Performance Comparison of Speaker Recognition using Vector Quantization by LBG and KFCG

Abstract

In this paper, two approaches for speaker Recognition based on Vector quantization are proposed and their performances are compared. Vector Quantization (VQ) is used for feature extraction in both the training and testing phases. Two methods for codebook generation have been used. In the 1 st method, codebooks are generated from the speech samples by using the Linde-Buzo-Gray (LBG) algorithm. In the 2 nd method, the codebooks are generated using the Kekre"s Fast Codebook Generation (KFCG) algorithm. For speaker identification, the codebook of the test sample is similarly generated and compared with the codebooks of the reference samples stored in the database. The results obtained for both the methods have been compared. The results show that KFCG gives better results than LBG.