Method and apparatus for distribution-based language model...

Data processing: speech signal processing – linguistics – language – Linguistics – Natural language

Reexamination Certificate

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C704S231000, C704S240000, C704S250000, C704S257000, C704S266000

Reexamination Certificate

active

07043422

ABSTRACT:
A method and apparatus are provided for adapting a language model to a task-specific domain. Under the method and apparatus, the relative frequency of n-grams in a small training set (i.e. task-specific training data set) and the relative frequency of n-grams in a large training set (i.e. out-of-domain training data set) are used to weight a distribution count of n-grams in the large training set. The weighted distributions are then used to form a modified language model by identifying probabilities for n-grams from the weighted distributions.

REFERENCES:
“A Language Model Adaptation Method for fixed Phrases by Emphasizing N-gram Subsets”- Tomoyoshi et al.
1992 IEEE -“Adaptive Language Modeling Using Minimum Discriminant Estimation”—Pietra et al.
1994 Academic Press limited-“A weighted average n-gram model of natural language”—O'Boyle et al.
R. Iyer, M. Ostendorf, H. Gish, “Using Out-of-Domain Data to Improve In-Domain Language Models,” IEEE Signal Processing letters, vol. 4, No. 8, pp. 221-223 (Aug. 1997).
P. Clarkson and A. Robinson, “Language Model Adaptation Using Mixtures and An Exponentially Decaying Cache,” In Proc. ICASSP-97, pp. 799-802 (1997).
K. Seymore, R. Rosenfeld, “Large-Scale Detection and Language Model Adaptation” (Jun. 1997).
J. Gao, K.F. Lee, “Distribution-Based Pruning of Backoff Language Models,” In Proceedings of the Annual Meeting of the ACL, Hong Kong, 7 pages (Oct. 3-6, 2000).

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