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ICMCS
2007
IEEE

Hidden Conditional Random Fields for Meeting Segmentation

14 years 5 months ago
Hidden Conditional Random Fields for Meeting Segmentation
Automatic segmentation and classification of recorded meetings provides a basis towards understanding the content of a meeting. It enables effective browsing and querying in a meeting archive. Though robustness of existing approaches is often not reliable enough. We therefore strive to improve on this task by applying conditional random fields augmented by hidden states. These Hidden Conditional Random Fields have been proven to be efficient in low level pattern recognition tasks. Now we propose to use these novel models to segment a pre-recorded meeting into meeting events. Since they can also be seen as an extension to Hidden Markov Models an elaborate comparison of the two approaches is provided. Extensive test runs on the public M4 Scripted Meeting Corpus prove the great performance of applying our suggested novel approach compared to other similar methods.
Stephan Reiter, Björn Schuller, Gerhard Rigol
Added 03 Jun 2010
Updated 03 Jun 2010
Type Conference
Year 2007
Where ICMCS
Authors Stephan Reiter, Björn Schuller, Gerhard Rigoll
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