Minimum cut model for spoken lecture segmentation

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Abstract

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. © 2006 Association for Computational Linguistics.

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APA

Malioutov, I., & Barzilay, R. (2006). Minimum cut model for spoken lecture segmentation. In COLING/ACL 2006 - 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Vol. 1, pp. 25–32). Association for Computational Linguistics (ACL). https://doi.org/10.3115/1220175.1220179

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