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NAACL
2003

Getting More Mileage from Web Text Sources for Conversational Speech Language Modeling using Class-Dependent Mixtures

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Getting More Mileage from Web Text Sources for Conversational Speech Language Modeling using Class-Dependent Mixtures
Sources of training data suitable for language modeling of conversational speech are limited. In this paper, we show how training data can be supplemented with text from the web filtered to match the style and/or topic of the target recognition task, but also that it is possible to get bigger performance gains from the data by using class-dependent interpolation of N-grams.
Ivan Bulyko, Mari Ostendorf, Andreas Stolcke
Added 31 Oct 2010
Updated 31 Oct 2010
Type Conference
Year 2003
Where NAACL
Authors Ivan Bulyko, Mari Ostendorf, Andreas Stolcke
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