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检索条件"机构=Pattern Recognition and Human Language"

共 401 条记录，以下是281-290 订阅

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Open Vocabulary Arabic Handwriting recognition Using Morphological Decomposition

Open Vocabulary Arabic Handwriting Recognition Using Morphol...

引用

International Conference on Document Analysis and recognition

作者： Mahdi Hamdani Amr El-Desoky Mousa Hermann Ney Human Language Technology and Pattern Recognition Group RWTH Aachen University Germany Rheinisch-Westfalische Technische Hochschule Aachen Aachen Nordrhein-Westfalen DE

ISBN: (纸本)9781479901937

The use of language Models (LMs) is a very important component in large and open vocabulary recognition systems. This paper presents an open-vocabulary approach for Arabic handwriting recognition. The proposed approach makes use of Arabic word decomposition based on morphological analysis. The vocabulary is a combination of words and sub-words obtained by the decomposition process. Out Of Vocabulary (OOV) words can be recognized by combining different elements from the lexicon. The recognition system is based on Hidden Markov Models (HMMs) with position and context dependent character models. An n-gram LM trained on the decomposed text is used along with the HMMs during the search. The approach is evaluated using two Arabic handwriting datasets. The open vocabulary approach leads to a significant improvement in the system performance. Two different types experiments for two Arabic handwriting recognition tasks are conducted in this work. The proposed approach for open vocabulary allows to have an absolute improvement of up to 1% in the Word Error Rate (WER) for the constrained task and to keep the same performance of the baseline system for the unconstrained one.

关键词： Vocabulary Hidden Markov models Handwriting recognition Training Feature extraction Context Character recognition

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May the force be with you: Force-aligned signwriting for automatic subunit annotation of corpora

May the force be with you: Force-aligned signwriting for aut...

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International Conference on Automatic Face and Gesture recognition

作者： Oscar Koller Hermann Ney Richard Bowden Centre for Vision Speech and Signal Processing University of Surrey Guildford UK Human Language Technology and Pattern Recognition Group RWTH Aachen University Germany

ISBN: (纸本)9781467355452

We propose a method to generate linguistically meaningful subunits in a fully automated fashion for sign language corpora. The ability to automate the process of subunit annotation has profound effects on the data available for training sign language recognition systems. The approach is based on the idea that subunits are shared among different signs. With sufficient data and knowledge of possible signing variants, accurate automatic subunit sequences are produced, matching the specific characteristics of given sign language data. Specifically we demonstrate how an iterative forced alignment algorithm can be used to transfer the knowledge of a user-edited open sign language dictionary to the task of annotating a challenging, large vocabulary, multi-signer corpus recorded from public TV. Existing approaches focus on labour intensive manual subunit annotations or on data-driven approaches. Our method yields an average precision and recall of 15% under the maximum achievable accuracy with little user intervention beyond providing a simple word gloss.

关键词： Assistive technology Gesture recognition Dictionaries Hidden Markov models Databases Manuals Vocabulary

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Deciphering foreign language by combining language models and context vectors

Deciphering foreign language by combining language models an...

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50th Annual Meeting of the Association for Computational Linguistics, ACL 2012

作者： Nuhn, Malte Mauser, Arne Ney, Hermann Human Language Technology and Pattern Recognition Group RWTH Aachen University Germany

ISBN: (纸本)9781937284244

In this paper we show how to train statistical machine translation systems on reallife tasks using only non-parallel monolingual data from two languages. We present a modification of the method shown in (Ravi and Knight, 2011) that is scalable to vocabulary sizes of several thousand words. On the task shown in (Ravi and Knight, 2011) we obtain better results with only 5% of the computational effort when running our method with an n-gram language model. The efficiency improvement of our method allows us to run experiments with vocabulary sizes of around 5,000 words, such as a non-parallel version of the VERBMOBIL corpus. We also report results using data from the monolingual French and English GIGAWORD corpora. © 2012 Association for computational Linguistics.

关键词： Computational linguistics

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Phrase Model Training for Statistical Machine Translation with Word Lattices of Preprocessing Alternatives 7

Phrase Model Training for Statistical Machine Translation wi...

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7th Workshop on Statistical Machine Translation, WMT 2012, immediately following the Conference of the North-American Chapter of the Association for Computational Linguistics - human language Technologies, NAACL HLT 2012

作者： Wuebker, Joern Ney, Hermann Human Language Technology And Pattern Recognition Group Rwth Aachen University Aachen Germany

ISBN: (纸本)9781937284206

In statistical machine translation, word lattices are used to represent the ambiguities in the preprocessing of the source sentence, such as word segmentation for Chinese or morphological analysis for German. Several approaches have been proposed to define the probability of different paths through the lattice with external tools like word segmenters, or by applying indicator features. We introduce a novel lattice design, which explicitly distinguishes between different preprocessing alternatives for the source sentence. It allows us to make use of specific features for each preprocessing type and to lexicalize the choice of lattice path directly in the phrase translation model. We argue that forced alignment training can be used to learn lattice path and phrase translation model simultaneously. On the newscommentary portion of the German!English WMT 2011 task we can show moderate improvements of up to 0.6% BLEU over a stateof- the-art baseline system. ©2012 Association for Computational Linguistics.

关键词： Computer aided language translation

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Tracking benchmark databases for video-based sign language recognition

Tracking benchmark databases for video-based sign language r...

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11th European Conference on Computer Vision, ECCV 2010

作者： Dreuw, Philippe Forster, Jens Ney, Hermann Human Language Technology and Pattern Recognition Group RWTH Aachen University Aachen Germany

A survey of video databases that can be used within a continuous sign language recognition scenario to measure the performance of head and hand tracking algorithms either w.r.t. a tracking error rate or w.r.t. a word error rate criterion is presented in this work. Robust tracking algorithms are required as the signing hand frequently moves in front of the face, may temporarily disappear, or cross the other hand. Only few studies consider the recognition of continuous sign language, and usually special devices such as colored gloves or blue-boxing environments are used to accurately track the regions-of-interest in sign language processing. Ground-truth labels for hand and head positions have been annotated for more than 30k frames in several publicly available video databases of different degrees of difficulty, and preliminary tracking results are presented. © 2012 Springer-Verlag.

关键词： Database systems

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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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Spoken language Translation Using Automatically Transcribed Text in Training 9

Spoken Language Translation Using Automatically Transcribed ...

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9th International Workshop on Spoken language Translation, IWSLT 2012

作者： Peitz, Stephan Wiesler, Simon Nußbaum-Thom, Markus Ney, Hermann Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen Germany

In spoken language translation a machine translation system takes speech as input and translates it into another language. A standard machine translation system is trained on written language data and expects written language as input. In this paper we propose an approach to close the gap between the output of automatic speech recognition and the input of machine translation by training the translation system on automatically transcribed speech. In our experiments we show improvements of up to 0.9 BLEU points on the IWSLT 2012 English-to-French speech translation task. © IWSLT 2012. All rights reserved.

关键词： Speech recognition

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Hierarchical Hybrid language models for Open Vocabulary Continuous Speech recognition using WFST

Hierarchical Hybrid Language models for Open Vocabulary Cont...

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2012 SAPA-SCALE Conference

作者： Shaik, M. Ali Basha Rybach, David Hahn, Stefan Schlüter, Ralf Ney, Hermann Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen52056 Germany

One of the main challenges in automatic speech recognition is recognizing an open, partly unseen vocabulary. To implicitly reduce the out-of-vocabulary (OOV) rate, hybrid vocabularies consisting of full-words and sub-words are used. Nevertheless, when using sub-words, OOV rates are not necessarily zero. In this work, we propose the use of separate character level graphones (orthography and phoneme sequence pair) as sub-words to effectively obtain zero OOV rate. To minimize negative effects on the core vocabulary of the most frequent words, a hierarchical language modeling approach is proposed. We augment the first level hybrid language model with an OOV word class, which is replaced by character level graphone sequences using a second-level graphone based character language and acoustic model during search. This approach is realized on-the-fly using weighted finite state transducers. We recognize a significant fraction of OOVs on the Wall Street Journal corpus, compared to the full-word and former hybrid language model based approaches. © SAPA-SCALE 2012. All rights reserved.

关键词： Modeling languages

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

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

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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

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

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

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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

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