Authors: Popović, Branislav
Janev, Marko 
Pekar, Darko
Jakovljević, Nikša
Gnjatović, Milan
Sečujski, Milan
Delić, Vlado
Affiliations: Mathematical Institute of the Serbian Academy of Sciences and Arts 
Title: A novel split-and-merge algorithm for hierarchical clustering of Gaussian mixture models
Journal: Applied Intelligence
Volume: 37
Issue: 3
First page: 377
Last page: 389
Issue Date: 1-Jan-2012
Rank: M21
ISSN: 0924-669X
DOI: 10.1007/s10489-011-0333-9
The paper presents a novel split-and-merge algorithm for hierarchical clustering of Gaussian mixture models, which tends to improve on the local optimal solution determined by the initial constellation. It is initialized by local optimal parameters obtained by using a baseline approach similar to k-means, and it tends to approach more closely to the global optimum of the target clustering function, by iteratively splitting and merging the clusters of Gaussian components obtained as the output of the baseline algorithm. The algorithm is further improved by introducing model selection in order to obtain the best possible trade-off between recognition accuracy and computational load in a Gaussian selection task applied within an actual recognition system. The proposed method is tested both on artificial data and in the framework of Gaussian selection performed within a real continuous speech recognition system, and in both cases an improvement over the baseline method has been observed.
Keywords: Continuous speech recognition | Gaussian mixtures | Hierarchical clustering | Split-and-merge operation
Publisher: Springer Link
Project: Development of Dialogue Systems for Serbian and Other South Slavic Languages 

Show full item record


checked on May 29, 2024

Page view(s)

checked on May 9, 2024

Google ScholarTM




Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.