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Segmenting Conversations by Topic, Initiative, and Style

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Information Retrieval Techniques for Speech Applications (IRTSA 2001)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2273))

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Abstract

Topical segmentation is a basic tool for information access to audio records of meetings and other types of speech documents which may be fairly long and contain multiple topics. Standard segmentation algorithms are typically based on keywords, pitch contours or pauses. This work demonstrates that speaker initiative and style may be used as segmentation criteria as well. A probabilistic segmentation procedure is presented which allows the integration and modeling of these features in a clean framework with good results.

Keyword based segmentation methods degrade significantly on our meeting database when speech recognizer transcripts are used instead of manual transcripts. Speaker initiative is an interesting feature since it delivers good segmentations and should be easy to obtain from the audio. Speech style variation at the beginning, middle and end of topics may also be exploited for topical segmentation and would not require the detection of rare keywords.

I would like to thank my advisor Alex Waibel for supporting and encouraging this work and my collegues for various discoursive and practical contributions, especially Hua Yu and Klaus Zechner. The reviewers provided valuable comments for the final paper presentation. I would also like to thank our sponsors at DARPA. Any opinions, findings and conclusions expressed in this material are those of the authors and do not necessarily reflect the views of DARPA, or any other party.

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Author information

Authors and Affiliations

  1. Interactive Systems Labs, Carnegie Mellon University, 15213, Pittsburgh, PA, USA

    Klaus Ries

  2. Fakultät für Informatik, Universität Karlsruhe, 76128, Karlsruhe, Germany

    Klaus Ries

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  1. Klaus Ries
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Editors and Affiliations

  1. IBM T.J. Watson Research Center, P.O.Box 704, 10598, Yorktown Heights, NY, USA

    Anni R. Coden  & Eric W. Brown  & 

  2. IBM Almaden Research Center, 650 Harry Road, 95120, San Jose, CA, USA

    Savitha Srinivasan

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© 2002 Springer-Verlag Berlin Heidelberg

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Ries, K. (2002). Segmenting Conversations by Topic, Initiative, and Style. In: Coden, A.R., Brown, E.W., Srinivasan, S. (eds) Information Retrieval Techniques for Speech Applications. IRTSA 2001. Lecture Notes in Computer Science, vol 2273. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45637-6_5

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  • DOI: https://doi.org/10.1007/3-540-45637-6_5

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  • Print ISBN: 978-3-540-43156-5

  • Online ISBN: 978-3-540-45637-7

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