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Content Provider | IEEE Xplore Digital Library |
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Author | Hutchinson, B. Bin Zhang Ostendorf, M. |
Copyright Year | 2010 |
Description | Author affiliation: Electrical Engineering Department, University of Washington, Seattle, 98195, USA (Hutchinson, B.; Bin Zhang; Ostendorf, M.) |
Abstract | We present an approach to unsupervised speaker role labeling in talk show data that makes use of two complementary sets of features: structural features that encode the participation patterns of speakers, and lexical features, which capture characteristic phrases. Techniques for using multiple clusterings are explored, leading to more robust results. Experiments on English and Mandarin talk shows yield performance similar to that reported for broadcast news using supervised learning. |
Starting Page | 5322 |
Ending Page | 5325 |
File Size | 134795 |
Page Count | 4 |
File Format | |
ISBN | 9781424442959 |
ISSN | 15206149 |
DOI | 10.1109/ICASSP.2010.5494958 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2010-03-14 |
Publisher Place | USA |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Broadcasting Labeling Hidden Markov models Unsupervised learning Entropy Clustering algorithms Robustness Supervised learning Frequency Training data broadcast conversations meta-clustering speaker role classification |
Content Type | Text |
Resource Type | Article |
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