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Neural Network Based Speaker Verification using GFCC

Sukhandeep Kaur1 , Kanwalvir Singh Dhindsa2

Section:Research Paper, Product Type: Journal Paper
Volume-3 , Issue-11 , Page no. 63-65, Nov-2015

Online published on Nov 30, 2015

Copyright © Sukhandeep Kaur , Kanwalvir Singh Dhindsa . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: Sukhandeep Kaur , Kanwalvir Singh Dhindsa, “Neural Network Based Speaker Verification using GFCC,” International Journal of Computer Sciences and Engineering, Vol.3, Issue.11, pp.63-65, 2015.

MLA Style Citation: Sukhandeep Kaur , Kanwalvir Singh Dhindsa "Neural Network Based Speaker Verification using GFCC." International Journal of Computer Sciences and Engineering 3.11 (2015): 63-65.

APA Style Citation: Sukhandeep Kaur , Kanwalvir Singh Dhindsa, (2015). Neural Network Based Speaker Verification using GFCC. International Journal of Computer Sciences and Engineering, 3(11), 63-65.

BibTex Style Citation:
@article{Kaur_2015,
author = {Sukhandeep Kaur , Kanwalvir Singh Dhindsa},
title = {Neural Network Based Speaker Verification using GFCC},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {11 2015},
volume = {3},
Issue = {11},
month = {11},
year = {2015},
issn = {2347-2693},
pages = {63-65},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=727},
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=727
TI - Neural Network Based Speaker Verification using GFCC
T2 - International Journal of Computer Sciences and Engineering
AU - Sukhandeep Kaur , Kanwalvir Singh Dhindsa
PY - 2015
DA - 2015/11/30
PB - IJCSE, Indore, INDIA
SP - 63-65
IS - 11
VL - 3
SN - 2347-2693
ER -

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Abstract

Speaker confirmation is feasible method of controlling access to computer and communication networks. Speakers resonance is different due to physiological differences such as vocal tract size, larynx size and other voice produce organs, and speaking manner differences such as accent and often used words. The task of automatic speaker identification is to identify the underlying speaker or confirm the claimed speaker from a sound recording, by exploiting these differences. This paper introduce the important concepts of speaker confirmation for security system.

Key-Words / Index Term

Gaussian Mixture Model, Finite Impulse Response, Artificial Neural Network, Gaussian

References

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