Guassian pdfs Clustering Using a Divergence Measure-based Neural Network 


Vol. 29,  No. 5, pp. 627-631, May  2004


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  Abstract

An efficient algorithm for clustering of GPDFs(Gaussian Probability Density Functions) in a speech recognition model is proposed in this paper. The proposed algorithm is based on CNN with the divergence as its distance measure and is applied to a speech recognition. The algorithm is compared with conventional Dk-means (Divergence-based k-means) algorithm in CDHMM(Continuous Density Hidden Markov Model). The results show that it can reduce about 31.3% of GPDFs over Dk-means algorithm without suffering any recognition performance. When compared with the case that no clustering is employed and full GPDFs are used, the prposed algorithm can save about 61.8% of GPDFs while preserving the recognition performance.

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  Cite this article

[IEEE Style]

D. Park and O. Kwon, "Guassian pdfs Clustering Using a Divergence Measure-based Neural Network," The Journal of Korean Institute of Communications and Information Sciences, vol. 29, no. 5, pp. 627-631, 2004. DOI: .

[ACM Style]

Dong-Chul Park and Oh-Hyun Kwon. 2004. Guassian pdfs Clustering Using a Divergence Measure-based Neural Network. The Journal of Korean Institute of Communications and Information Sciences, 29, 5, (2004), 627-631. DOI: .

[KICS Style]

Dong-Chul Park and Oh-Hyun Kwon, "Guassian pdfs Clustering Using a Divergence Measure-based Neural Network," The Journal of Korean Institute of Communications and Information Sciences, vol. 29, no. 5, pp. 627-631, 5. 2004.

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pISSN : 1226 - 4717
eISSN : 2287 - 3880