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Reseach Article

Speech Recognition: Increasing Efficiency of Support Vector Machines

by Aamir Khan, Muhammad Farhan, Asar Ali
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 35 - Number 7
Year of Publication: 2011
Authors: Aamir Khan, Muhammad Farhan, Asar Ali
10.5120/4413-6131

Aamir Khan, Muhammad Farhan, Asar Ali . Speech Recognition: Increasing Efficiency of Support Vector Machines. International Journal of Computer Applications. 35, 7 ( December 2011), 17-21. DOI=10.5120/4413-6131

@article{ 10.5120/4413-6131,
author = { Aamir Khan, Muhammad Farhan, Asar Ali },
title = { Speech Recognition: Increasing Efficiency of Support Vector Machines },
journal = { International Journal of Computer Applications },
issue_date = { December 2011 },
volume = { 35 },
number = { 7 },
month = { December },
year = { 2011 },
issn = { 0975-8887 },
pages = { 17-21 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume35/number7/4413-6131/ },
doi = { 10.5120/4413-6131 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:21:21.839455+05:30
%A Aamir Khan
%A Muhammad Farhan
%A Asar Ali
%T Speech Recognition: Increasing Efficiency of Support Vector Machines
%J International Journal of Computer Applications
%@ 0975-8887
%V 35
%N 7
%P 17-21
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

With the advancement of communication and security technologies, it has become crucial to have robustness of embedded biometric systems. This paper presents the realization of such technologies which demands reliable and error-free biometric identity verification systems. High dimensional patterns are not permitted due to eigen-decomposition in high dimensional feature space and degeneration of scattering matrices in small size sample. Generalization, dimensionality reduction and maximizing the margins are controlled by minimizing weight vectors. Results show good pattern by multimodal biometric system proposed in this paper. This paper is aimed at investigating a biometric identity system using Support Vector Machines(SVMs) and Lindear Discriminant Analysis(LDA) with MFCCs and implementing such system in real-time using SignalWAVE.

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Index Terms

Computer Science
Information Sciences

Keywords

Support Vector Machines(SVMs) Linear Discrimenent Analysis Speech Recognition FPGA Biometric System