Adaptive language modeling using minimum discriminant estimation
- 1 January 1992
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 1 (15206149) , 633-636 vol.1
- https://doi.org/10.1109/icassp.1992.225829
Abstract
The authors present an algorithm to adapt a n-gram language model to a document as it is dictated. The observed partial document is used to estimate a unigram distribution for the words that already occurred. Then, they find the closest n-gram distribution to the static n-gram distribution (using the discrimination information distance measure) that satisfies the marginal constraints derived from the document. The resulting minimum discrimination information model results in a perplexity of 208 instead of 290 for the static trigram model on a document of 321 words.Keywords
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