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 curren...
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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 extra...
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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...
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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 ...
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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.
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 ...
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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.
We describe Joshua, an open source toolkit for statistical machine translation. Joshua implements all of the algorithms required for synchronous context free grammars (SCFGs): chart-parsing, ngram language model integ...
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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 advant...
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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.
We propose to explicitly model white-spaces for Arabic handwriting recognition within different writing variants. Position-dependent character shapes in Arabic handwriting allow for large white-spaces between characte...
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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 transla...
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