Paper: Domain Adaptation of Maximum Entropy Language Models

ACL ID P10-2056
Title Domain Adaptation of Maximum Entropy Language Models
Venue Annual Meeting of the Association of Computational Linguistics
Session Short Paper
Year 2010
Authors

We investigate a recently proposed Bayesian adaptation method for building style-adapted maximum entropy language models for speech recognition, given a large corpus of written language data and a small corpus of speech transcripts. Experiments show that the method con- sistently outperforms linear interpolation which is typically used in such cases.