In this paper, a method of domain adaptation for clustered language models is developed. It is based on a previously developed clustering algorithm, but with a modified optimisation criterion. The results are shown to be slightly superior to the previously published 'Fillup' method, which can be used to adapt standard n-gram models. However, the improvement both methods give compared to models built from scratch on the adaptation data is quite small (less than 11% relative improvement in word error rate). This suggests that both methods are still unsatisfactory from a practical point of view.
@article{arxiv.cmp-lg/9703001,
title = {Domain Adaptation with Clustered Language Models},
author = {Joerg P. Ueberla},
journal= {arXiv preprint arXiv:cmp-lg/9703001},
year = {2008}
}