Language model adaptation using dynamic marginals

Abstract

A new method is presented to quickly adapt a given language model to local text characteristics. The basic approach is to choose the adaptive models as close as possible to the background estimates while constraining them to respect the locally estimated unigram probabilities. Several means are investigated to speed up the calculations. We measure both perplexity and word error rate to gauge the quality of our model.