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A weighted average n-gram model of natural language

โœ Scribed by P. O'Boyle; M. Owens; F.J. Smith

Publisher
Elsevier Science
Year
1994
Tongue
English
Weight
403 KB
Volume
8
Category
Article
ISSN
0885-2308

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โœฆ Synopsis

A new (n)-gram model of natural language designed to aid speech recognition is presented in which the probabilities are calculated as a weighted average of maximum likelihood probabilities obtained from a training corpus. This simple approach produces a model that can be constructed quickly and is easily adapted either by changing the weights or by changing the training corpus. The model is compared with two other models; the first is based on Turing-Good estimates and uses Katz's back-off approach. The second model is a deleted estimate model which combines different probability distributions in approximately optimal proportions.

We introduce a new measure for language models based on their performance when predicting words removed randomly from samples of unseen text. The performance of all three models using both this new measure and the existing measure of perplexity have been compared. Results indicate that the performance of the new model is close to the performance of the deleted estimate model, while both are superior to the Turing-Good model.

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