Speaker Recognition and Performance Comparison based on Machine Learning
Main Article Content
Abstract
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.
Downloads
Metrics
Article Details
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
