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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Kazuki Irie Zhihong Lei Ralf Schlüter Hermann Ney Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University D-52056 Aachen Germany

ISBN: (纸本)9781538646595

Long short-term memory (LSTM) recurrent neural network language models compress the full context of variable lengths into a fixed size vector. In this work, we investigate the task of predicting the LSTM hidden representation of the full context from a truncated n-gram context as a subtask for training an n-gram feedforward language model. Since this approach is a form of knowledge distillation, we compare two methods. First, we investigate the standard transfer based on the Kullback-Leibler divergence of the output distribution of the feedforward model from that of the LSTM. Second, we minimize the mean squared error between the hidden state of the LSTM and that of the n-gram feedforward model. We carry out experiments on different subsets of the Switchboard speech recognition dataset for feedforward models with a short (5-gram) and a medium (10-gram) context length. We show that we get improvements in perplexity and word error rate of up to 8% and 4% relative for the medium model, while the improvements are only marginal for the short model.

关键词： language modeling neural networks knowledge distillation student-teacher speech recognition modelling languages Speech recognition FEEDFORWARD Neural network Error analysis

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INVESTIGATION ON CROSS- AND MULTILINGUAL MLP FEATURES UNDER MATCHED AND MISMATCHED ACOUSTICAL CONDITIONS

INVESTIGATION ON CROSS- AND MULTILINGUAL MLP FEATURES UNDER ...

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IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Zoltan Tuske Joel Pinto Daniel Willett Ralf Schluter Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Nuance Communications Deutschland GmbH

ISBN: (纸本)9781479903573

In this paper, Multi Layer Perceptron (MLP) based multilingual bottleneck features are investigated for acoustic modeling in three languages -- German, French, and US English. We use a modified training algorithm to handle the multilingual training scenario without having to explicitly map the phonemes to a common phoneme set. Furthermore, the cross-lingual portability of bottleneck features between the three languages are also investigated. Single pass recognition experiments on large vocabulary SMS dictation task indicate that (1) multilingual bottleneck features yield significantly lower word error rates compared to standard MFCC features (2) multilingual bottleneck features are superior to monolingual bottleneck features trained for the target language with limited training data, and (3) multilingual bottleneck features are beneficial in training acoustic models in a low resource language where only mismatched training data is available - by exploiting the more matched training data from other languages.

关键词： MLP Bottleneck Multilingual Mismatched acoustical condition Traffic Bottlenecks CSRP3 gene Multilingualism Object oriented programming languages language acoustic modeling Training data Phonemes Error analysis

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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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Bagging by design for continuous Handwriting recognition using multi-objective particle swarm optimization

Bagging by design for continuous Handwriting Recognition usi...

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International Conference on Document Analysis and recognition

作者： Mahdi Hamdani Patrick Doetsch Hermann Ney Human Language Technology and Pattern Recognition Group - RWTH Aachen University Germany Spoken Language Processing Group LIMSI CNRS Paris France

Multiple classifier systems are used to improve baseline results using different strategies. Bagging by design improves standard bagging by the minimization of intersection between the different ensembles. This work proposes the use of design bagging for continuous handwriting recognition. The design is performed using a multi-objective particle swarm optimizer. Hidden Markov Models and Long-Short Term Memory Recurrent Neural Networks are used to validate the proposed design. Experiments on English and French Handwriting recognition with different setups show significant improvements.

关键词： Lattices Optimization Optical character recognition software

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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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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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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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Fine-Grained Visual Classification with Efficient End-to-end Localization y 202

arXiv

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arXiv 2020年

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

The term fine-grained visual classification (FGVC) refers to classification tasks where the classes are very similar and the classification model needs to be able to find subtle differences to make the correct prediction. State-of-the-art approaches often include a localization step designed to help a classification network by localizing the relevant parts of the input images. However, this usually requires multiple iterations or passes through a full classification network or complex training schedules. In this work we present an efficient localization module that can be fused with a classification network in an end-to-end setup. On the one hand the module is trained by the gradient flowing back from the classification network. On the other hand, two self-supervised loss functions are introduced to increase the localization accuracy. We evaluate the new model on the three benchmark datasets CUB200-2011, Stanford Cars and FGVC-Aircraft and are able to achieve competitive recognition performance. Copyright © 2020, The Authors. All rights reserved.

关键词： Benchmarking

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