Minimum Cut Model for Spoken Lecture Segmentation

We consider the task of unsupervised lecture segmentation. We formalize segmentation as a graph-partitioning task that optimizes the normalized cut criterion. Our approach moves beyond localized comparisons and takes into account longrange cohesion dependencies. Our results demonstrate that global analysis improves the segmentation accuracy and is robust in the presence of speech recognition errors.

• Type: Conference
• Year: 2006
• Where: ACL
• Authors: Igor Malioutov, Regina Barzilay
• Keywords: ACL 2006, ACL 2007, Normalized Cut Criterion, Segmentation Accuracy, Unsupervised Lecture Segmentation