Assessing the effectiveness of conversational features for dialogue segmentation in medical team meetings and in the AMI corpus

Saturnino Luz*, Jing Su

*Corresponding author for this work

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

Abstract

This paper presents a comparison of two similar dialogue analysis tasks: segmenting real-life medical team meetings into patient case discussions, and segmenting scenario-based meetings into topics. In contrast to other methods which use transcribed content and prosodic features (such as pitch, loudness etc), the method used in this comparison employs only the duration of the prosodic units themselves as the basis for dialogue representation. A concept of Vocalisation Horizon (VH) allows us to treat segmentation as a classification task where each instance to be classified is represented by the duration of a talk spurt, pause or speech overlap event in the dialogue. We report on the results this method yielded in segmentation of medical meetings, and on the implications of the results of further experiments on a larger corpus, the Augmented Multiparty Meeting corpus, to our ongoing efforts to support data collection and information retrieval in medical team meetings.

Original languageEnglish
Title of host publicationProceedings of the SIGDIAL 2010 Conference
Subtitle of host publication11th Annual Meetingof the Special Interest Group onDiscourse and Dialogue
Pages332-339
Number of pages8
Publication statusPublished - 2010
Event11th Annual Meeting of the Special Interest Group on Discourse and Dialogue, SIGDIAL 2010 - Tokyo, Japan
Duration: 24 Sep 201025 Sep 2010

Publication series

NameProceedings of the SIGDIAL 2010 Conference: 11th Annual Meeting of the Special Interest Group onDiscourse and Dialogue

Conference

Conference11th Annual Meeting of the Special Interest Group on Discourse and Dialogue, SIGDIAL 2010
CountryJapan
CityTokyo
Period24/09/1025/09/10

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