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Content Provider | IEEE Xplore Digital Library |
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Author | Lapidot, I. |
Copyright Year | 2012 |
Description | Author affiliation: LIA, University of Avignon, 339 Chemin des Meinajaries BP 91228, Avignon, 84911 France (Lapidot, I.) |
Abstract | We examine different initializations and their influence on the performances of iterative speaker diarization system. Six methods of initializations were under examination, starting with a naive frame based random initialization, continue with uniform conversation dividing between the clusters and ending with weighted segmental k-means. The initialization methods were tested on two telephone conversation databases: LDC America CallHome and NIST SRE-05. In contrast to most works on meeting and shows where the speakers turns are not very frequent and minimal duration constraints of 2.5 sec or more can be applied to capture speakers statistics, in telephone conversations the speaker turns are much more frequent and the minimum duration should be set to several hundreds of milliseconds. In such cases, good cluster initialization is very important. It will be shown that good initialization using weighted segmental k-means is outperforms all other methods, and the either fixed or minimum duration constraints can be minor, and even without any constraint on the segment duration the results are significantly better than in other initializations. |
Starting Page | 1 |
Ending Page | 4 |
File Size | 344455 |
Page Count | 4 |
File Format | |
ISBN | 9781467346825 |
e-ISBN | 9781467346818 |
DOI | 10.1109/EEEI.2012.6376947 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2012-11-14 |
Publisher Place | Israel |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Speech Hidden Markov models Databases NIST Vectors Density estimation robust algorithm Training weighted segmental k-means (WSKMeans) Speaker diarization initialization |
Content Type | Text |
Resource Type | Article |
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