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Language Modeling with Limited Domain Data

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journal contribution
posted on 01.12.2008, 00:00 by Alexander Rudnicky

Generic recognition systems contain language models which are
representative of a broad corpus. In actual practice, however, recognition
is usually on a coherent text covering a single topic, suggesting
that knowledge of the topic at hand can be used to advantage. A base
model can be augmented with information from a small sample of
domain-specific language data to significantly improve recognition
performance. Good performance may be obtained by merging in
only those n-grams that include words that are out of vocabulary
with respect to the base model.