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.