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The problem of matching samples between two data sets is a fundamental task in unsupervised learning. In this paper we propose an algorithm based on statistical dependency between the data sets to solve the matching problem in a general case when samples in both data sets have different feature representations. As a concrete example, we consider the task of sentence-level alignment of parallel corpus...
N-gram models are the most widely used language models in large vocabulary continuous speech recognition. Since the size of the model grows rapidly with respect to the model order and available training data, many methods have been proposed for pruning the least relevant -grams from the model. However, correct smoothing of the N-gram probability distributions is important and performance may degrade...
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