Title :
Clustering via the Bayesian information criterion with applications in speech recognition
Author :
Chen, Scott Shaobing ; Gopalakrishnan, P.S.
Author_Institution :
IBM Thomas J. Watson Res. Center, Yorktown Heights, NY, USA
Abstract :
One difficult problem we are often faced with in clustering analysis is how to choose the number of clusters. We propose to choose the number of clusters by optimizing the Bayesian information criterion (BIC), a model selection criterion in the statistics literature. We develop a termination criterion for the hierarchical clustering methods which optimizes the BIC criterion in a greedy fashion. The resulting algorithms are fully automatic. Our experiments on Gaussian mixture modeling and speaker clustering demonstrate that the BIC criterion is able to choose the number of clusters according to the intrinsic complexity present in the data
Keywords :
Bayes methods; Gaussian processes; information theory; pattern classification; speech recognition; statistical analysis; Gaussian mixture modeling; automatic algorithms; clustering analysis; data complexity; experiments; greedy Bayesian information criterion; hierarchical clustering methods; model selection; speaker clustering; speech recognition; statistics; termination criterion; Bayesian methods; Clustering algorithms; Clustering methods; Data analysis; Gaussian processes; Hidden Markov models; Merging; Optimization methods; Speech recognition; Statistics;
Conference_Titel :
Acoustics, Speech and Signal Processing, 1998. Proceedings of the 1998 IEEE International Conference on
Conference_Location :
Seattle, WA
Print_ISBN :
0-7803-4428-6
DOI :
10.1109/ICASSP.1998.675347