Unsupervised language model adaptation via topic modeling based on named entity hypotheses

Language model (LM) adaptation is often achieved by combining a generic LM with a topic-specific model that is more relevant to the target document. Unlike previous work on unsupervised LM adaptation, in this paper we propose to leverage named entity (NE) information for topic analysis and LM adaptation. We investigate two topic modeling approaches, latent Dirichlet allocation (LDA) and clustering, and proposed a new mixture topic model for LDA based LM adaptation. Our experiments for N-best list rescoring have shown that this new adaptation framework using NE information and topic analysis outperforms the baseline generic N-gram LM based on a state-of-the-art Mandarin recognition system.