the decomposition process. Out Of Vocabulary (OOV) words can be recognized by combining different elements from the lexicon. The recognition system is based on Hidden Markov Models (HMMs) with position and context dependent character models. An n-gram LM trained on the decomposed text is used along with the HMMs during the search. The approach is evaluated using two Arabic handwriting datasets. The open vocabulary approach leads to a significant improvement in the system performance. Two different types experiments for two Arabic handwriting recognition tasks are conducted in this work. The proposed approach for open vocabulary allows to have an absolute improvement of up to 1% in the Word Error Rate (WER) for the constrained task and to keep the same performance of the baseline system for the unconstrained one.

关键词： Vocabulary Hidden Markov models Handwriting recognition Training Feature extraction Context Character recognition

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May the force be with you: Force-aligned signwriting for automatic subunit annotation of corpora

May the force be with you: Force-aligned signwriting for aut...

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International Conference on Automatic Face and Gesture Recognition

作者： Oscar Koller Hermann Ney Richard Bowden Centre for Vision Speech and Signal Processing University of Surrey Guildford UK Human Language Technology and Pattern Recognition Group RWTH Aachen University Germany

ISBN: (纸本)9781467355452

We propose a method to generate linguistically meaningful subunits in a fully automated fashion for sign language corpora. The ability to automate the process of subunit annotation has profound effects on the data available for training sign language recognition systems. The approach is based on the idea that subunits are shared among different signs. With sufficient data and knowledge of possible signing variants, accurate automatic subunit sequences are produced, matching the specific characteristics of given sign language data. Specifically we demonstrate how an iterative forced alignment algorithm can be used to transfer the knowledge of a user-edited open sign language dictionary to the task of annotating a challenging, large vocabulary, multi-signer corpus recorded from public TV. Existing approaches focus on labour intensive manual subunit annotations or on data-driven approaches. Our method yields an average precision and recall of 15% under the maximum achievable accuracy with little user intervention beyond providing a simple word gloss.

关键词： Assistive technology Gesture recognition Dictionaries Hidden Markov models Databases Manuals Vocabulary

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Deciphering foreign language by combining language models and context vectors

Deciphering foreign language by combining language models an...

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

作者： Nuhn, Malte Mauser, Arne Ney, Hermann Human Language Technology and Pattern Recognition Group RWTH Aachen University Germany

ISBN: (纸本)9781937284244

In this paper we show how to train statistical machine translation systems on reallife tasks using only non-parallel monolingual data from two languages. We present a modification of the method shown in (Ravi and Knight, 2011) that is scalable to vocabulary sizes of several thousand words. On the task shown in (Ravi and Knight, 2011) we obtain better results with only 5% of the computational effort when running our method with an n-gram language model. The efficiency improvement of our method allows us to run experiments with vocabulary sizes of around 5,000 words, such as a non-parallel version of the VERBMOBIL corpus. We also report results using data from the monolingual French and English GIGAWORD corpora. © 2012 Association for computational Linguistics.

关键词： Computational linguistics

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