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Speaker adaptive joint training of Gaussian mixture models and bottleneck features

Speaker adaptive joint training of Gaussian mixture models a...

引用

IEEE Workshop on Automatic Speech recognition and Understanding

作者： Zoltán Tüske Pavel Golik Ralf Schlüter Hermann Ney Human Language Technology and Pattern Recognition RWTH Aachen University Aachen Germany

In the tandem approach, the output of a neural network (NN) serves as input features to a Gaussian mixture model (GMM) aiming to improve the emission probability estimates. As has been shown in our previous work, GMM with pooled covariance matrix can be integrated into a neural network framework as a softmax layer with hidden variables, which allows for joint estimation of both neural network and Gaussian mixture parameters. Here, this approach is extended to include speaker adaptive training (SAT) by introducing a speaker dependent neural network layer. Error backpropagation beyond this speaker dependent layer realizes the adaptive training of the Gaussian parameters as well as the optimization of the bottleneck (BN) tandem features of the underlying acoustic model, simultaneously. In this study, after the initialization by constrained maximum likelihood linear regression (CMLLR) the speaker dependent layer itself is kept constant during the joint training. Experiments show that the deeper backpropagation through the speaker dependent layer is necessary for improved recognition performance. The speaker adaptively and jointly trained BN-GMM results in 5% relative improvement over very strong speaker-independent hybrid baseline on the Quaero English broadcast news and conversations task, and on the 300-hour Switchboard task.

关键词： Adaptation models Training Acoustics Hidden Markov models Robustness Artificial neural networks

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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

IEEE Workshop on Automatic Speech recognition and Understanding

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

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.

关键词： Hidden Markov models Training Optimization Convergence Speech recognition Eigenvalues and eigenfunctions Polynomials

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Confidence-Based Discriminative Training for Model Adaptation in Offline Arabic Handwriting recognition

Confidence-Based Discriminative Training for Model Adaptatio...

引用

International Conference on Document Analysis and recognition

作者： Philippe Dreuw Georg Heigold Hermann Ney Human Language Technology and Pattern Recognition RWTH Aachen University Aachen Germany

ISBN: (纸本)9781424445004

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 *** 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 *** 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 *** 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.

关键词： Adaptation model Handwriting recognition Hidden Markov models Writing Mutual information Maximum likelihood decoding Maximum likelihood estimation pattern recognition Databases Automatic speech recognition

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EXPLOITING SPARSENESS OF BACKING-OFF language MODELS FOR EFFICIENT LOOK-AHEAD IN LVCSR

EXPLOITING SPARSENESS OF BACKING-OFF LANGUAGE MODELS FOR EFF...

引用

IEEE International Conference on Acoustics, Speech and Signal Processing

作者： David Nolden Hermann Ney Ralf Schluter Human Language Technology and Pattern Recognition GroupRWTH Aachen University Aachen Germany

ISBN: (纸本)9781457705380

In this paper, we propose a new method for computing and applying language model look-ahead in a dynamic network decoder, exploiting the sparseness of backing-off n-gram language models. Only partial (sparse) look-ahead tables are computed, with a size that depends on the number of words that have an n-gram score in the language model for a specific context, rather than a constant, vocabulary dependent size. Since high order backing-off language models are inherently sparse, this mechanism reduces the runtime- and memory effort of computing the look-ahead tables by magnitudes. A modified decoding algorithm is required to apply these sparse LM look-ahead tables efficiently. We show that sparse LM look-ahead is much more efficient than the classical method, and that full n-gram look-ahead becomes favorable over lower order look-ahead even when many distinct LM contexts appear during decoding.

关键词： speech recognition decoding search language model look-ahead modelling languages Tables language DECODING indium new methods dynamic network WWOX gene decoding algorithm

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Full-Sum Decoding for Hybrid Hmm Based Speech recognition Using LSTM language Model

Full-Sum Decoding for Hybrid Hmm Based Speech Recognition Us...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Wei Zhou Ralf Schluter Hermann Ney Human Language Technology and Pattern Recognition RWTH Aachen University Aachen Germany

ISBN: (数字)9781509066315

ISBN: (纸本)9781509066322

In hybrid HMM based speech recognition, LSTM language models have been widely applied and achieved large improvements. The theoretical capability of modeling any unlimited context suggests that no recombination should be applied in decoding. This motivates to reconsider full summation over the HMM-state sequences instead of Viterbi approximation in decoding. We explore the potential gain from more accurate probabilities in terms of decision making and apply the full-sum decoding with a modified prefix-tree search framework. The proposed full-sum decoding is evaluated on both Switchboard and Librispeech corpora. Different models using CE and sMBR training criteria are used. Additionally, both MAP and confusion network decoding as approximated variants of general Bayes decision rule are evaluated. Consistent improvements over strong baselines are achieved in almost all cases without extra cost. We also discuss tuning effort, efficiency and some limitations of full-sum decoding.

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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...

引用

International Conference on Document Analysis and recognition

作者： Philippe Dreuw David Rybach Christian Gollan Hermann Ney Human Language Technology and Pattern Recognition RWTH Aachen University Aachen Germany

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.

关键词： Writing Handwriting recognition Maximum likelihood decoding Hidden Markov models Automatic speech recognition Maximum likelihood linear regression pattern recognition Bayesian methods Databases Text analysis

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Handwriting recognition with Large Multidimensional Long Short-Term Memory Recurrent Neural Networks

Handwriting Recognition with Large Multidimensional Long Sho...

引用

International Workshop on Frontiers in Handwriting recognition

作者： Paul Voigtlaender Patrick Doetsch Hermann Ney Human Language Technology and Pattern Recognition RWTH Aachen University Aachen Germany

ISBN: (纸本)9781509009824

Multidimensional long short-term memory recurrent neural networks achieve impressive results for handwriting recognition. However, with current CPU-based implementations, their training is very expensive and thus their capacity has so far been limited. We release an efficient GPU-based implementation which greatly reduces training times by processing the input in a diagonal-wise fashion. We use this implementation to explore deeper and wider architectures than previously used for handwriting recognition and show that especially the depth plays an important role. We outperform state of the art results on two databases with a deep multidimensional network.

关键词： Training Handwriting recognition Recurrent neural networks Graphics processing units Standards Context Databases

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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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Impact of automatic segmentation on the quality, productivity and self-reported post-editing effort of intralingual subtitles 10

Impact of automatic segmentation on the quality, productivit...

引用

10th International Conference on language Resources and Evaluation, LREC 2016

作者： Álvarez, Aitor Balenciaga, Marina Del Pozo, Arantza Arzelus, Haritz Matamala, Anna Martínez-Hinarejos, Carlos-D. Human Speech and Language Technology Group Vicomtech-IK4 San-Sebastian Spain Department of Translation Interpreting and East Asian Studies UAB Barcelona Spain Pattern Recognition and Human Language Technologies Research Center Universitat Politècnica de València Spain

ISBN: (纸本)9782951740891

This paper describes the evaluation methodology followed to measure the impact of using a machine learning algorithm to automatically segment intralingual subtitles. The segmentation quality, productivity and self-reported post-editing effort achieved with such approach are shown to improve those obtained by the technique based in counting characters, mainly employed for automatic subtitle segmentation currently. The corpus used to train and test the proposed automated segmentation method is also described and shared with the community, in order to foster further research in this area.

关键词： Productivity

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Towards Unsupervised Learning for Handwriting recognition

Towards Unsupervised Learning for Handwriting Recognition

引用

International Workshop on Frontiers in Handwriting recognition

作者： Michal Kozielski Malte Nuhn Patrick Doetsch Hermann Ney Human Language Technology and Pattern Recognition Group RWTH Aachen University Aachen Germany

We present a method for training an off-line handwriting recognition system in an unsupervised manner. For an isolated word recognition task, we are able to bootstrap the system without any annotated data. We then retrain the system using the best hypothesis from a previous recognition pass in an iterative fashion. Our approach relies only on a prior language model and does not depend on an explicit segmentation of words into characters. The resulting system shows a promising performance on a standard dataset in comparison to a system trained in a supervised fashion for the same amount of training data.

关键词： Hidden Markov models Training Vocabulary Error analysis Handwriting recognition Training data Ciphers

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