  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.
