Most of the previous work on topic-mixture models focuses on in-domain adaptation using large amounts of matched training data.
experiments with static model interpolation yielded a perplexity and relative word error rate (WeR) reduction of 7.1% and 2.1%, respectively, over an adapted trigram baseline.