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

作者： Zijian Yang Wei Zhou Ralf Schlüter Hermann Ney Computer Science Department Human Language Technology and Pattern Recognition RWTH Aachen University Aachen Germany AppTek GmbH Aachen Germany

Recently, RNN-Transducers have achieved remarkable results on various automatic speech recognition tasks. However, lattice-free sequence discriminative training methods, which obtain superior performance in hybrid models, are rarely investigated in RNN-Transducers. In this work, we propose three lattice-free training objectives, namely lattice-free maximum mutual information, lattice-free segment-level minimum Bayes risk, and lattice-free minimum Bayes risk, which are used for the final posterior output of the phoneme-based neural transducer with a limited context dependency. Compared to criteria using N-best lists, lattice-free methods eliminate the decoding step for hypotheses generation during training, which leads to more efficient training. Experimental results show that lattice-free methods gain up to 6.5% relative improvement in word error rate compared to a sequence-level cross-entropy trained model. Compared to the N-best-list based minimum Bayes risk objectives, lattice-free methods gain 40% - 70% relative training time speedup with a small degradation in performance.

关键词： Training Degradation Transducers Error analysis Signal processing Decoding Speech processing

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On the Relation Between Internal language Model and Sequence Discriminative Training for Neural Transducers

On the Relation Between Internal Language Model and Sequence...

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

Internal language model (ILM) subtraction has been widely applied to improve the performance of the RNN-Transducer with external language model (LM) fusion for speech recognition. In this work, we show that sequence discriminative training has a strong correlation with ILM subtraction from both theoretical and empirical points of view. Theoretically, we derive that the global optimum of maximum mutual information (MMI) training shares a similar formula as ILM subtraction. Empirically, we show that ILM subtraction and sequence discriminative training achieve similar effects across a wide range of experiments on Librispeech, including both MMI and minimum Bayes risk (MBR) criteria, as well as neural transducers and LMs of both full and limited context. The benefit of ILM subtraction also becomes much smaller after sequence discriminative training. We also provide an indepth study to show that sequence discriminative training has a minimal effect on the commonly used zero-encoder ILM estimation, but a joint effect on both encoder and prediction + joint network for posterior probability reshaping including both ILM and blank suppression.

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Why does CTC result in peaky behavior?

arXiv

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arXiv 2021年

作者： Zeyer, Albert Schlüter, Ralf Ney, Hermann Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen Germany AppTek GmbH Aachen Germany

The peaky behavior of CTC models is well known experimentally. However, an understanding about why peaky behavior occurs is missing, and whether this is a good property. We provide a formal analysis of the peaky behavior and gradient descent convergence properties of the CTC loss and related training criteria. Our analysis provides a deep understanding why peaky behavior occurs and when it is suboptimal. On a simple example which should be trivial to learn for any model, we prove that a feed-forward neural network trained with CTC from uniform initialization converges towards peaky behavior with a 100% error rate. Our analysis further explains why CTC only works well together with the blank label. We further demonstrate that peaky behavior does not occur on other related losses including a label prior model, and that this improves convergence. © 2021, CC BY-SA.

关键词： Gradient methods

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One decade of statistical machine translation: 1996-2005

One decade of statistical machine translation: 1996-2005

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IEEE Workshop on Automatic Speech recognition and Understanding

作者： H. Ney Human Language Technology and Pattern Recognition Lehrstuhl fur Informatik VI-Computer Science Department RWTH Aachen University Aachen Germany

In the last decade, the statistical approach has found widespread use in machine translation both for written and spoken language and has had a major impact on the translation accuracy. The goal of this paper is to cover the state of the art in statistical machine translation. We would re-visit the underlying principles of the statistical approach to machine translation and summarize the progress that has been made over the last decade

关键词： Natural languages Context modeling Hidden Markov models Speech humans pattern recognition computer science Transducers Tides Vocabulary

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Efficient Utilization of Large Pre-Trained Models for Low Resource ASR

Efficient Utilization of Large Pre-Trained Models for Low Re...

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Acoustics, Speech, and Signal Processing Workshops (ICASSPW), IEEE International Conference on

作者： Peter Vieting Christoph Lüscher Julian Dierkes Ralf Schlüter Hermann Ney Computer Science Department Human Language Technology and Pattern Recognition Group RWTH Aachen University Aachen Germany AppTek GmbH Aachen Germany

Unsupervised representation learning has recently helped automatic speech recognition (ASR) to tackle tasks with limited labeled data. Following this, hardware limitations and applications give rise to the question how to take advantage of large pre-trained models efficiently and reduce their complexity. In this work, we study a challenging low resource conversational telephony speech corpus from the medical domain in Vietnamese and German. We show the benefits of using unsupervised techniques beyond simple fine-tuning of large pre-trained models, discuss how to adapt them to a practical telephony task including bandwidth transfer and investigate different data conditions for pre-training and fine-tuning. We outperform the project baselines by 22% relative using pre-training techniques. Further gains of 29% can be achieved by refinements of architecture and training and 6% by adding 0.8 h of in-domain adaptation data.

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THE RWTH ENGLISH LECTURE recognition SYSTEM

THE RWTH ENGLISH LECTURE RECOGNITION SYSTEM

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Simon Wiesler Kazuki Irie Zoltan Tuske Ralf Schluter Hermann Ney Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen Germany Ecole Centrale Paris Paris France

ISBN: (纸本)9781479928941

In this paper, we describe the RWTH speech recognition system for English lectures developed within the Translectures project. A difficulty in the development of an English lectures recognition system, is the high ratio of non-native speakers. We address this problem by using very effective deep bottleneck features trained on multilingual data. The acoustic model is trained on large amounts of data from different domains and with different dialects. Large improvements are obtained from unsupervised acoustic adaptation. Another challenge is the frequent use of technical terms and the wide range of topics. In our recognition system, slides, which are attached to most lectures, are used for improving lexical coverage and language model adaptation.

关键词： Lectures (teaching method) recognition system modelling languages speech recognition system English English language Acoustic models

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Acoustic feature combination for robust speech recognition

Acoustic feature combination for robust speech recognition

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

作者： A. Zolnay R. Schluter H. Ney Human Language Technology and Pattern Recognition Lehrstuhl für Informatik VI Computer Science Department RWTH Aachen University Aachen Germany

In this paper, we consider the use of multiple acoustic features of the speech signal for robust speech recognition. We investigate the combination of various auditory based (mel frequency cepstrum coefficients, perceptual linear prediction, etc.) and articulatory based (voicedness) features. Features are combined by linear discriminant analysis and log-linear model combination based techniques. We describe the two feature combination techniques and compare the experimental results. Experiments performed on the large-vocabulary task VerbMobil II (German conversational speech) show that the accuracy of automatic speech recognition systems can be improved by the combination of different acoustic features.

关键词： Robustness Speech recognition Mel frequency cepstral coefficient Linear discriminant analysis Cepstrum Feature extraction Automatic speech recognition Autocorrelation Cepstral analysis Concatenated codes

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The RWTH Arabic-to-English spoken language translation system

The RWTH Arabic-to-English spoken language translation syste...

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IEEE Workshop on Automatic Speech recognition and Understanding

作者： Oliver Bender Evgeny Matusov Stefan Hahn Sasa Hasan Shahram Khadivi Hermann Ney Human Language Technology and Pattern Recognition Lehrstuhl für Informatik 6-Computer Science Department RWTH Aachen University Aachen Germany

ISBN: (纸本)9781424413690;1424413699

We present the RWTH phrase-based statistical machine translation system designed for the translation of Arabic speech into English text. This system was used in the Global Autonomous language Exploitation (GALE) Go/No-Go Translation Evaluation 2007. Using a two-pass approach, we first generate n-best translation candidates and then rerank these candidates using additional models. We give a short review of the decoder as well as of the models used in both passes. We stress the difficulties of spoken language translation, i.e. how to combine the recognition and translation systems and how to compensate for missing punctuation. In addition, we cover our work on domain adaptation for the applied language models. We present translation results for the official GALE 2006 evaluation set and the GALE 2007 development set.

关键词： Natural languages Automatic speech recognition Surface-mount technology Vocabulary Decoding Speech analysis Broadcasting humans pattern recognition computer science

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COMPARISON OF FEEDFORWARD AND RECURRENT NEURAL NETWORK language MODELS

COMPARISON OF FEEDFORWARD AND RECURRENT NEURAL NETWORK LANGU...

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IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： M. Sundermeyer I. Oparin J.-L. Gauvain B. Freiberg R. Schluter H. Ney Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen Germany Spoken Language Processing Group LIMSI CNRS Paris France

ISBN: (纸本)9781479903573

Research on language modeling for speech recognition has increasingly focused on the application of neural networks. Two competing concepts have been developed: On the one hand, feedforward neural networks representing an ngram approach, on the other hand recurrent neural networks that may learn context dependencies spanning more than a fixed number of predecessor words. To the best of our knowledge, no comparison has been carried out between feedforward and state-of-the-art recurrent networks when applied to speech recognition. This paper analyzes this aspect in detail on a well-tuned French speech recognition task. In addition, we propose a simple and efficient method to normalize language model probabilities across different vocabularies, and we show how to speed up training of recurrent neural networks by parallelization.

关键词： Automatic speech recognition feedforward neural networks recurrent neural networks Speech recognition recurrent neural nets feedforward neural nets FEEDFORWARD modelling languages Neural network parallelization

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Efficient Training of Neural Transducer for Speech recognition

arXiv

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arXiv 2022年

作者： Zhou, Wei Michel, Wilfried Schlüter, Ralf Ney, Hermann Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen52074 Germany AppTek GmbH Aachen52062 Germany

As one of the most popular sequence-to-sequence modeling approaches for speech recognition, the RNN-Transducer has achieved evolving performance with more and more sophisticated neural network models of growing size and increasing training epochs. While strong computation resources seem to be the prerequisite of training superior models, we try to overcome it by carefully designing a more efficient training pipeline. In this work, we propose an efficient 3-stage progressive training pipeline to build highly-performing neural transducer models from scratch with very limited computation resources in a reasonable short time period. The effectiveness of each stage is experimentally verified on both Librispeech and Switchboard corpora. The proposed pipeline is able to train transducer models approaching state-of-the-art performance with a single GPU in just 2-3 weeks. Our best conformer transducer achieves 4.1% WER on Librispeech test-other with only 35 epochs of training. Copyright © 2022, The Authors. All rights reserved.

关键词： Speech recognition

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