Multistream Dynamic Bayesian Network for Meeting Segmentation

Alfred Dielmann, Steve Renals

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract / Description of output

This paper investigates the automatic analysis and segmentation of meetings. A meeting is analysed in terms of individual behaviours and group interactions, in order to decompose each meeting in a sequence of relevant phases, named meeting actions. Three feature families are extracted from multimodal recordings: prosody from individual lapel microphone signals, speaker activity from microphone array data and lexical features from textual transcripts. A statistical approach is then used to relate low-level features with a set of abstract categories. In order to provide a flexible and powerful framework, we have employed a dynamic Bayesian network based model, characterized by multiple stream processing and flexible state duration modelling. Experimental results demonstrate the strength of this system, providing a meeting action error rate of 9%.
Original languageEnglish
Title of host publicationMachine Learning for Multimodal Interaction
Subtitle of host publication First International Workshop, MLMI 2004, Martigny, Switzerland, June 21-23, 2004, Revised Selected Papers
EditorsSamy Bengio, Hervé Bourlard
Place of PublicationBerlin, Heidelberg
PublisherSpringer Berlin Heidelberg
Number of pages11
ISBN (Electronic)978-3-540-30568-2
ISBN (Print)978-3-540-24509-4
Publication statusPublished - 2005
EventFirst International Workshop MLMI 2004 - Martigny, Switzerland
Duration: 21 Jun 200423 Jun 2004

Publication series

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


WorkshopFirst International Workshop MLMI 2004


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