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

共 383 条记录，以下是221-230 订阅

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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...

引用

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

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

引用

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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Phase difference of filter-stable part-tones as acoustic feature

Phase difference of filter-stable part-tones as acoustic fea...

引用

IEEE/SP Workshop on Statistical Signal Processing (SSP)

作者： Zoltán Tüske Friedhelm R. Drepper Ralf Schlüter Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen Germany

A part-tone decomposition of voiced sections of speech is introduced, which is adapted with high accuracy to the frequency of the glottal oscillator of the speaker. The iterative replacement of the center filter frequency contours (chosen locally as linear chirp) of the non-stationary bandpass filters converges extremely fast and leads to the extraction of filter-stable part-tones with uncorrupted phases. In contrast to phases of frequency decomposition with a priori defined, constant filter frequencies, the phase differences of filter-stable part-tones promise to become a useful supplement of the amplitude based acoustic features used for conventional automatic speech recognition. The derived phase features are tested in vowel classification experiments based on the phonetically rich TIMIT database.

关键词： Speech Harmonic analysis Equations Time frequency analysis Mel frequency cepstral coefficient Speech processing

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Faster sequence training

Faster sequence training

引用

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

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

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IEEE International Conference on Acoustics, Speech and Signal Processing

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

ISBN: (纸本)9781509041183

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.

关键词： recurrent neural networks lstm rnn speech recognition software package multi-gpu recurrent neural nets Software packages Speech recognition EXPANDABLE research purposes Frameworks Functional training

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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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Feature selection for log-linear acoustic models

Feature selection for log-linear acoustic models

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

作者： S. Wiesler A. Richard Y. Kubo R. Schlüter H. Ney Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen Germany

ISBN: (纸本)9781457705380

Log-linear acoustic models have been shown to be competitive with Gaussian mixture models in speech recognition. Their high training time can be reduced by feature selection. We compare a simple univariate feature selection algorithm with ReliefF - an efficient multivariate algorithm. An alternative to feature selection is ℓ 1 -regularized training, which leads to sparse models. We observe that this gives no speedup when sparse features are used, hence feature selection methods are preferable. For dense features, ℓ 1 -regularization can reduce training and recognition time. We generalize the well known Rprop algorithm for the optimization of ℓ 1 -regularized functions. Experiments on the Wall Street Journal corpus showed that a large number of sparse features could be discarded without loss of performance. A strong regularization led to slight performance degradations, but can be useful on large tasks, where training the full model is not tractable.

关键词： Training Hidden Markov models Acoustics Speech recognition Polynomials Optimization Complexity theory

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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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RASR/NN: THE RWTH NEURAL NETWORK TOOLKIT FOR SPEECH recognition

RASR/NN: THE RWTH NEURAL NETWORK TOOLKIT FOR SPEECH RECOGNIT...

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IEEE International Conference on Acoustics, Speech and Signal Processing

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

ISBN: (纸本)9781479928941

This paper describes the new release of RASR - the open source version of the well-proven speech recognition toolkit developed and used at RWTH Aachen University. The focus is put on the implementation of the NN module for training neural network acoustic models. We describe code design, configuration, and features of the NN module. The key feature is a high flexibility regarding the network topology, choice of activation functions, training criteria, and optimization algorithm, as well as a built-in support for efficient GPU computing. The evaluation of run-time performance and recognition accuracy is performed exemplary with a deep neural network as acoustic model in a hybrid NN/HMM system. The results show that RASR achieves a state-of-the-art performance on a real-world large vocabulary task, while offering a complete pipeline for building and applying large scale speech recognition systems.

关键词： Speech recognition Acoustic models Neural network runtime performance Optimization algorithms speech recognition system Buildings

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