We propose a tracking adaptation to recover from early tracking errors in sign languagerecognition by optimizing the obtained tracking paths w.r.t. the hypothesized word sequences of an automatic sign language recogn...
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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 language...
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In this paper, we dissect the influence of several target-side dependency-based extensions to hierarchical machine translation, including a dependency language model (LM). We pursue a non-restrictive approach that doe...
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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 descrip...
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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.
A Gaussian or log-linear mixture model trained by maximum likelihood may be trained further using discriminative training. It is desirable that the mixture splitting is also done during the discriminative training, to...
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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 punctua...
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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 mod...
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In this work, multiple hierarchical language modeling strategies for a zero OOV rate large vocabulary continuous speech recognition system are investigated. In our previously proposed hierarchical approach, a full-wor...
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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 m...
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