Discriminative training of language models for text and speech classification
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United States of America Patent
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Nov 6, 2012
Grant Date -
Sep 4, 2008
app pub date -
Apr 15, 2008
filing date -
Jun 3, 2003
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Abstract
Methods are disclosed for estimating language models such that the conditional likelihood of a class given a word string, which is very well correlated with classification accuracy, is maximized. The methods comprise tuning statistical language model parameters jointly for all classes such that a classifier discriminates between the correct class and the incorrect ones for a given training sentence or utterance. Specific embodiments of the present invention pertain to implementation of the rational function growth transform in the context of a discriminative training technique for n-gram classifiers.
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