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Using Probabilistic Feature Matching to Understand Spoken Descriptions

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AI 2008: Advances in Artificial Intelligence (AI 2008)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5360))

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Abstract

We describe a probabilistic reference disambiguation mechanism developed for a spoken dialogue system mounted on an autonomous robotic agent. Our mechanism performs probabilistic comparisons between features specified in referring expressions (e.g. size and colour) and features of objects in the domain. The results of these comparisons are combined using a function weighted on the basis of the specified features. Our evaluation shows high reference resolution accuracy across a range of spoken referring expressions.

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Author information

Authors and Affiliations

  1. Faculty of Information Technology, Monash University, Clayton, VICTORIA, 3800, Australia

    Ingrid Zukerman, Enes Makalic & Michael Niemann

Authors
  1. Ingrid Zukerman
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  2. Enes Makalic
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  3. Michael Niemann
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Editor information

Editors and Affiliations

  1. Wales, School of Computer Science and Engineering,, University of New South, NSW 2052, Sydney, Australia

    Wayne Wobcke

  2. School of Mathematics, Statistics and Computer Science, Victoria University of Wellington, P.O. Box 600, 6140, Wellington, New Zealand

    Mengjie Zhang

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Zukerman, I., Makalic, E., Niemann, M. (2008). Using Probabilistic Feature Matching to Understand Spoken Descriptions. In: Wobcke, W., Zhang, M. (eds) AI 2008: Advances in Artificial Intelligence. AI 2008. Lecture Notes in Computer Science(), vol 5360. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89378-3_16

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  • DOI: https://doi.org/10.1007/978-3-540-89378-3_16

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-89377-6

  • Online ISBN: 978-3-540-89378-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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