Domain Adaptation of Maximum Entropy Language Models
Tanel Alumäe, Mikko Kurimo · 2010
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 consistently outperforms linear interpolation which is typically used in such cases. 1