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Spoken language processing techniques for sign language recognition and translation

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

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

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

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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

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Visual modeling and feature adaptation in sign language recognition

Visual modeling and feature adaptation in sign language reco...

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8. ITG-Fachtagung Sprachkommunikation 2008 - 8th ITG Conference on Speech Communication 2008

作者： Dreuw, Philippe Ney, Hermann Human Language Technology and Pattern Recognition RWTH Aachen University AachenD-52056 Germany

ISBN: (纸本)9783800731206

We propose a tracking adaptation to recover from early tracking errors in sign language recognition by optimizing the obtained tracking paths w.r.t. the hypothesized word sequences of an automatic sign language recognition system. Hand or head tracking is usually only optimized according to a tracking criterion. As a consequence, methods which depend on accurate detection and tracking of body parts lead to recognition errors in gesture and sign language processing. Similar to speaker dependent feature adaptation methods in automatic speech recognition, we propose an automatic visual alignment of signers for vision-based sign language recognition. Furthermore, the generation of additional virtual training samples is proposed to reduce the lack of data problem in sign language processing, which often leads to "one-shot" trained models. Most state-of-the-art systems are speaker dependent, and consider tracking as a preprocessing feature extraction part. Experiments on a publicly available benchmark database show that the proposed methods strongly improve the recognition accuracy of the system. © Sprachkommunikation 2008 - 8. ITG-Fachtagung.

关键词： Modeling languages

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Comparison of Different Neural Network Architectures for Spoken language Identification 15

Comparison of Different Neural Network Architectures for Spo...

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15th ITG Conference on Speech Communication

作者： Bazazo, Tala Zeineldeen, Mohammad Plahl, Christian Schlüter, Ralf Ney, Hermann Human Language Technology and Pattern Recognition RWTH Aachen University Germany eBay Aachen Germany

ISBN: (纸本)9783800761654

This paper compares different neural network based architectures on the spoken language identification task. To our best knowledge such a comparison of different models on the same dataset and the same set of languages does not yet exist. We incorporate 7 different models which include the latest architectures: a spectral images based Resnet model, a Convolutional Neural Network, a Bi-directional Long Short-Term Memory, a Convolutional Recurrent Neural Network, Wav2Vec 2.0, a transformer and a conformer. We also tackle audio with background noise and music by training on data with similar accoustics. We finally also show that our models generalize well on third-party data. © VDE VERLAG GMBH Berlin Offenbach.

关键词： Recurrent neural networks

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Modeling Punctuation Prediction as Machine Translation 8

Modeling Punctuation Prediction as Machine Translation

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8th International Workshop on Spoken language Translation, IWSLT 2011

作者： Peitz, Stephan Freitag, Markus Mauser, Arne Ney, Hermann Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen Germany

Punctuation prediction is an important task in Spoken language Translation. The output of speech recognition systems does not typically contain punctuation marks. In this paper we analyze different methods for punctuation prediction and show improvements in the quality of the final translation output. In our experiments we compare the different approaches and show improvements of up to 0.8 BLEU points on the IWSLT 2011 English French Speech Translation of Talks task using a translation system to translate from unpunctuated to punctuated text instead of a language model based punctuation prediction method. Furthermore, we do a system combination of the hypotheses of all our different approaches and get an additional improvement of 0.4 points in BLEU. © IWSLT 2011. All rights reserved.

关键词： Forecasting

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Translation model based weighting for phrase extraction 17

Translation model based weighting for phrase extraction

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17th Annual Conference of the European Association for Machine Translation, EAMT 2014

作者： Mansour, Saab Ney, Hermann Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen Germany

ISBN: (纸本)9789535537533

Domain adaptation for statistical machine translation is the task of altering general models to improve performance on the test domain. In this work, we suggest several novel weighting schemes based on translation models for adapted phrase extraction. To calculate the weights, we first phrase align the general bilingual training data, then, using domain specific translation models, the aligned data is scored and weights are defined over these scores. Experiments are performed on two translation tasks, German-to-English and Arabic-to-English translation with lectures as the target domain. Different weighting schemes based on translation models are compared, and significant improvements over automatic translation quality are reported. In addition, we compare our work to previous methods for adaptation and show significant gains. © 2014 The authors.

关键词： Extraction

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MorphTagger: HMM-Based Arabic Segmentation for Statistical Machine Translation 7

MorphTagger: HMM-Based Arabic Segmentation for Statistical M...

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7th International Workshop on Spoken language Translation, IWSLT 2010

作者： Mansour, Saab Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen Germany

In this paper, we investigate different methodologies of Arabic segmentation for statistical machine translation by comparing a rule-based segmenter to different statistically-based segmenters. We also present a new method for segmentation that serves the need for a real-time translation system without impairing the translation accuracy. © IWSLT 2010. All rights reserved.

关键词： Computer aided language translation

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A Simple and Effective Weighted Phrase Extraction for Machine Translation Adaptation 9

A Simple and Effective Weighted Phrase Extraction for Machin...

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9th International Workshop on Spoken language Translation, IWSLT 2012

作者： Mansour, Saab Ney, Hermann Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen Germany

The task of domain-adaptation attempts to exploit data mainly drawn from one domain (e.g. news) to maximize the performance on the test domain (e.g. weblogs). In previous work, weighting the training instances was used for filtering dissimilar data. We extend this by incorporating the weights directly into the standard phrase training procedure of statistical machine translation (SMT). This allows the SMT system to make the decision whether to use a phrase translation pair or not, a more methodological way than discarding phrase pairs completely when using filtering. Furthermore, we suggest a combined filtering and weighting procedure to achieve better results while reducing the phrase table size. The proposed methods are evaluated in the context of Arabic-to-English translation on various conditions, where significant improvements are reported when using the suggested weighted phrase training. The weighting method also improves over filtering, and the combined filtering and weighting is better than a standalone filtering method. Finally, we experiment with mixture modeling, where additional improvements are reported when using weighted phrase extraction over a variety of baselines. © IWSLT 2012. All rights reserved.

关键词： Machine translation

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A neural, interactive-predictive system for multimodal sequence to sequence tasks 57

A neural, interactive-predictive system for multimodal seque...

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57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, ACL 2019

作者： Peris, Álvaro Casacuberta, Francisco Pattern Recognition and Human Language Technology Research Center Universitat Politcnica de Valncia Valncia Spain

ISBN: (纸本)9781950737499

We present a demonstration of a neural interactive-predictive system for tackling mul- timodal sequence to sequence tasks. The sys- tem generates text predictions to different se- quence to sequence tasks: machine translation, image and video captioning. These predictions are revised by a human agent, who introduces corrections in the form of characters. The sys- tem reacts to each correction, providing alter- native hypotheses, compelling with the feed- back provided by the user. The final objective is to reduce the human effort required during this correction process. This system is implemented following a client-server architecture. For accessing the system, we developed a website, which com- municates with the neural model, hosted in a local server. From this website, the dif- ferent tasks can be tackled following the interactive-predictive framework. We open- source all the code developed for building this system. The demonstration in hosted in http://***/ interactive-seq2seq. © 2019 All rights reserved.

关键词： Websites

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Combining Translation and language Model Scoring for Domain-Specific Data Filtering 8

Combining Translation and Language Model Scoring for Domain-...

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8th International Workshop on Spoken language Translation, IWSLT 2011

作者： Mansour, Saab Wuebker, Joern Ney, Hermann Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen Germany

The increasing popularity of statistical machine translation (SMT) systems is introducing new domains of translation that need to be tackled. As many resources are already available, domain adaptation methods can be applied to utilize these recourses in the most beneficial way for the new domain. We explore adaptation via filtering, using the cross-entropy scores to discard irrelevant sentences. We focus on filtering for two important components of an SMT system, namely the language model (LM) and the translation model (TM). Previous work has already applied LM cross-entropy based scoring for filtering. We argue that LM cross-entropy might be appropriate for LM filtering, but not as much for TM filtering. We develop a novel filtering approach based on a combined TM and LM cross-entropy scores. We experiment with two large-scale translation tasks, the Arabic-to-English and English-to-French IWSLT 2011 TED Talks MT tasks. For LM filtering, we achieve strong perplexity improvements which carry over to the translation quality with improvements up to +0.4% BLEU. For TM filtering, the combined method achieves small but consistent improvements over the standalone methods. As a side effect of adaptation via filtering, the fully fledged SMT system vocabulary size and phrase table size are reduced by a factor of at least 2 while up to +0.6% BLEU improvement is observed. © IWSLT 2011. All rights reserved.

关键词： Computer aided language translation

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