Improvements on common vector approach using k-clustering method

Authors:
Seohoon Jin;MyungWoo Nam;Sang-Tae Han
Affiliations:
Department of Cross Sell Marketing, Hyundai Capital, Seoul, Korea;Department of Digital Elecronics Design, Hyejeon College, Choongnam, Korea;Department of Informational Statistics, Hoseo University, 29-1, Asan, Korea
Venue:
PKAW'06 Proceedings of the 9th Pacific Rim Knowledge Acquisition international conference on Advances in Knowledge Acquisition and Management
Year:
2006

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Abstract

In this paper, an advanced common vector approach (CVA) method for isolated word recognition is presented. The proposed method eliminates drawback of conventional CVA method, which is impossibility of being applied to a large number of training voices case, by dividing the training voices into a few small groups where those voices belong to a class of one of the spoken words. The results from using MFCC, LPC, LSP, Cepstrum, and auditory model shows that the proposed method solves the drawback of conventional CVA method. It got better recognition rate of 1.39% without significant changes of amounts of computation.