(untitled) (Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2021)
The speaker recognition is one of the most emerging field in the area of digital signal processing. In this paper, analyzed the performance comparison of voice recognition using Mel frequency Cepstrum coefficient (MFCC) with two methods based on vector quantization (VQ) techniques by Linde buzo gray (LBG) algorithm and improved weighted VQ method. In the first phase, extract the minor amount of data points from the voice signal that is used subsequent to represent every speaker is known as MFCC and in the second phase, for feature matching there are two approaches which have been proposed here based on VQ techniques for recognition purpose and also comparison of both algorithms are being done with different time length speech samples to get better recognition rate and improving in efficiency of the system. The VQ is used as feature matching with traditional LBG Algorithm and improved weighted VQ algorithm for better recognition. The weight of vector in VQ algorithm to get the weighted distortion and after compare between traditional and improved weighted VQ algorithm. When the test time ‘1s’ for traditional VQ algorithm 81.25% whereas improved weighted VQ 87.5 % and if test time ‘2s’ for traditional VQ algorithm 87 % whereas, improved weighted VQ 93.7 %. That is improved weighted VQ algorithm gives the better speaker recognition than VQ with LBG algorithm.