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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Original languageEnglish
Title of host publicationMachine Learning for Multimodal Interaction
Subtitle of host publication5th International Workshop, MLMI 2008, Utrecht, The Netherlands, September 8-10, 2008. Proceedings
EditorsA. Popescu-Belis, R. Stiefelhagen
PublisherSpringer Berlin Heidelberg
Pages214-225
Number of pages12
Volume5237
DOIs
Publication statusPublished - 2008

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Bearlin Heidelberg
Volume5237
ISSN (Print)0302-9743

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.

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