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arXiv 2019年

作者： Kim, Yunsu Gao, Yingbo Ney, Hermann Human Language Technology and Pattern Recognition Group Rwth Aachen University Aachen Germany

Transfer learning or multilingual model is essential for low-resource neural machine translation (NMT), but the applicability is limited to cognate languages by sharing their vocabularies. This paper shows effective techniques to transfer a pre-trained NMT model to a new, unrelated language without shared vocabularies. We relieve the vocabulary mismatch by using cross-lingual word embedding, train a more language-agnostic encoder by injecting artificial noises, and generate synthetic data easily from the pre-training data without back-translation. Our methods do not require restructuring the vocabulary or retraining the model. We improve plain NMT transfer by up to +5.1% BLEU in five low-resource translation tasks, outperforming multilingual joint training by a large margin. We also provide extensive ablation studies on pre-trained embedding, synthetic data, vocabulary size, and parameter freezing for a better understanding of NMT transfer. Copyright © 2019, The Authors. All rights reserved.

关键词： Neural machine translation

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Direct construction of compact context-dependency transducers from data

Direct construction of compact context-dependency transducer...

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作者： Rybach, David Riley, Michael Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Germany Google Inc. 76 Ninth Avenue New York NY United States

This paper describes a new method for building compact context-dependency transducers for finite-state transducer-based ASR decoders. Instead of the conventional phonetic decision-tree growing followed by FST compilation, this approach incorporates the phonetic context splitting directly into the transducer construction. The objective function of the split optimization is augmented with a regularization term that measures the number of transducer states introduced by a split. We give results on a large spoken-query task for various n-phone orders and other phonetic features that show this method can greatly reduce the size of the resulting context-dependency transducer with no significant impact on recognition accuracy. This permits using context sizes and features that might otherwise be unmanageable. © 2010 ISCA.

关键词： Transducers

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Investigations on byte-level convolutional neural networks for language modeling in low resource speech recognition

Investigations on byte-level convolutional neural networks f...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Kazuki Irie Pavel Golik Ralf Schluter Hermann Ney Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Germany

ISBN: (纸本)9781509041183

In this paper, we present an investigation on technical details of the byte-level convolutional layer which replaces the conventional linear word projection layer in the neural language model. In particular, we discuss and compare the effective filter configurations, pooling types and the use of bytes instead of characters. We carry out experiments on language packs released by the IARPA Babel project and measure the performance in terms of perplexity and word error rate. Introducing a convolutional layer consistently improves the results on all languages. Also, there is no degradation from using raw bytes instead of proper Unicode characters, even on syllabic alphabets like Amharic. In addition, we report improvements in word error rate from rescoring lattices and evaluate keyword search performance on several languages.

关键词： language modeling convolutional neural networks speech recognition keyword search modelling languages Speech recognition Key Search Error analysis language Amharic Byte Word

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Towards reinforcement learning for pivot-based neural machine translation with non-autoregressive transformer

arXiv

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arXiv 2021年

作者： Tokarchuk, Evgeniia Rosendahl, Jan Wang, Weiyue Petrushkov, Pavel Lancewicki, Tomer Khadivi, Shahram Ney, Hermann eBay Inc Human Language Technology and Pattern Recognition Group RWTH Aachen University Germany

Pivot-based neural machine translation (NMT) is commonly used in low-resource setups, especially for translation between non-English language pairs. It benefits from using high-resource source→pivot and pivot→target language pairs and an individual system is trained for both sub-tasks. However, these models have no connection during training, and the source→pivot model is not optimized to produce the best translation for the source→target task. In this work, we propose to train a pivot-based NMT system with the reinforcement learning (RL) approach, which has been investigated for various text generation tasks, including machine translation (MT). We utilize a non-autoregressive transformer and present an end-to-end pivot-based integrated model, enabling training on source→target data. Copyright © 2021, The Authors. All rights reserved.

关键词： Neural machine translation

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Audio segmentation for speech recognition using segment features

Audio segmentation for speech recognition using segment feat...

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

作者： David Rybach Christian Gollan Ralf Schluter Hermann Ney Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Germany

Audio segmentation is an essential preprocessing step in several audio processing applications with a significant impact e.g. on speech recognition performance. We introduce a novel framework which combines the advantages of different well known segmentation methods. An automatically estimated log-linear segment model is used to determine the segmentation of an audio stream in a holistic way by a maximum a posteriori decoding strategy, instead of classifying change points locally. A comparison to other segmentation techniques in terms of speech recognition performance is presented, showing a promising segmentation quality of our approach.

关键词： Speech recognition Streaming media Decoding Broadcasting Loudspeakers Automatic speech recognition Bayesian methods humans Natural languages pattern recognition

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Advances in Arabic broadcast news transcription at RWTH

Advances in Arabic broadcast news transcription at RWTH

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IEEE Workshop on Automatic Speech recognition and Understanding

作者： David Rybach Stefan Hahn Christian Gollan Ralf Schluter Hermann Ney Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Germany

ISBN: (纸本)9781424413690;1424413699

This paper describes the RWTH speech recognition system for Arabic. Several design aspects of the system, including cross-adaptation, multiple system design and combination, are analyzed. We summarize the semi-automatic lexicon generation for Arabic using a statistical approach to grapheme-to-phoneme conversion and pronunciation statistics. Furthermore, a novel ASR-based audio segmentation algorithm is presented. Finally, we discuss practical approaches for parallelized acoustic training and memory efficient lattice rescoring. Systematic results are reported on recent GALE evaluation corpora.

关键词： Broadcasting Mel frequency cepstral coefficient Hidden Markov models Neural networks Lattices Cepstral analysis Speech recognition humans Natural languages Loudspeakers

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Morpheme-based feature-rich language models using Deep Neural Networks for LVCSR of Egyptian Arabic

Morpheme-based feature-rich language models using Deep Neura...

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2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013

作者： El-Desoky Mousa, Amr Kuo, Hong-Kwang Jeff Mangu, Lidia Soltau, Hagen Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University 52056 Aachen Germany IBM T. J. Watson Research Center Yorktown Heights NY 10598 United States

ISBN: (纸本)9781479903566

Egyptian Arabic (EA) is a colloquial version of Arabic. It is a low-resource morphologically rich language that causes problems in Large Vocabulary Continuous Speech recognition (LVCSR). Building LMs on morpheme level is considered a better choice to achieve higher lexical coverage and better LM probabilities. Another approach is to utilize information from additional features such as morphological tags. On the other hand, LMs based on Neural Networks (NNs) with a single hidden layer have shown superiority over the conventional n-gram LMs. Recently, Deep Neural Networks (DNNs) with multiple hidden layers have achieved better performance in various tasks. In this paper, we explore the use of feature-rich DNN-LMs, where the inputs to the network are a mixture of words and morphemes along with their features. Significant Word Error Rate (WER) reductions are achieved compared to the traditional word-based LMs. © 2013 IEEE.

关键词： Deep neural networks

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A random forest approach for authorship profiling 16

A random forest approach for authorship profiling

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16th Conference and Labs of the Evaluation Forum, CLEF 2015

作者： Palomino-Garibay, Alonso Camacho-González, Adolfo T. Fierro-Villaneda, Ricardo A. Hernández-Farias, Irazú Buscaldi, Davide Meza-Ruiz, Ivan V. Ciudad de Mexico Mexico Ciudad de Mexico Mexico Pattern Recognition and Human Language Technology Universitat Politécnica de Valencia Valencia Spain Universite Paris 13 Sorbonne Paris Cité Villetaneuse France

In this paper we present our approach to extract profile information from anonymized tweets for the author profiling task at PAN 2015 [10]. Particularly we explore the versatility of random forest classifiers for the genre and age groups information and random forest regressions to score important aspects of the personality of a user. Furthermore we propose a set of features tailored for this task based on characteristics of the twitters. In particular, our approach relies on previous proposed features for sentiment analysis tasks.

关键词： Decision trees

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Demonstration of Joshua: An open source toolkit for parsing-based machine translation

Demonstration of Joshua: An open source toolkit for parsing-...

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Joint Conference of the 47th Annual Meeting of the Association for Computational Linguistics and 4th International Joint Conference on Natural language Processing of the AFNLP, ACL-IJCNLP 2009

作者： Li, Zhifei Callison-Burch, Chris Dyer, Chris Ganitkevitch, Juri Khudanpur, Sanjeev Schwartz, Lane Thornton, Wren N.G. Weese, Jonathan Zaidan, Omar F. Center for Language and Speech Processing Johns Hopkins University United States Computational Linguistics and Information Processing Lab. University of Maryland United States Human Language Technology and Pattern Recognition Group RWTH Aachen University Germany Natural Language Processing Lab. University of Minnesota United States

ISBN: (纸本)9781617382581

We describe Joshua (Li et al., 2009a)1, an open source toolkit for statistical machine translation. Joshua implements all of the algorithms required for translation via synchronous context free grammars (SCFGs): chart-parsing, n-gram language model integration, beam- and cubepruning, and k-best extraction. The toolkit also implements suffix-array grammar extraction and minimum error rate training. It uses parallel and distributed computing techniques for scalability. We also provide a demonstration outline for illustrating the toolkit's features to potential users, whether they be newcomers to the field or power users interested in extending the toolkit. © 2009 ACL and AFNLP.

关键词： Distributed computer systems

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Improvement of Context Dependent Modeling for Arabic Handwriting recognition

Improvement of Context Dependent Modeling for Arabic Handwri...

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International Workshop on Frontiers in Handwriting recognition

作者： Mahdi Hamdani Patrick Doetsch Hermann Ney Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen Germany

This paper proposes the improvement of context dependent modeling for Arabic handwriting recognition. Since the number of parameters in context dependent models is huge, CART trees are used for state tying. This work is based on a new set of questions for the CART tree construction based on a "lossy mapping" categorization of the Arabic shapes. The used system is a combination of Hidden Markov Models and Recurrent Neural Networks using the hybrid approach. A comparison between a Neural network trained using the baseline labels and another one based on the CART tree labels is done. The experimental results show that the use of the CART labels for the Neural Network training beneficial. The lossy mapping based CART tree performed better than the baseline system. An absolute improvement of 2.9% in terms of Word Error Rate is performed on the test set of the Open Hart database.

关键词： Hidden Markov models Context Handwriting recognition Context modeling Training Shape Speech recognition

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