Extracting Phrase Patterns with Minimum Redundancy for Unsupervised Speaker Role Classification

Bin Zhang, Brian Hutchinson, Wei Wu, Mari Ostendorf

Abstract: This paper addresses the problem of learning phrase patterns for unsupervised speaker role classification. Phrase patterns are automatically extracted from large corpora, and redundant patterns are removed via a graph pruning algorithm. In experiments on English and Mandarin talk shows, the use of phrase patterns results in an increase of role classification accuracy over n-gram lexical features, and more compact phrase pattern lists are obtained due to the redundancy removal.