检索结果-内蒙古大学图书馆

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

作者： Robin Schmitt Albert Zeyer Mohammad Zeineldeen Ralf Schlűter Hermann Ney Computer Science Department Human Language Technology and Pattern Recognition RWTH Aachen University Aachen Germany AppTek GmbH Aachen Germany

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

We sometimes observe monotonically decreasing cross-attention weights in our Conformer-based global attention-based encoder-decoder (AED) models, negatively affecting performance compared to monotonically increasing attention weights. Further investigation shows that the Conformer encoder reverses the sequence in the time dimension. We analyze the initial behavior of the decoder cross-attention mechanism and find that it encourages the Conformer encoder self-attention to build a connection between the initial frames and all other informative frames. Furthermore, we show that, at some point in training, the self-attention module of the Conformer starts dominating the output over the preceding feed-forward module, which then only allows the reversed information to pass through. We propose methods and ideas of how this flipping can be avoided and investigate a novel method to obtain label-frame-position alignments by using the gradients of the label log probabilities w.r.t. the encoder input frames.

关键词： Training Signal processing Acoustics Decoding Speech processing

来源：评论

学校读者我要写书评

暂无评论

Transformer-based direct hidden Markov model for machine translation 59

Transformer-based direct hidden Markov model for machine tra...

引用

2021 Student Research Workshop, SRW 2021 at the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural language Processing, ACL-IJCNLP 2021

作者： Wang, Weiyue Yang, Zijian Gao, Yingbo Ney, Hermann Human Language Technology and Pattern Recognition Group Computer Science Department RWTH Aachen University Germany

ISBN: (纸本)9781954085558

The neural hidden Markov model has been proposed as an alternative to attention mechanism in machine translation with recurrent neural networks. However, since the introduction of the transformer models, its performance has been surpassed. This work proposes to introduce the concept of the hidden Markov model to the transformer architecture, which outperforms the transformer baseline. Interestingly, we find that the zero-order model already provides promising performance, giving it an edge compared to a model with first-order dependency, which performs similarly but is significantly slower in training and decoding. © 2021 Association for Computational Linguistics.

关键词： Hidden Markov models

来源：评论

学校读者我要写书评

暂无评论

Extending statistical machine translation with discriminative and trigger-based lexicon models

Extending statistical machine translation with discriminativ...

引用

2009 Conference on Empirical Methods in Natural language Processing, EMNLP 2009, Held in Conjunction with ACL-IJCNLP 2009

作者： Mauser, Arne Hasan, SǍa Ney, Hermann Human Language Technology and Pattern Recognition Group Department of Computer Science 6 RWTH Aachen University Germany

In this work, we propose two extensions of standard word lexicons in statistical machine translation: A discriminative word lexicon that uses sentence-level source information to predict the target words and a trigger-based lexicon model that extends IBM model 1 with a second trigger, allowing for a more fine-grained lexical choice of target words. The models capture dependencies that go beyond the scope of conventional SMT models such as phraseand language models. We show that the models improve translation quality by 1% in BLEU over a competitive baseline on a large-scale task. © 2009 ACL and AFNLP.

关键词： computer aided language translation

来源：评论

学校读者我要写书评

暂无评论

The RWTH Aachen Speech recognition and Machine Translation System for IWSLT 2012 9

The RWTH Aachen Speech Recognition and Machine Translation S...

引用

9th International Workshop on Spoken language Translation, IWSLT 2012

作者： Peitz, Stephan Mansour, Saab Freitag, Markus Feng, Minwei Huck, Matthias Wuebker, Joern Nuhn, Malte Nußbaum-Thom, Markus Ney, Hermann Human Language Technology and Pattern Recognition Group Computer Science Department RWTH Aachen University Aachen Germany

In this paper, the automatic speech recognition (ASR) and statistical machine translation (SMT) systems of RWTH Aachen University developed for the evaluation campaign of the International Workshop on Spoken language Translation (IWSLT) 2012 are presented. We participated in the ASR (English), MT (English-French, Arabic-English, Chinese-English, German-English) and SLT (English-French) tracks. For the MT track both hierarchical and phrase-based SMT decoders are applied. A number of different techniques are evaluated in the MT and SLT tracks, including domain adaptation via data selection, translation model interpolation, phrase training for hierarchical and phrase-based systems, additional reordering model, word class language model, various Arabic and Chinese segmentation methods, postprocessing of speech recognition output with an SMT system, and system combination. By application of these methods we can show considerable improvements over the respective baseline systems. © IWSLT 2012. All rights reserved.

关键词： Speech recognition

来源：评论

学校读者我要写书评

暂无评论

The RWTH Aachen Machine Translation System for IWSLT 2011 8

The RWTH Aachen Machine Translation System for IWSLT 2011

引用

8th International Workshop on Spoken language Translation, IWSLT 2011

作者： Wuebker, Joern Huck, Matthias Mansour, Saab Freitag, Markus Feng, Minwei Peitz, Stephan Schmidt, Christoph Ney, Hermann Human Language Technology and Pattern Recognition Group Computer Science Department RWTH Aachen University Aachen Germany

In this paper the statistical machine translation (SMT) systems of RWTH Aachen University developed for the evaluation campaign of the International Workshop on Spoken language Translation (IWSLT) 2011 is presented. We participated in the MT (English-French, Arabic-English, Chinese-English) and SLT (English-French) tracks. Both hierarchical and phrase-based SMT decoders are applied. A number of different techniques are evaluated, including domain adaptation via monolingual and bilingual data selection, phrase training, different lexical smoothing methods, additional reordering models for the hierarchical system, various Arabic and Chinese segmentation methods, punctuation prediction for speech recognition output, and system combination. By application of these methods we can show considerable improvements over the respective baseline systems. © IWSLT 2011. All rights reserved.

关键词： Hierarchical systems

来源：评论

学校读者我要写书评

暂无评论

Sequence Labeling-based Reordering Model for Phrase-based SMT 9

Sequence Labeling-based Reordering Model for Phrase-based SM...

引用

9th International Workshop on Spoken language Translation, IWSLT 2012

作者： Feng, Minwei Peter, Jan-Thorsten Ney, Hermann Human Language Technology and Pattern Recognition Group Computer Science Department RWTH Aachen University Aachen Germany

For current statistical machine translation system, reordering is still a major problem for language pairs like Chinese-English, where the source and target language have significant word order differences. In this paper, we propose a novel reordering model based on sequence labeling techniques. Our model converts the reordering problem into a sequence labeling problem, i.e. a tagging task. For the given source sentence, we assign each source token a label which contains the reordering information for that token. We also design an unaligned word tag so that the unaligned word phenomenon is automatically implanted in the proposed model. Our reordering model is conditioned on the whole source sentence. Hence it is able to catch the long dependency in the source sentence. Although the learning on large scale task requests notably amounts of computational resources, the decoder makes use of the tagging information as soft constraints. Therefore, the training procedure of our model is computationally expensive for large task while in the test phase (during translation) our model is very efficient. We carried out experiments on five Chinese-English NIST tasks trained with BOLT data. Results show that our model improves the baseline system by 1.32 BLEU 1.53 TER on average. © IWSLT 2012. All rights reserved.

关键词： computer aided language translation

来源：评论

学校读者我要写书评

暂无评论

Improving Continuous Sign language recognition: Speech recognition Techniques and System Design 4

Improving Continuous Sign Language Recognition: Speech Recog...

引用

4th Workshop on Speech and language Processing for Assistive Technologies, SLPAT 2013

作者： Forster, Jens Koller, Oscar Oberdorfer, Christian Gweth, Yannick Ney, Hermann Human Language Technology and Pattern Recognition Group Computer Science Department RWTH Aachen University Aachen Germany

ISBN: (纸本)9781937284930

Automatic sign language recognition (ASLR) is a special case of automatic speech recognition (ASR) and computer vision (CV) and is currently evolving from using artificial labgenerated data to using 'real-life' data. Although ASLR still struggles with feature extraction, it can benefit from techniques developed for ASR. We present a large-vocabulary ASLR system that is able to recognize sentences in continuous sign language and uses features extracted from standard single-view video cameras without using additional equipment. ASR techniques such as the multi-layer-perceptron (MLP) tandem approach, speaker adaptation, pronunciation modelling, and parallel hidden Markov models are investigated. We evaluate the influence of each system component on the recognition performance. On two publicly available large vocabulary databases representing lab-data (25 signer, 455 sign vocabulary, 19k sentence) and unconstrained 'real-life' sign language (1 signer, 266 sign vocabulary, 351 sentences) we can achieve 22.1% respectively 38.6% WER. © 2013 Association for Computational Linguistics.

关键词： Vocabulary control

来源：评论

学校读者我要写书评

暂无评论

Spoken language processing techniques for sign language recognition and translation

引用

technology and Disability 2008年第2期20卷 121-133页

作者： Dreuw, Philippe Stein, Daniel Deselaers, Thomas Rybach, David Zahedi, Morteza Bungeroth, Jan Ney, Hermann Human Language Technology and Pattern Recognition Computer Science Department 6 RWTH Aachen University Aachen Germany

We present an approach to automatically recognize sign language and translate it into a spoken language. A system to address these tasks is created based on state-of-the-art techniques from statistical machine translation, speech recognition, and image processing research. Such a system is necessary for communication between deaf and hearing people. The communication is otherwise nearly impossible due to missing sign language skills on the hearing side, and the low reading and writing skills on the deaf side. As opposed to most current approaches, which focus on the recognition of isolated signs only, we present a system that recognizes complete sentences in sign language. Similar to speech recognition, we have to deal with temporal sequences. Instead of the acoustic signal in speech recognition, we process a video signal as input. Therefore, we use a speech recognition system to obtain a textual representation of the signed sentences. This intermediate representation is then fed into a statistical machine translation system to create a translation into a spoken language. To achieve good results, some particularities of sign languages are considered in both systems. We use a publicly available corpus to show the performance of the proposed system and report very promising results. © 2008 IOS Press. All rights reserved.

关键词： computer input-output equipment SIGN language SYMBOLIC communication MEANS of communication for deaf people SPEECH perception AUTOMATIC speech recognition

来源：评论

学校读者我要写书评

暂无评论

The RWTH 2010 Quaero ASR evaluation system for English, French, and German

The RWTH 2010 Quaero ASR evaluation system for English, Fren...

引用

36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011

作者： Sundermeyer, M. Nussbaum-Thom, M. Wiesler, S. Plahl, C. El-Desoky Mousa, A. Hahn, S. Nolden, D. Schlüter, R. Ney, H. Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Germany

ISBN: (纸本)9781457705397

关键词： Speech recognition

来源：评论

学校读者我要写书评

暂无评论

The RWTH Aachen University English-Romanian Machine Translation System for WMT 2016 1

The RWTH Aachen University English-Romanian Machine Translat...

引用

1st Conference on Machine Translation, WMT 2016, held at the 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016

作者： Peter, Jan-Thorsten Alkhouli, Tamer Guta, Andreas Ney, Hermann Human Language Technology and Pattern Recognition Group Computer Science Department RWTH Aachen University AachenD-52056 Germany

ISBN: (纸本)9781945626104

This paper describes the statistical machine translation system developed at RWTH Aachen University for the English→Romanian translation task of the ACL 2016 First Conference on Machine Translation (WMT 2016). We combined three different state-of the- art systems in a system combination: A phrase-based system, a hierarchical phrase-based system and an attentionbased neural machine translation system. The phrase-based and the hierarchical phrase-based systems make use of a language model trained on all available data, a language model trained on the bilingual data and a word class language model. In addition, we utilized a recurrent neural network language model and a bidirectional recurrent neural network translation model for reranking the output of both systems. The attention-based neural machine translation system was trained using all bilingual data together with the backtranslated data from the News Crawl 2015 corpora. © 2016 Association for Computational Linguistics.

关键词： Neural machine translation

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：