Speaker Recognition System Techniques and Applications
Keywords:
Speaker Identification, Gamma Tone Frequency Cepstral Coefficient, Mel Frequency Cepstral CoefficientAbstract
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.
References
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.
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.
D.A.Reynolds, “Speaker Identification and Verification Using Gaussian Mixture Speaker Models,” Speech Communication, Vol.17,1995, No. 1-2, pp. 91-108.
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.
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.
H.Hermansky, “Perceptual Linear Predictive (PLP) Analysis of Speech.” Speech Technology Laboratory, Division of Panasonic Technologies, Vol. 87, No. 4, 1990,pp. 1738-1752.
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.
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.
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.
A.Solomon off,. "Channel compensation for SVM speaker recognition." Odyssey.Vol. 4,2004, pp.57-62.
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.
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.
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.
S.Gfroerer, “Auditory instrumental forensic speaker recognition” Proceedings of Eurospeech,Geneva, 2003,pp. 705–708.
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.
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.
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.
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.
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.
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.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.
