Automatic segmentation and classification of recorded meetings provides a basis that enables effective browsing and querying in a meeting archive. Yet, robustness of today's approaches is often not reliable enough. We therefore strive to improve on this task by introduction of a hybrid approach combining the discriminative abilities of artificial neural nets and warping capabilities of hidden markov models. Dividing the task into two layers and defining a proper set of individual actions helps to cope with the problem of lack of data and overcomes conventional single-layered approaches. Extensive test runs on the public M4 Scripted Meeting Corpus prove the great performance gain applying our suggested novel approach compared to other similar methods.
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