Domain adaptation with clustered language models
J.P. Ueberla · 2002
A method of domain adaptation for clustered language models is developed. It is based on a previously developed clustering algorithm (Ueberla, 1994), but with a modified optimisation criterion. The results are shown to be slightly superior to the previously published 'Fillup' method (Besling and Meier, 1995), 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.