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An improved K-means clustering algorithm and application to combined multi-codebook/MLP neural network speech recognition

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2 Author(s)
Wang, Fang ; Dept. of Electron., Carleton Univ., Ottawa, Ont., Canada ; Zhang, Q.J.

Unsupervised learning algorithms play a central part in models of neural computation. K-means clustering algorithms, a type of unsupervised learning algorithms, have been used in many application areas. We propose an improved K-means algorithm for optimal partition which can achieve better variation equalization than standard binary splitting algorithms. The proposed clustering algorithm was applied to combined multi-codebook/MLP neural network speech recognition system to train the LPC based codebooks. It achieved smaller variation of the variances of clusters than that from the standard binary splitting algorithm

Published in:

Electrical and Computer Engineering, 1995. Canadian Conference on  (Volume:2 )

Date of Conference:

5-8 Sep 1995