9th Annual Conference of the International Speech Communication Association

Brisbane, Australia
September 22-26, 2008

Unsupervised Language Model Adaptation Based on Topic and Role Information in Multiparty Meetings

Songfang Huang, Steve Renals

University of Edinburgh, UK

We continue our previous work on the modeling of topic and role information from multiparty meetings using a hierarchical Dirichlet process (HDP), in the context of language model adaptation. In this paper we focus on three problems: 1) an empirical analysis of the HDP as a nonparametric topic model; 2) the mismatch problem of vocabularies of the baseline n-gram model and the HDP; and 3) an automatic speech recognition experiment to further verify the effectiveness of our adaptation framework. Experiments on a large meeting corpus of more than 70 hours speech data show consistent and significant improvements in terms of word error rate for language model adaptation based on the topic and role information.

Full Paper

Bibliographic reference.  Huang, Songfang / Renals, Steve (2008): "Unsupervised language model adaptation based on topic and role information in multiparty meetings", In INTERSPEECH-2008, 833-836.