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

共 397 条记录，以下是211-220 订阅

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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...

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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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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Moment-Based Image Normalization for Handwritten Text recognition

Moment-Based Image Normalization for Handwritten Text Recogn...

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International Workshop on Frontiers in Handwriting recognition

作者： Michal Kozielski Jens Forster Hermann Ney Human Language Technology and Pattern Recognition Group Chair of Computer Science 6 RWTH Aachen University Aachen Germany

In this paper, we extend the concept of moment-based normalization of images from digit recognition to the recognition of handwritten text. Image moments provide robust estimates for text characteristics such as size and position of words within an image. For handwriting recognition the normalization procedure is applied to image slices independently. Additionally, a novel moment-based algorithm for line-thickness normalization is presented. The proposed normalization methods are evaluated on the RIMES database of French handwriting and the IAM database of English handwriting. For RIMES we achieve an improvement from 16.7% word error rate to 13.4% and for IAM from 46.6% to 37.3%.

关键词： Hidden Markov models Databases Shape Image segmentation Vectors Handwriting recognition Error analysis

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Training language Models for Long-Span Cross-Sentence Evaluation

Training Language Models for Long-Span Cross-Sentence Evalua...

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IEEE Workshop on Automatic Speech recognition and Understanding

作者： Kazuki Irie Albert Zeyer Ralf Schlüter Hermann Ney Human Language Technology and Pattern Recognition Group RWTH Aachen University Aachen Germany AppTek GmbH Aachen Germany

ISBN: (数字)9781728103068

ISBN: (纸本)9781728103075

While recurrent neural networks can motivate cross-sentence language modeling and its application to automatic speech recognition (ASR), corresponding modifications of the training method for that end are rarely discussed. In fact, even more generally, the impact of training sequence construction strategy in language modeling for different evaluation conditions is typically ignored. In this work, we revisit this basic but fundamental question. We train language models based on long short-term memory recurrent neural networks and Transformers using various types of training sequences and study their robustness with respect to different evaluation modes. Our experiments on 300h Switchboard and Quaero English datasets show that models trained with back-propagation over sequences consisting of concatenation of multiple sentences with state carry-over across sequences effectively outperform those trained with the sentence-level training, both in terms of perplexity and word error rates for cross-utterance ASR.

关键词： Training Context modeling Computational modeling Recurrent neural networks Encoding Switches Standards

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On Architectures and Training for Raw Waveform Feature Extraction in ASR

On Architectures and Training for Raw Waveform Feature Extra...

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IEEE Workshop on Automatic Speech recognition and Understanding

作者： Peter Vieting Christoph Lüscher Wilfried Michel Ralf Schlüter Hermann Ney Human Language Technology and Pattern Recognition Group RWTH Aachen University Aachen Germany AppTek GmbH Aachen Germany

ISBN: (纸本)9781665437400

With the success of neural network based modeling in auto-matic speech recognition (ASR), many studies investigated acoustic modeling and learning of feature extractors directly based on the raw waveform. Recently, one line of research has focused on unsupervised pre-training of feature extractors on audio-only data to improve downstream ASR performance. In this work, we investigate the usefulness of one of these front-end frameworks, namely wav2vec, in a setting without additional untranscribed data for hybrid ASR systems. We compare this framework both to the manually defined stan-dard Gammatone feature set, as well as to features extracted as part of the acoustic model of an ASR system trained su-pervised. We study the benefits of using the pre-trained feature extractor and explore how to additionally exploit an ex-isting acoustic model trained with different features. Finally, we systematically examine combinations of the described features in order to further advance the performance.

关键词： Training Conferences Neural networks Feature extraction Acoustics Data mining Automatic speech recognition

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Tight Integrated End-to-End Training for Cascaded Speech Translation

Tight Integrated End-to-End Training for Cascaded Speech Tra...

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IEEE Spoken language technology Workshop

作者： Parnia Bahar Tobias Bieschke Ralf Schlüter Hermann Ney Human Language Technology and Pattern Recognition Group RWTH Aachen University Aachen Germany AppTek GmbH Aachen Germany

ISBN: (数字)9781728170664

ISBN: (纸本)9781728170671

A cascaded speech translation model relies on discrete and non-differentiable transcription, which provides a supervision signal from the source side and helps the transformation between source speech and target text. Such modeling suffers from error propagation between ASR and MT models. Direct speech translation is an alternative method to avoid error propagation; however, its performance is often behind the cascade system. To use an intermediate representation and preserve the end-to-end trainability, previous studies have proposed using two-stage models by passing the hidden vectors of the recognizer into the decoder of the MT model and ignoring the MT encoder. This work explores the feasibility of collapsing the entire cascade components into a single end-to-end trainable model by optimizing all parameters of ASR and MT models jointly without ignoring any learned parameters. It is a tightly integrated method that passes renormalized source word posterior distributions as a soft decision instead of one-hot vectors and enables backpropagation. Therefore, it provides both transcriptions and translations and achieves strong consistency between them. Our experiments on four tasks with different data scenarios show that the model outperforms cascade models up to 1.8% in BLEU and 2.0% in TER and is superior compared to direct models.

关键词： Training Adaptation models Correlation Training data Data models Decoding Task analysis

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Two-Way Neural Machine Translation: A Proof of Concept for Bidirectional Translation Modeling Using a Two-Dimensional Grid

Two-Way Neural Machine Translation: A Proof of Concept for B...

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IEEE Spoken language technology Workshop

作者： Parnia Bahar Christopher Brix Hermann Ney Human Language Technology and Pattern Recognition Group RWTH Aachen University Aachen Germany AppTek GmbH Aachen Germany

ISBN: (数字)9781728170664

ISBN: (纸本)9781728170671

Neural translation models have proven to be effective in capturing sufficient information from a source sentence and generating a high-quality target sentence. However, it is not easy to get the best effect for bidirectional translation, i.e., both source-to-target and target-to-source translation using a single model. If we exclude some pioneering attempts, such as multilingual systems, all other bidirectional translation approaches are required to train two individual models. This paper proposes to build a single end-to-end bidirectional translation model using a two-dimensional grid, where the left-to-right decoding generates source-to-target, and the bottom-to-up decoding creates target-to-source output. Instead of training two models independently, our approach encourages a single network to jointly learn to translate in both directions. Experiments on the WMT2018 German↔English and Turkish↔English translation tasks show that the proposed model is capable of generating a good translation quality and has sufficient potential to direct the research.

关键词： Training Conferences Decoding Machine translation Task analysis

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A Comparative Study on End-to-End Speech to Text Translation

A Comparative Study on End-to-End Speech to Text Translation

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IEEE Workshop on Automatic Speech recognition and Understanding

作者： Parnia Bahar Tobias Bieschke Hermann Ney AppTek GmbH Aachen Germany Human Language Technology and Pattern Recognition Group RWTH Aachen University Aachen Germany

ISBN: (数字)9781728103068

ISBN: (纸本)9781728103075

Recent advances in deep learning show that end-to-end speech to text translation model is a promising approach to direct the speech translation field. In this work, we provide an overview of different end-to-end architectures, as well as the usage of an auxiliary connectionist temporal classification (CTC) loss for better convergence. We also investigate on pre-training variants such as initializing different components of a model using pretrained models, and their impact on the final performance, which gives boosts up to 4% in Bleu and 5% in Ter. Our experiments are performed on 270h IWSLT TED-talks En→De, and 100h LibriSpeech Audio-books En→Fr. We also show improvements over the current end-to-end state-of-the-art systems on both tasks.

关键词： Decoding Task analysis Computational modeling Training Speech recognition Data models Feature extraction

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