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Workshop on Statistical Machine Translation

作者： Joern Wuebker Mei-Yuh Hwang Chris Quirk Human Language Technology and Pattern Recognition Group RWTH Aachen University Germany Microsoft Corporation Redmond WA USA

ISBN: (纸本)9781622765928

Training the phrase table by force-aligning (FA) the training data with the reference translation has been shown to improve the phrasal translation quality while significantly reducing the phrase table size on medium sized tasks. We apply this procedure to several large-scale tasks, with the primary goal of reducing model sizes without sacrificing translation quality. To deal with the noise in the automatically crawled parallel training data, we introduce on-demand word deletions, insertions, and backoffs to achieve over 99% successful alignment rate. We also add heuristics to avoid any increase in OOV rates. We are able to reduce already heavily pruned baseline phrase tables by more than 50% with little to no degradation in quality and occasionally slight improvement, without any increase in OOVs. We further introduce two global scaling factors for re-estimation of the phrase table via posterior phrase alignment probabilities and a modified absolute discounting method that can be applied to fractional counts.

关键词： reduced mass Model trains Heuristics Tables

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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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Combining handwriting and speech recognition for transcribing historical handwritten documents

Combining handwriting and speech recognition for transcribin...

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International Conference on Document Analysis and Recognition

作者： Emilio Granell Carlos-D. Martínez-Hinarejos Pattern Recognition and Human Language Technology Research Center Universitat Politècnica de València Valencia Spain

Transcription of historical documents is an interesting task for libraries in order to make available their funds. In the lasts years, the use of Handwritten Text Recognition allowed paleographs to speed up the manual transcription process, since they are able to correct on a draft transcription. Another alternative is obtaining the draft transcription by dictating the contents to an Automatic Speech Recognition system. When both sources (image and speech) are available, a multimodal combination is possible, and an iterative process can be used in order to refine the final hypothesis. In this work, a multimodal combination based on confusion networks is presented. Results on two different sets of data, with different difficulty level, show that the proposed technique provides similar or better draft transcriptions than a previously proposed approach, allowing for a faster transcription process.

关键词： Iterative decoding Acoustics Proposals Laplace equations Integrated optics Optical imaging

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A comparison of sequence-trained deep neural networks and recurrent neural networks optical modeling for handwriting recognition

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2014年 8791卷 199-219页

作者： Bluche, Théodore Ney, Hermann Kermorvant, Christopher A2iA SA Paris France Spoken Language Processing Group LIMSI CNRS Orsay France Human Language Technology and Pattern Recognition RWTH Aachen University Aachen Germany

Long Short-Term Memory Recurrent Neural Networks are the current state-of-the-art in handwriting recognition. In speech recognition, Deep Multi-Layer Perceptrons (DeepMLPs) have become the standard acoustic model for Hidden Markov Models (HMMs). Although handwriting and speech recognition systems tend to include similar components and techniques, DeepMLPs are not used as optical model in unconstrained large vocabulary handwriting recognition. In this paper, we compare Bidirectional LSTM-RNNs with DeepMLPs for this task. We carried out experiments on two public databases of multi-line handwritten documents: Rimes and IAM. We show that the proposed hybrid systems yield performance comparable to the state-of-the-art, regardless of the type of features (hand-crafted or pixel values) and the neural network optical model (DeepMLP or RNN). © Springer International Publishing Switzerland 2014.

关键词： Deep neural networks

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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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Improved Robustness to Disfluencies in Rnn-Transducer Based Speech Recognition

Improved Robustness to Disfluencies in Rnn-Transducer Based ...

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

作者： Valentin Mendelev Tina Raissi Guglielmo Camporese Manuel Giollo Amazon Alexa Human Language Technology and Pattern Recognition Group RWTH Aachen University Germany University of Padova Italy

Automatic Speech Recognition (ASR) based on Recurrent Neural Network Transducers (RNN-T) is gaining interest in the speech community. We investigate data selection and preparation choices aiming for improved robustness of RNN-T ASR to speech disfluencies with a focus on partial words. For evaluation we use clean data, data with disfluen- cies and a separate dataset with speech affected by stuttering. We show that after including a small amount of data with disfluencies in the training set the recognition accuracy on the tests with disfluencies and stuttering improves. Increasing the amount of training data with disfluencies gives additional gains without degradation on the clean data. We also show that replacing partial words with a dedicated token helps to get even better accuracy on utterances with disfluencies and stutter. The evaluation of our best model shows 22.5% and 16.4% relative WER reduction on those two evaluation sets.

关键词： Training Degradation Transducers Recurrent neural networks Training data Speech recognition Signal processing

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Improvements in Dynamic Programming Beam Search for Phrase-based Statistical Machine Translation 5

Improvements in Dynamic Programming Beam Search for Phrase-b...

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5th International Workshop on Spoken language Translation, IWSLT 2008

作者： Zens, Richard Ney, Hermann Human Language Technology and Pattern Recognition Lehrstuhl für Informatik 6 Computer Science Department RWTH Aachen University AachenD-52056 Germany Google Inc. 1600 Am-phitheatre Parkway Mountain ViewCA94043 United States

Search is a central component of any statistical machine translation system. We describe the search for phrase-based SMT in detail and show its importance for achieving good translation quality. We introduce an explicit distinction between reordering and lexical hypotheses and organize the pruning accordingly. We show that for the large Chinese-English NIST task already a small number of lexical alternatives is sufficient, whereas a large number of reordering hypotheses is required to achieve good translation quality. The resulting system compares favorably with the current state-of-the-art, in particular we perform a comparison with cube pruning as well as with Moses. © 2008 International Workshop on Spoken language Translation, IWSLT 2008. All rights reserved.

关键词： Dynamic programming

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ICFHR2014 Competition on Handwritten Text Recognition on Transcriptorium Datasets (HTRtS)

ICFHR2014 Competition on Handwritten Text Recognition on Tra...

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International Workshop on Frontiers in Handwriting Recognition

作者： Joan Andreu Sánchez Verónica Romero Alejandro H. Toselli Enrique Vidal Pattern Recognition and Human Language Technology Research Center Universitat Politècnica de València València Spain

A contest on Handwritten Text Recognition organised in the context of the ICFHR 2014 conference is described. Two tracks with increased freedom on the use of training data were proposed and three research groups participated in these two tracks. The handwritten images for this contest were drawn from an English data set which is currently being considered in the Tran scriptorium project. The goal of this project is to develop innovative, efficient and cost-effective solutions for the transcription of historical handwritten document images, focusing on four languages: English, Spanish, German and Dutch. For the English language, the so-called "Bentham collection" is being considered in Tran scriptorium. It encompasses a large set of manuscripts written by the renowned English philosopher and reformer Jeremy Bentham (1748-1832). A small subset of this collection has been chosen for the present HTR competition. The selected subset has been written by several hands (Bentham himself and his secretaries) and entails significant variabilities and difficulties regarding the quality of text images and writing styles. Training and test data were provided in the form of carefully segmented line images, along with the corresponding transcripts. The three participants achieved very good results, with transcription word error rates ranging from 15.0% down to 8.6%.

关键词： Training Hidden Markov models Histograms Artificial neural networks Text recognition Training data Adaptive optics

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Towards two-dimensional sequence to sequence model in neural machine translation

arXiv

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arXiv 2018年

作者： Bahar, Parnia Brix, Christopher Ney, Hermann Human Language Technology and Pattern Recognition Group Computer Science Department Rwth Aachen University AachenD-52056 Germany

This work investigates an alternative model for neural machine translation (NMT) and proposes a novel architecture, where we employ a multi-dimensional long short-term memory (MDLSTM) for translation modeling. In the state-of-the-art methods, source and target sentences are treated as one-dimensional sequences over time, while we view translation as a two-dimensional (2D) mapping using an MDLSTM layer to define the correspondence between source and target words. We extend beyond the current sequence to sequence backbone NMT models to a 2D structure in which the source and target sentences are aligned with each other in a 2D grid. Our proposed topology shows consistent improvements over attention-based sequence to sequence model on two WMT 2017 tasks, German↔English. Copyright © 2018, The Authors. All rights reserved.

关键词： Neural machine translation

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Does Joint Training Really Help Cascaded Speech Translation?

arXiv

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arXiv 2022年

作者： Tran, Viet Anh Khoa Thulke, David Gao, Yingbo Herold, Christian Ney, Hermann Human Language Technology and Pattern Recognition Group Computer Science Department RWTH Aachen University AachenD-52056 Germany

Currently, in speech translation, the straightforward approach - cascading a recognition system with a translation system - delivers state-of-the-art results. However, fundamental challenges such as error propagation from the automatic speech recognition system still remain. To mitigate these problems, recently, people turn their attention to direct data and propose various joint training methods. In this work, we seek to answer the question of whether joint training really helps cascaded speech translation. We review recent papers on the topic and also investigate a joint training criterion by marginalizing the transcription posterior probabilities. Our findings show that a strong cascaded baseline can diminish any improvements obtained using joint training, and we suggest alternatives to joint training. We hope this work can serve as a refresher of the current speech translation landscape, and motivate research in finding more efficient and creative ways to utilize the direct data for speech translation. Copyright © 2022, The Authors. All rights reserved.

关键词： Speech recognition

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