Open Access   Article Go Back

Speaker Recognition System Techniques and Applications

Sukhandeep Kaur1 , Anwalvir Singh Dhindsa2

Section:Review Paper, Product Type: Journal Paper
Volume-3 , Issue-8 , Page no. 101-104, Aug-2015

Online published on Aug 31, 2015

Copyright © Sukhandeep Kaur , Anwalvir 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.

View this paper at   Google Scholar | DPI Digital Library

XML View
PDF Download

How to Cite this Paper

IEEE Style Citation: Sukhandeep Kaur , Anwalvir Singh Dhindsa , “Speaker Recognition System Techniques and Applications,” International Journal of Computer Sciences and Engineering, Vol.3, Issue.8, pp.101-104, 2015.

MLA Style Citation: Sukhandeep Kaur , Anwalvir Singh Dhindsa "Speaker Recognition System Techniques and Applications." International Journal of Computer Sciences and Engineering 3.8 (2015): 101-104.

APA Style Citation: Sukhandeep Kaur , Anwalvir Singh Dhindsa , (2015). Speaker Recognition System Techniques and Applications. International Journal of Computer Sciences and Engineering, 3(8), 101-104.

BibTex Style Citation:
@article{Kaur_2015,
author = {Sukhandeep Kaur , Anwalvir Singh Dhindsa },
title = {Speaker Recognition System Techniques and Applications},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {8 2015},
volume = {3},
Issue = {8},
month = {8},
year = {2015},
issn = {2347-2693},
pages = {101-104},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=617},
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=617
TI - Speaker Recognition System Techniques and Applications
T2 - International Journal of Computer Sciences and Engineering
AU - Sukhandeep Kaur , Anwalvir Singh Dhindsa
PY - 2015
DA - 2015/08/31
PB - IJCSE, Indore, INDIA
SP - 101-104
IS - 8
VL - 3
SN - 2347-2693
ER -

VIEWS PDF XML
2604 2507 downloads 2452 downloads
  
  
           

Abstract

Speaker verification is feasible method of controlling access to computer and communication network. It is an automatic process that uses human voice characteristics obtained from a recorded speech signal, as the biometric measurements to verify claimed identity of speaker. It can be classified into two categories, text–dependent and text-independent system. This paper introduces the fundamental concepts of speaker verification for security system. It focuses on techniques and their unique features.

Key-Words / Index Term

Speaker identification, Gamma tone frequency cepstral coefficient, Mel frequency cepstral coefficient

References

[1] Wu Ju, “Speaker Recognition System Based on Mfcc and Schmm.” Symposium on ICT and Energy Efficiency and Workshop on Information Theory and Security,2005, Dublin Ireland, pp. 88 – 92.
[2] R.Mukherje, I.Tanmoy, and R.Sankar, "Text dependent speaker recognition using shifted MFCC." Southeast on,2013, Proceedings ofIEEE,Orlando,FL USA,pp.1-4.
[3] D.A.Reynolds, “Speaker Identification and Verification Using Gaussian Mixture Speaker Models,” Speech Communication, Vol.17,1995, No. 1-2, pp. 91-108.
[4] W. Junqin, and Y.Junjun, “An Improved Arithmetic of Mfcc in Speech Recognitions System.” Electronics, Communications and Control (ICECC), International Conference on.IEEE,2011, ZhejiangChina,pp.719-722.
[5] U.Shrawankar, and V.M. Thakare, “Techniques for Feature Extraction In Speech Recognition System: A Comparative Study.”
International Journal Of Computer Applications In Engineering, Technology And Sciences,Vol. 2, No. 5,2010, pp. 412-418.
[6] H.Hermansky, “Perceptual Linear Predictive (PLP) Analysis of Speech.” Speech Technology Laboratory, Division of Panasonic Technologies, Vol. 87, No. 4, 1990,pp. 1738-1752.
[7] N. Wang, and P.C.Ching, “Robust Speaker Recognition Using Denoised Vocal Source and Vocal Tract Features speaker verification,” IEEE Transaction on Audio Speech and Language processing, Vol. 19, No. 1,2011, pp. 196-205.
[8] M.I. Faraj, and J.Bigun,"Synergy of lip-motion and acoustic features in biometric speech andspeaker recognition."Computers, IEEE Transactions on computers Vol.56, No.9,2007, pp. 1169-1175.
[9]M.S. Sinith,A.Salim,K. GowriShankar,S. Narayanan, and V. Soman,"A novel method for Text-Independent speaker identification using MFCC and GMM."Audio Language and Image Processing (ICALIP), International Conference on. IEEE,Shanghai,2010, pp.292-296.
[10] A.Solomon off,. "Channel compensation for SVM speaker recognition." Odyssey.Vol. 4,2004, pp.57-62.
[11] R.Collobert, andS.Bengio, "SVMTorch: Support vector machines for large-scale regression problems." The Journal of Machine Learning Research ,No.1,2001, pp. 143-160.
[12] D.E.Sturim, and D.A. Reynolds, "Speaker Adaptive Cohort Selection for Tnorm in Text-Independent Speaker Verification."ICASSP ,No.1,USA ,2005, pp.741-744.
[13] G.S.V.S.Sivaram, Thomas, and H.Hermansky, “Mixture of Auto-Associative Neural Networks for Speaker Verification,”INTERSPEECH, Baltimore, USA,2011, pp. 2381-2384.
[14] S.Gfroerer, “Auditory instrumental forensic speaker recognition” Proceedings of Eurospeech,Geneva, 2003,pp. 705–708.
[15] H.R.Bolt,andF.S.Cooper, “Identification of a Speaker by Speech Spectrograms,” American Association for the Advancement in Science, Science, Vol. 166, 1969.pp. 338–344.
[16] D.Charlet, D.Jouvet, and O.Collin, “An Alternative Normalization Scheme in HMM-based Text-dependent Speaker Verification,” Speech Communication, Vol. 31,2000, pp. 113-20.
[17] T.Dutta, “Dynamic Time Warping Based Approach to Text-Dependent Speaker Identification Using Spectrograms,” Congress on Image and Signal Processing, Vol. 2,No.8,2008, pp. 354-60.
[18] M.Ben, M.Betser, F.Bimbot, and G.Gravier, "Speaker diarization using bottom-up clustering based on a parameter-derived distance between adapted GMMs." 2004,Proc. ICSLP, France.
[19] A.A.M. AbushariahT.S,Gunawan, O.O. Khalifa, and M.A.M. Abushariah, “Voice based automatic person identification system using vector quantization”. In Computer and Communication Engineering (ICCCE), International Conference, Kuala Lumpur, 2012,pp. 549–554.
[20] S.Agrawal, A.Shruti,and A.C.Krishna , “Prosodic Features Based Text Dependent Speaker Recognition Using Machine Learning Algorithms,” International Journal of Engineering Science and Technology, Vol. 2,No.10,2010, pp. 5150-5157.