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Published in:
1992 | OriginalPaper | Chapter
Clustering of Gaussian densities in hidden Markov models
Author : S. Euler
Published in: Speech Recognition and Understanding
Publisher: Springer Berlin Heidelberg
Included in: Professional Book Archive
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In order to reduce the number of Gaussian densities in a hidden Markov model based speech recognition system, a clustering scheme based on the Kullback divergence and the k-means clustering algorithm is proposed. The approach is tested in speaker independent recognition experiments for a vocabulary of 23 German words. A reduction of 50% in the number of densities can be achieved without degradation of the recognition performance.