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Speaker Recognition using Support Vector Machine

by Geeta Nijhawan, M. K. Soni
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 87 - Number 2
Year of Publication: 2014
Authors: Geeta Nijhawan, M. K. Soni
10.5120/15178-3379

Geeta Nijhawan, M. K. Soni . Speaker Recognition using Support Vector Machine. International Journal of Computer Applications. 87, 2 ( February 2014), 7-10. DOI=10.5120/15178-3379

@article{ 10.5120/15178-3379,
author = { Geeta Nijhawan, M. K. Soni },
title = { Speaker Recognition using Support Vector Machine },
journal = { International Journal of Computer Applications },
issue_date = { February 2014 },
volume = { 87 },
number = { 2 },
month = { February },
year = { 2014 },
issn = { 0975-8887 },
pages = { 7-10 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume87/number2/15178-3379/ },
doi = { 10.5120/15178-3379 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:04:51.769129+05:30
%A Geeta Nijhawan
%A M. K. Soni
%T Speaker Recognition using Support Vector Machine
%J International Journal of Computer Applications
%@ 0975-8887
%V 87
%N 2
%P 7-10
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Speaker recognition is the process of recognizing the speaker based on characteristics such as pitch ,tone in the speech wave. Background noise influences the overall efficiency of speaker recognition system and is still considered as one of the most challenging issue in Speaker Recognition System (SRS). In this paper mel-frequency cepstral coefficients (MFCC) feature is used along with Vector Quantisation(VQ)-LBG [Linde, Buzo and Gray, 1980] algorithm for designing SRS. MFCC feature is extracted from the input speech and then vector quantization of the extracted MFCC features is done using VQLBG algorithm. It reduces the dimensionality of the input vector . These MFCCs are used as the speaker features for matching via Support Vector Machine (SVM) method. The experimental results show that the proposed text-dependent speaker identification system gives an accuracy rate of 95. 0%.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Feature extraction vector quantization MFCC SVM

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