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

作者： Stefan Hahn Patrick Lehnen Hermann Ney Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen Germany

ISBN: (纸本)9781457705380

Conditional Random Fields (CRFs) have proven to per form well on natural language processing tasks like name transliteration, concept tagging or grapheme-to-phoneme (g2p) conversion. The aim of this paper is to propose some extension to the state-of-the-art CRF systems for these tasks. Since the number of features can grow rapidly, a method for features selection is very helpful to boost performance. A combination of L1 and L2 regularization (elastic net) has been adopted and implemented within the Rprop optimization algorithm. Usually, dependencies on the target side are limited to bigram dependencies since the computational complexity grows exponentially with the history length. We present a modified CRF decoding where a conventional language model on target side is integrated into the CRF search process. Thus, larger contexts can be taken into account. Besides these two main parts, the already published margin-extension to the CRF training criterion has been adopted.

关键词： Training Context Context modeling Optimization Natural language processing Software algorithms Interpolation

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DISCRIMINATIVE HMMS, LOG-LINEAR MODELS, AND CRFS: WHAT IS THE DIFFERENCE?

DISCRIMINATIVE HMMS, LOG-LINEAR MODELS, AND CRFS: WHAT IS TH...

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IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： G. Heigold S. Wiesler M. Nussbaum-Thom P. Lehnen R. Schluter H. Ney Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen Germany

ISBN: (纸本)9781424442959

Recently, there have been many papers studying discriminative acoustic modeling techniques like conditional random fields or discriminative training of conventional Gaussian HMMs. This paper will give an overview of the recent work and progress. We will strictly distinguish between the type of acoustic models on the one hand and the training criterion on the other hand. We will address two issues in more detail: the relation between conventional Gaussian HMMs and conditional random fields and the advantages of formulating the training criterion as a convex optimization problem. Experimental results for various speech tasks will be presented to carefully evaluate the different concepts and approaches, including both a digit string and large vocabulary continuous speech recognition tasks.

关键词： speech recognition hidden Markov model discriminative training log-linear model conditional random field

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A comparative study on vocabulary reduction for phrase table smoothing

arXiv

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arXiv 2019年

作者： Kim, Yunsu Guta, Andreas Wuebker, Joern Ney, Hermann Human Language Technology and Pattern Recognition Group RWTH Aachen University Aachen Germany Lilt Inc

This work systematically analyzes the smoothing effect of vocabulary reduction for phrase translation models. We extensively compare various word-level vocabularies to show that the performance of smoothing is not significantly affected by the choice of vocabulary. This result provides empirical evidence that the standard phrase translation model is extremely sparse. Our experiments also reveal that vocabulary reduction is more effective for smoothing large-scale phrase tables. Copyright © 2019, The Authors. All rights reserved.

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Faster sequence training

Faster sequence training

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Albert Zeyer Ilia Kulikov Ralf Schluter Hermann Ney Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University 52062 Germany

ISBN: (纸本)9781509041183

It has been shown that sequence-discriminative training can improve the performance for large vocabulary continuous speech recognition. Our main contribution is a novel method for reducing the computation time of any sort of sequence training while only slightly decreasing the overall performance. The method allows to parallelize the forward propagation through the network, the loss and loss gradient calculation which will provide a frame-wise error signal, and an independent forward and back propagation using that error signal. That last step can be calculated in a frame-wise manner and thus allows to use frame chunking to further improve the runtime. The loss calculation can itself be parallelized over many sequences. In addition to several experiments which outline the runtime gains, we also provide a convergence proof sketch. We extend on the research of sequence training of bidirectional long-short term memory ((B)LSTM) networks and provide an overview and comparison over different criteria. We have published all the code as part of our RETURNN and RASR framework including our training setup configurations.

关键词： sequence training LSTM computation time parallelization computational time parallelization Functional training Runtime forward propagation Training Fasting

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A comprehensive study of deep bidirectional LSTM RNNS for acoustic modeling in speech recognition

A comprehensive study of deep bidirectional LSTM RNNS for ac...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Albert Zeyer Patrick Doetsch Paul Voigtlaender Ralf Schluter Hermann Ney Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University 52062 Germany

ISBN: (纸本)9781509041183

Recent experiments show that deep bidirectional long short-term memory (BLSTM) recurrent neural network acoustic models outperform feedforward neural networks for automatic speech recognition (ASR). However, their training requires a lot of tuning and experience. In this work, we provide a comprehensive overview over various BLSTM training aspects and their interplay within ASR, which has been missing so far in the literature. We investigate on different variants of optimization methods, batching, truncated backpropagation, and regularization techniques such as dropout, and we study the effect of size and depth, training models of up to 10 layers. This includes a comparison of computation times vs. recognition performance. Furthermore, we introduce a pretraining scheme for LSTMs with layer-wise construction of the network showing good improvements especially for deep networks. The experimental analysis mainly was performed on the Quaero task, with additional results on Switchboard. The best BLSTM model gave a relative improvement in word error rate of over 15% compared to our best feed-forward baseline on our Quaero 50h task. All experiments were done using RETURNN and RASR, RWTH's extensible training framework for universal recurrent neural networks and ASR toolkit. The training configuration files are publicly available.

关键词： acoustic modeling LSTM RNN acoustic modeling Speech recognition recurrent neural nets aquifer storage and recovery Functional training feedforward neural nets Bidirectional

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EM-style optimization of hidden conditional random fields for grapheme-to-phoneme conversion

EM-style optimization of hidden conditional random fields fo...

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

作者： Georg Heigold Stefan Hahn Patrick Lehnen Hermann Ney Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen Germany

We have recently proposed an EM-style algorithm to optimize log-linear models with hidden variables. In this paper, we use this algorithm to optimize a hidden conditional random field, i.e., a conditional random field with hidden variables. Similar to hidden Markov models, the alignments are the hidden variables in the examples considered. Here, EM-style algorithms are iterative optimization algorithms which are guaranteed to improve the training criterion in each iteration without the need for tuning step sizes, sophisticated update schemes or numerical line optimization (with hardly predictable complexity). This is a rather strong property which conventional gradient-based optimization algorithms do not have. We present experimental results for a grapheme-to-phoneme conversion task and compare the convergence behavior of the EM-style algorithm with L-BFGS and Rprop.

关键词： Optimization Training Geographic Information Systems Mathematical model Convergence Error analysis Equations

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The Varied Impacts of Energy Storage and Photovoltaics on Fossil Fuel Emissions

The Varied Impacts of Energy Storage and Photovoltaics on Fo...

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IEEE Power and Energy Society General Meeting

作者： Emily Barrett Brandon Thayer Karen Studarus Seemita Pal Pattern Recognition and Human Language Technology Research Center Universitat Politecnica de Valencia Valencia Spain

ISBN: (纸本)9781538622131;9781538622124

The emissions consequences of smart grid technologies can be significant but are not always intuitive. This is particularly true in the implementation of energy storage (ES) to enable the installation of solar photovoltaic (PV) systems. Using the web-based calculator at and prototypical distribution feeders, this paper explores the CO_2, SO_2 and NO_x impacts of ES deployed with solar PV, where the energy storage system is operated to minimize load variation assuming hourly dispatch. Five regions of the country were explored using 15 prototypical distribution feeders and 2015 historical data. Impacts vary in direction, magnitude, and trend, and require a context-dependent screening method for faithful representation.

关键词： Meteorology Load modeling Fossil fuels Energy storage Smart grids Data models Photovoltaic systems

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Incorporating alignments into Conditional Random Fields for grapheme to phoneme conversion

Incorporating alignments into Conditional Random Fields for ...

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

作者： Patrick Lehnen Stefan Hahn Andreas Guta Hermann Ney Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen Germany

Conditional Random Fields (CRFs) are a state-of-the-art approach to natural language processing tasks like grapheme-to phoneme (g2p) conversion which is used to produce pronunciations or pronunciation variants for almost all ASR pronunciation lexica. One drawback of CRFs is that for training, an alignment is needed between graphemes and phonemes, usually even 1-to-l. The quality of the g2p result heavily depends on this alignment. Since these alignments are usually not annotated within the corpora, external models have to be used to produce such an alignment in a preprocessing step. In this work, we propose two approaches to integrate the alignment generation directly and efficiently into the CRF training process. Whereas the first approach relies on linear segmentation as starting point, the second approach considers all possible alignments given certain constraints. Both methods have been evaluated on two English g2p tasks, namely NETtalk and Celex, on which state-of-the-art results have been reported in the literature. The proposed approaches lead to results comparable to the state-of-the art.

关键词： Training Joints Hidden Markov models Biological system modeling Manuals Automata Mathematical model

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Incorporating On-demand Stereo for Real Time Recognition

Incorporating On-demand Stereo for Real Time Recognition

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Conference on Computer Vision and pattern Recognition (CVPR)

作者： T. Deselaers A. Criminisi J. Winn A. Agarwal Microsoft Research Limited Cambridge UK Human Language Technology and Pattern Recognition RWTH Aachen University of Technology Germany

A new method for localising and recognising hand poses and objects in real-time is presented. This problem is important in vision-driven applications where it is natural for a user to combine hand gestures and real objects when interacting with a machine. Examples include using a real eraser to remove words from a document displayed on an electronic surface. In this paper the task of simultaneously recognising object classes, hand gestures and detecting touch events is cast as a single classification problem. A random forest algorithm is employed which adaptively selects and combines a minimal set of appearance, shape and stereo features to achieve maximum class discrimination for a given image. This minimal set leads to both efficiency at run time and good generalisation. Unlike previous stereo works which explicitly construct disparity maps, here the stereo matching costs are used directly as visual cue and only computed on-demand, i.e. only for pixels where they are necessary for recognition. This leads to improved efficiency. The proposed method is assessed on a database of a variety of objects and hand poses selected for interacting on a flat surface in an office environment.

关键词： Object detection pattern recognition Shape Cameras humans Event detection Costs Visual databases Hardware Object recognition

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Silence is golden: Modeling non-speech events in WFST-based dynamic network decoders

Silence is golden: Modeling non-speech events in WFST-based ...

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

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

Models for silence are a fundamental part of continuous speech recognition systems. Depending on application requirements, audio data segmentation, and availability of detailed training data annotations, it may be necessary or beneficial to differentiate between other non-speech events, for example breath and background noise. The integration of multiple non-speech models in a WFST-based dynamic network decoder is not straightforward, because these models do not perfectly fit in the transducer framework. This paper describes several options for the transducer construction with multiple non-speech models, shows their considerable different characteristics in memory and runtime efficiency, and analyzes the impact on the recognition performance.

关键词： Hidden Markov models Transducers Decoding Context Speech Noise Speech recognition

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