Automatic Decision Detection in Meeting Speech

Pei-Yun Hsueh, Johanna D. Moore

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

Abstract / Description of output

Decision making is an important aspect of meetings in organisational settings, and archives of meeting recordings constitute a valuable source of information about the decisions made. However, standard utilities such as playback and keyword search are not sufficient for locating decision points from meeting archives. In this paper, we present the AMI DecisionDetector, a system that automatically detects and highlights where the decision-related conversations are. In this paper, we apply the models developed in our previous work [1], which detects decision-related dialogue acts (DAs) from parts of the transcripts that have been manually annotated as extract-worthy, to the task of detecting decision-related DAs and topic segments directly from complete transcripts. Results show that we need to combine features extracted from multiple knowledge sources (e.g., lexical, prosodic, DA-related, and topical class) in order to yield the model with the highest precision. We have provided a quantitative account of the feature class effects. As our ultimate goal is to operate AMI DecisionDetector in a fully automatic fashion, we also investigate the impacts of using automatically generated features, for example, the 5-class DA features obtained in [2].
Original languageEnglish
Title of host publicationMachine Learning for Multimodal Interaction
Subtitle of host publication4th International Workshop, MLMI 2007, Brno, Czech Republic, June 28-30, 2007, Revised Selected Papers
PublisherSpringer Berlin Heidelberg
Number of pages11
ISBN (Electronic)978-3-540-78155-4
ISBN (Print)978-3-540-78154-7
Publication statusPublished - 2008

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Berlin / Heidelberg
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Keywords / Materials (for Non-textual outputs)

  • argumentation modelling
  • meeting tracking and analysis
  • spoken language understanding


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