Iterative Unsupervised GMM Training for Speaker Indexing

Loading...
Thumbnail Image
Date
2007-09
ORCID
Advisor
Referee
Mark
Journal Title
Journal ISSN
Volume Title
Publisher
Společnost pro radioelektronické inženýrství
Abstract
The paper addresses a novel algorithm for speaker searching and indexation based on unsupervised GMM training. The proposed method doesn\'t require a predefined set of generic background models, and the GMM speaker models are trained only from test samples. The constrain of the method is that the number of the speakers has to be known in advance. The results of initial experiments show that the proposed training method enables to create precise GMM speaker models from only a small amount of training data.
Description
Citation
Radioengineering. 2007, vol. 16, č. 3, s. 138-144. ISSN 1210-2512
http://www.radioeng.cz/fulltexts/2007/07_03_138_144.pdf
Document type
Peer-reviewed
Document version
Published version
Date of access to the full text
Language of document
en
Study field
Comittee
Date of acceptance
Defence
Result of defence
Document licence
Creative Commons Attribution 3.0 Unported License
http://creativecommons.org/licenses/by/3.0/
DOI
Collections
Citace PRO