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A comparative large scale study of MLP features for mandarin ASR

A comparative large scale study of MLP features for mandarin...

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作者： Valente, Fabio Doss, Mathew Magimai Plahl, Christian Ravuri, Suman Wang, Wen IDIAP Research Institute CH-1920 Martigny Switzerland Human Language Technology and Pattern Recognition RWTH Aachen University Germany International Computer Science Institute 1947 Center Street Berkeley CA 94704 United States Speech Technology and Research Laboratory SRI International Menlo Park CA United States

MLP based front-ends have shown significant complementary properties to conventional spectral features. As part of the DARPA GALE program, different MLP features were developed for Mandarin ASR. In this paper, all the proposed frontends are compared in systematic manner and we extensively investigate the scalability of these features in terms of the amount of training data (from 100 hours to 1600 hours) and system complexity (maximum likelihood training, SAT, lattice level combination, and discriminative training). Results on 5 hours of evaluation data from the GALE project reveal that the MLP features consistently produce relative improvements in the range of 15% - 23% at the different steps of a multipass system when compared to the conventional short-term spectral based features like MFCC and PLP. The largest improvement is obtained using a hierarchical MLP approach. © 2010 ISCA.

关键词： Maximum likelihood

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SURF-face: Face recognition under viewpoint consistency constraints

SURF-face: Face recognition under viewpoint consistency cons...

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2009 20th British Machine Vision Conference, BMVC 2009

作者： Dreuw, Philippe Steingrube, Pascal Hanselmann, Harald Ney, Hermann Human Language Technology and Pattern Recognition RWTH Aachen University Aachen Germany

ISBN: (纸本)1901725391

We analyze the usage of Speeded Up Robust Features (SURF) as local descriptors for face recognition. The effect of different feature extraction and viewpoint consistency constrained matching approaches are analyzed. Furthermore, a RANSAC based outlier removal for system combination is proposed. The proposed approach allows to match faces under partial occlusions, and even if they are not perfectly aligned or illuminated. Current approaches are sensitive to registration errors and usually rely on a very good initial alignment and illumination of the faces to be recognized. A grid-based and dense extraction of local features in combination with a block-based matching accounting for different viewpoint constraints is proposed, as interest-point based feature extraction approaches for face recognition often fail. The proposed SURF descriptors are compared to SIFT descriptors. Experimental results on the AR-Face and CMU-PIE database using manually aligned faces, unaligned faces, and partially occluded faces show that the proposed approach is robust and can outperform current generic approaches. © 2009. The copyright of this document resides with its authors.

关键词： Face recognition

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A Deep Learning Approach to Machine Transliteration 4

A Deep Learning Approach to Machine Transliteration

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4th Workshop on Statistical Machine Translation, WMT 2009, immediately preceding the 12th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2009

作者： Deselaers, Thomas Hasan, Sǎsa Bender, Oliver Ney, Hermann Human Language Technology And Pattern Recognition Group RWTH Aachen University Germany

In this paper we present a novel transliteration technique which is based on deep belief networks. Common approaches use finite state machines or other methods similar to conventional machine translation. Instead of using conventional NLP techniques, the approach presented here builds on deep belief networks, a technique which was shown to work well for other machine learning problems. We show that deep belief networks have certain properties which are very interesting for transliteration and possibly also for translation and that a combination with conventional techniques leads to an improvement over both components on an Arabic-English transliteration task. ©2009 Association for Computational Linguistics.

关键词： Deep learning

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Comparison of extended lexicon models in search and rescoring for SMT

Comparison of extended lexicon models in search and rescorin...

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2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics: human language Technologies, NAACL-HLT 2009

作者： Hasan, Saša Ney, Hermann Human Language Technology and Pattern Recognition Group RWTH Aachen University Germany

ISBN: (纸本)9781932432428

We show how the integration of an extended lexicon model into the decoder can improve translation performance. The model is based on lexical triggers that capture long-distance dependencies on the sentence level. The results are compared to variants of the model that are applied in reranking of n-best lists. We present how a combined application of these models in search and rescoring gives promising results. Experiments are reported on the GALE Chinese-English task with improvements of up to +0.9% BLEU and -1.5% TER absolute on a competitive baseline. © 2009 Association for Computational Linguistics

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Writer adaptive training and writing variant model refinement for offline Arabic handwriting recognition

Writer adaptive training and writing variant model refinemen...

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ICDAR2009 - 10th International Conference on Document Analysis and recognition

作者： Dreuw, Philippe Rybach, David Gollan, Christian Ney, Hermann RWTH Aachen University Human Language Technology and Pattern Recognition Ahornstr 55 D-52056 Aachen Germany

ISBN: (纸本)9780769537252

We present a writer adaptive training and writer clustering approach for an HMM based Arabic handwriting recognition system to handle different handwriting styles and their variations. Additionally, a writing variant model refinement for specific writing variants is proposed. Current approaches try to compensate the impact of different writing styles during preprocessing and normalization steps. Writer adaptive training with a CMLLR based feature adaptation is used to train writer dependent models. An unsupervised writer clustering with Bayesian information criterion based stopping condition for a CMLLR based feature adaptation during a two-pass decoding process is used to cluster different handwriting styles of unknown test writers. The proposed methods are evaluated on the IFN/ENIT Arabic handwriting database. © 2009 IEEE.

关键词： Character recognition

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

Extending statistical machine translation with discriminativ...

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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

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Confidence-based discriminative training for model adaptation in offline Arabic handwriting recognition

Confidence-based discriminative training for model adaptatio...

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ICDAR2009 - 10th International Conference on Document Analysis and recognition

作者： Dreuw, Philippe Heigold, Georg Ney, Hermann RWTH Aachen University Human Language Technology and Pattern Recognition Ahornstr 55 D-52056 Aachen Germany

ISBN: (纸本)9780769537252

We present a novel confidence-based discriminative training for model adaptation approach for an HMM based Arabic handwriting recognition system to handle different handwriting styles and their variations. Most current approaches are maximum-likelihood trained HMM systems and try to adapt their models to different writing styles using writer adaptive training, unsupervised clustering, or additional writer specific data. Discriminative training based on the Maximum Mutual Information criterion is used to train writer independent handwriting models. For model adaptation during decoding, an unsupervised confidence-based discriminative training on a word and frame level within a two-pass decoding process is proposed. Additionally, the training criterion is extended to incorporate a margin term. The proposed methods are evaluated on the IFN/ENIT Arabic handwriting database, where the proposed novel adaptation approach can decrease the word-error-rate by 33% relative. © 2009 IEEE.

关键词： Decoding

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Exploring multimedia databases via optimization-based relevance feedback and the earth mover's distance

Exploring multimedia databases via optimization-based releva...

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ACM 18th International Conference on Information and Knowledge Management, CIKM 2009

作者： Wichterich, Marc Beecks, Christian Sundermeyer, Martin Seidl, Thomas Data Management and Data Exploration Group RWTH Aachen University Germany Human Language Technology and Pattern Recognition Group RWTH Aachen University Germany

ISBN: (纸本)9781605585123

Determining similar objects is a fundamental operation both in data mining tasks such as clustering and in query-driven object retrieval. By definition of similarity search, query objects can only be imprecise descriptions of what users are looking for in a database, and even high-quality similarity measures can only be approximations of the users' notion of similarity. To overcome these shortcomings, iterative query refinement systems have been proposed. They utilize user feedback regarding the relevance of intermediate results to adapt the query object and/or the similarity measure. We propose an optimization-based relevance feedback approach for adaptable distance measures - focusing on the Earth Mover's Distance. Our technique enables quicker iterative database exploration as shown by our experiments. Copyright 2009 ACM.

关键词： Database systems

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Are unaligned words important for machine translation?

Are unaligned words important for machine translation?

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13th Annual Conference of the European Association for Machine Translation, EAMT 2009

作者： Zhang, Yuqi Matusov, Evgeny Ney, Hermann Human Language Technology and Pattern Recognition Lehrstuhl für Informatik 6 - Computer Science Department RWTH Aachen University D-52056 Aachen Germany

In this paper, we deal with the problem of a large number of unaligned words in automatically learned word alignments for machine translation (MT). These unaligned words are the reason for ambiguous phrase pairs extracted by a statistical phrase-based MT system. In translation, this phrase ambiguity causes deletion and insertion errors. We present hard and optional deletion approaches to remove the unaligned words in the source language sentences. Improvements in translation quality are achieved both on large and small vocabulary tasks with the presented methods. © 2009 European Association for Machine Translation.

关键词： Machine translation

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Confidence-Based Discriminative Training for Model Adaptation in Offline Arabic Handwriting recognition

Confidence-Based Discriminative Training for Model Adaptatio...

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International Conference on Document Analysis and recognition

作者： Philippe Dreuw Georg Heigold Hermann Ney Human Language Technology and Pattern Recognition RWTH Aachen University Aachen Germany

ISBN: (纸本)9781424445004

We present a novel confidence-based discriminative training for model adaptation approach for an HMM based Arabic handwriting recognition system to handle different handwriting styles and their *** current approaches are maximum-likelihood trained HMM systems and try to adapt their models to different writing styles using writer adaptive training, unsupervised clustering, or additional writer specific *** training based on the maximum mutual information criterion is used to train writer independent handwriting models. For model adaptation during decoding, an unsupervised confidence-based discriminative training on a word and frame level within a two-pass decoding process is proposed. Additionally, the training criterion is extended to incorporate a margin *** proposed methods are evaluated on the IFN/ENIT Arabic handwriting database, where the proposed novel adaptation approach can decrease the word-error-rate by 33% relative.

关键词： Adaptation model Handwriting recognition Hidden Markov models Writing Mutual information Maximum likelihood decoding Maximum likelihood estimation pattern recognition Databases Automatic speech recognition

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