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http://dx.doi.org/10.3745/KIPSTB.2003.10B.1.067

A Semi-Noniterative VQ Design Algorithm for Text Dependent Speaker Recognition  

Lim, Dong-Chul (아주대학교 대학원 전자공학부)
Lee, Haing-Sei (아주대학교)
Publication Information
The KIPS Transactions:PartB / v.10B, no.1, 2003 , pp. 67-72 More about this Journal
Abstract
In this paper, we study the enhancement of VQ (Vector Quantization) design for text dependent speaker recognition. In a concrete way, we present the non-Iterative method which makes a vector quantization codebook and this method Is nut Iterative learning so that the computational complexity is epochally reduced. The proposed semi-noniterative VQ design method contrasts with the existing design method which uses the iterative learning algorithm for every training speaker. The characteristics of a semi-noniterative VQ design is as follows. First, the proposed method performs the iterative learning only for the reference speaker, but the existing method performs the iterative learning for every speaker. Second, the quantization region of the non-reference speaker is equivalent for a quantization region of the reference speaker. And the quantization point of the non-reference speaker is the optimal point for the statistical distribution of the non-reference speaker In the numerical experiment, we use the 12th met-cepstrum feature vectors of 20 speakers and compare it with the existing method, changing the codebook size from 2 to 32. The recognition rate of the proposed method is 100% for suitable codebook size and adequate training data. It is equal to the recognition rate of the existing method. Therefore the proposed semi-noniterative VQ design method is, reducing computational complexity and maintaining the recognition rate, new alternative proposal.
Keywords
Vector Quantization; Clustering; Speaker Recognition; Computational Complexity; Learning;
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