Modeling Topic and Role Information in Meetings Using the Hierarchical Dirichlet Process

Authors:
Songfang Huang;Steve Renals
Affiliations:
The Centre for Speech Technology Research, University of Edinburgh, Edinburgh, UK EH8 9LW;The Centre for Speech Technology Research, University of Edinburgh, Edinburgh, UK EH8 9LW
Venue:
MLMI '08 Proceedings of the 5th international workshop on Machine Learning for Multimodal Interaction
Year:
2008

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Abstract

In this paper, we address the modeling of topic and role information in multiparty meetings, via a nonparametric Bayesian model called the hierarchical Dirichlet process. This model provides a powerful solution to topic modeling and a flexible framework for the incorporation of other cues such as speaker role information. We present our modeling framework for topic and role on the AMI Meeting Corpus, and illustrate the effectiveness of the approach in the context of adapting a baseline language model in a large-vocabulary automatic speech recognition system for multiparty meetings. The adapted LM produces significant improvements in terms of both perplexity and word error rate.