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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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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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A comparative analysis of dynamic network decoding

A comparative analysis of dynamic network decoding

引用

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

ISBN: (纸本)9781457705380

The use of statically compiled search networks for ASR systems using huge vocabularies and complex language models often becomes challenging in terms of memory requirements. Dynamic network decoders introduce additional computations in favor of significantly lower memory consumption. In this paper we investigate the properties of two well-known search strategies for dynamic network decoding, namely history conditioned tree search and WFST-based search using dynamic transducer composition. We analyze the impact of the differences in search graph representation, search space structure, and language model look-ahead techniques. Experiments on an LVCSR task illustrate the influence of the compared properties.

关键词： Decoding Transducers Hidden Markov models History Speech recognition Context Vocabulary

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Powerful extensions to CRFS for grapheme to phoneme conversion

Powerful extensions to CRFS for grapheme to phoneme conversi...

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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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Hierarchical hybrid MLP/HMM or rather MLP features for a discriminatively trained Gaussian HMM: A comparison for offline handwriting recognition

Hierarchical hybrid MLP/HMM or rather MLP features for a dis...

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IEEE International Conference on Image Processing

作者： Philippe Dreuw Patrick Doetsch Christian Plahl Hermann Ney Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen Germany

We use neural network based features extracted by a hierarchical multilayer-perceptron (MLP) network either in a hybrid MLP/HMM approach or to discriminatively retrain a Gaussian hidden Markov model (GHMM) system in a tandem approach. MLP networks have been successfully used to model long-term and non-linear features dependencies in automatic speech and optical character recognition. In offline hand writing recognition, MLPs have been mostly used for isolated character and word recognition in hybrid approaches. Here we analyze MLPs within an LVCSR framework for continuous handwriting recognition using discriminative MMI/MPE training. Especially hybrid MLP/HMM and discriminatively retrained MLP-GHMM tandem approaches are evaluated. Significant improvements and competitive results are re ported for a closed-vocabulary task on the IfN/ENIT Arabic handwriting database and for a large-vocabulary task using the IAM English handwriting database.

关键词： Hidden Markov models Handwriting recognition Training Databases Feature extraction Text recognition Character recognition

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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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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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Non-stationary feature extraction for automatic speech recognition

Non-stationary feature extraction for automatic speech recog...

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

作者： Zoltán Tüske Pavel Golik Ralf Schlüter Friedhelm R. Drepper Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen Germany Zentralinstitut für Elektronik Forschungszentrum Jülich (KFA) Julich Germany

In current speech recognition systems mainly Short-Time Fourier Transform based features like MFCC are applied. Dropping the short-time stationarity assumption of the voiced speech, this paper introduces the non-stationary signal analysis into the ASR framework. We present new acoustic features extracted by a pitch-adaptive Gammatone filter bank. The noise robustness was proved on AURORA 2 and 4 tasks, where the proposed features outperform the standard MFCC. Furthermore, successful combination experiments via ROVER indicate the differences between the new features and MFCC.

关键词： Mel frequency cepstral coefficient Harmonic analysis Feature extraction Speech Speech recognition Time frequency analysis

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Warp that smile on your face: Optimal and smooth deformations for face recognition

Warp that smile on your face: Optimal and smooth deformation...

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International Conference on Automatic Face and Gesture recognition

作者： Tobias Gass Leonid Pishchulin Philippe Dreuw Hermann Ney Human Language Technology and Pattern Recognition Group RWTH Aachen University Germany Computer Vision Laboratory ETH Zurich Switzerland Computer Vision and Multimodal Computing MPI Informatics Saarbruecken Germany

In this work, we present novel warping algorithms for full 2D pixel-grid deformations for face recognition. Due to high variation in face appearance, face recognition is considered a very difficult task, especially if only a single reference image, for example a mug-shot, per face is available. Usually model-based approaches with additional training data are used to cope with several types of variation occurring in facial imaging. Image warping contrarily yields a distance measure which is invariant with regard to several types of variation. This allows for precise recognition even using only very few reference observations. Due to the computationally complex problem of optimal 2D warping, pseudo-2D warping-based approaches in the past represented strong approximations of the original problem, and were mainly successful on data with low variability or rectified images. We propose a novel 2D warping method which is globally optimal and makes no prior assumtions on the data variability besides two-dimensional smootheness constraints which both avoid local mirroring and gaps and significantly speed up the optimization. Furthermore, we show that occlusion handling is imperative to obtain smooth warpings in a variety of domains. We evaluate our novel algorithm on various well known databases, such as the AR-Face and CMU-PIE database, and provide a detailed comparison to existing warping approaches. We show that by using simple relative 2D constraints, strong local features and a kernel, which is robust w.r.t. occlusions, our computationally complex approaches outperform state-of-the-art results for recognizing faces under varying expressions, occlusions and poses. Most interestingly, we achieve higher accuracy using fewer training instances per class compared to methods learning a model of the 3D shape.

关键词： Pixel Face Face recognition Optimization Databases Approximation methods Hidden Markov models

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