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Automatic Content Segmentation of audio recordings at multidisciplinary medical team meetings
University of Dublin, Trinity College, Ireland.ORCID iD: 0000-0003-3211-6529
2008 (English)In: International Conference on Information Technology, IEEE, 2008, p. 309-312Conference paper, Published paper (Refereed)
Abstract [en]

A single recording of a multidisciplinary medical team meeting (MDTM) can be expected to contain several sep- arate discussions on different patients. Automatic speaker segmentation alone does not allow for the separation of in- dividual patient case discussions (PCDs). A novel method is presented here, based on Hidden Markov Models (HMM), to segment audio recordings of MDTMs and facilitate the non-linear retrieval of individual PCDs. The method com- bines professional role interaction with speaker vocaliza- tion patterns. The sequence and duration of vocalization and speakers’ roles are used as training states. Results demonstrate HMM segmentation to have good potential in the development of an MDTM browser. The approach out- lined here can be applied in a wide range of meetings. 

Place, publisher, year, edition, pages
IEEE, 2008. p. 309-312
National Category
Engineering and Technology
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kau:diva-56978OAI: oai:DiVA.org:kau-56978DiVA, id: diva2:1119975
Conference
International Conference on Information Technology
Available from: 2017-07-05 Created: 2017-07-05 Last updated: 2017-07-05

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Kane, Bridget
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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
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Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
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  • Other locale
More languages
Output format
  • html
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  • asciidoc
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