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Speeding up 2D-warping for pose-invariant face recognition

Speeding up 2D-warping for pose-invariant face recognition

引用

International Conference on Automatic Face and Gesture recognition

作者： Harald Hanselmann Hermann Ney Human Language Technology and Pattern Recognition Group RWTH Aachen University Germany

Recently, state-of-the-art recognition accuracies for pose-invariant face recognition have been achieved by using 2D-Warping methods in a nearest-neighbor framework. However, the main drawback of these methods is the high computational complexity. In this paper we address this issue. We use a simple and fast method to get a rough estimate of a 2D-Warping. This estimate can then be used to apply an image dependent warprange on the 2D-Warping algorithm, limit the possible poses or preselect the most likely classes. By this method we are able significantly reduce the runtime of a recently proposed 2D-Warping algorithm without sacrificing recognition accuracy.

关键词： Face recognition Accuracy Runtime Databases Complexity theory Manuals Face

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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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The Rwth Asr System for Ted-Lium Release 2: Improving Hybrid Hmm With Specaugment

The Rwth Asr System for Ted-Lium Release 2: Improving Hybrid...

引用

IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Wei Zhou Wilfried Michel Kazuki Irie Markus Kitza Ralf Schluter Hermann Ney Human Language Technology and Pattern Recognition RWTH Aachen University Aachen Germany

ISBN: (数字)9781509066315

ISBN: (纸本)9781509066322

We present a complete training pipeline to build a state-of-the-art hybrid HMM-based ASR system on the 2nd release of the TED-LIUM corpus. Data augmentation using SpecAugment is successfully applied to improve performance on top of our best SAT model using i-vectors. By investigating the effect of different maskings, we achieve improvements from SpecAugment on hybrid HMM models without increasing model size and training time. A subsequent sMBR training is applied to fine-tune the final acoustic model, and both LSTM and Transformer language models are trained and evaluated. Our best system achieves a 5.6% WER on the test set, which outperforms the previous state-of-the-art by 27% relative.

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The RWTH Large Vocabulary Arabic Handwriting recognition System

The RWTH Large Vocabulary Arabic Handwriting Recognition Sys...

引用

IAPR International Workshop on Document Analysis Systems, DAS

作者： Mahdi Hamdani Patrick Doetsch Michal Kozielski Amr El-Desoky Mousa Hermann Ney Human Language Technology and Pattern Recognition Group RWTH Aachen University Germany

This paper describes the RWTH system for large vocabulary Arabic handwriting recognition. The recognizer is based on Hidden Markov Models (HMMs) with state of the art methods for visual/language modeling and decoding. The feature extraction is based on Recurrent Neural Networks (RNNs) which estimate the posterior distribution over the character labels for each observation. Discriminative training using the Minimum Phone Error (MPE) criterion is used to train the HMMs. The recognition is done with the help of n-gram language Models (LMs) trained using in-domain text data. Unsupervised writer adaptation is also performed using the Constrained Maximum Likelihood Linear Regression (CMLLR) feature adaptation. The RWTH Arabic handwriting recognition system gave competitive results in previous handwriting recognition competitions. The used techniques allows to improve the performance of the system participating in the OpenHaRT 2013 evaluation.

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

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Using morpheme and syllable based sub-words for polish LVCSR

Using morpheme and syllable based sub-words for polish LVCSR

引用

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

作者： M. Ali Basha Shaik Amr El-Desoky Mousa Ralf Schlüter Hermann Ney Human Language Technology and Pattern Recognition RWTH Aachen University Aachen Germany

Polish is a synthetic language with a high morpheme-per-word ratio. It makes use of a high degree of inflection leading to high out-of-vocabulary (OOV) rates, and high language Model (LM) perplexities. This poses a challenge for Large Vocabulary and Continuous Speech recognition (LVCSR) systems. Here, the use of morpheme and syllable based units is investigated for building sub-lexical LMs. A different type of sub-lexical units is proposed based on combining morphemic or syllabic units with corresponding pronunciations. Thereby, a set of grapheme-phoneme pairs called graphones are used for building LMs. A relative reduction of 3.5% in Word Error Rate (WER) is obtained with respect to a traditional system based on full-words.

关键词： Vocabulary Speech recognition Speech Adaptation models Computational modeling Error analysis Joints

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Speaker adaptive joint training of Gaussian mixture models and bottleneck features

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

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

Confidence-Based Discriminative Training for Model Adaptatio...

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

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

引用

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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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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