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Reseach Article

Improved Dynamic Speaker Recognition System using NLMS Adaptive Filter

by P. Hema Kumar, V. Srinivas, T. Madhu
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 148 - Number 10
Year of Publication: 2016
Authors: P. Hema Kumar, V. Srinivas, T. Madhu
10.5120/ijca2016911278

P. Hema Kumar, V. Srinivas, T. Madhu . Improved Dynamic Speaker Recognition System using NLMS Adaptive Filter. International Journal of Computer Applications. 148, 10 ( Aug 2016), 39-43. DOI=10.5120/ijca2016911278

@article{ 10.5120/ijca2016911278,
author = { P. Hema Kumar, V. Srinivas, T. Madhu },
title = { Improved Dynamic Speaker Recognition System using NLMS Adaptive Filter },
journal = { International Journal of Computer Applications },
issue_date = { Aug 2016 },
volume = { 148 },
number = { 10 },
month = { Aug },
year = { 2016 },
issn = { 0975-8887 },
pages = { 39-43 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume148/number10/25796-2016911278/ },
doi = { 10.5120/ijca2016911278 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:53:01.845621+05:30
%A P. Hema Kumar
%A V. Srinivas
%A T. Madhu
%T Improved Dynamic Speaker Recognition System using NLMS Adaptive Filter
%J International Journal of Computer Applications
%@ 0975-8887
%V 148
%N 10
%P 39-43
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In a controlled environment, we can implement a speaker recognition system using MFCC and Vector Quantization. So, the main objective of this paper is to develop a speaker recognition system using MFCC and Vector Quantization(VQ) in a noisy environment, when the input speech utterance is given through a microphone. Normalised Least Mean Square Adaptive(NLMS) Filter is used to improve the performance of the system in noisy environment. So the NLMS Adaptive filter is used to reduce the background noise from input speech signal and then the filtered signal is given to the Feature Extraction phase. For implementation simplicity, it is developed as Text- Dependent Speaker Recognition System with 10 speakers, each speaker locally recorded database is used for training. The performance of the proposed system tested in noisy environment with and without using the NLMS adaptive filter and improved recognition evaluated using Equal Error Rate (EER).

References
  1. Salina Abdul Samad,Aini Hussain, Khairul Anuar Ishak “ Improved Hybrid Speaker Verification in Noisy Environments Using Least Mean -Square Adaptive Filters”.
  2. Roshny Jose George “Design Of An Adaptive Filtering Algorithm For Noise Cancellation” International Research Journal of Engineering and Technology (IRJET), Volume: 02 Issue: 04 | July-2015.
  3. John Creighton and R.Doraiswami “Real Time Implementation Of an Adaptive Filter For Speech Enhancement” 2004 IEEE.
  4. Geeta Nijhawan, Dr. M.K Soni ” Speaker Recognition Using MFCC and Vector Quantisation” Int. J. on Recent Trends in Engineering and Technology, Vol. 11, No. 1, July 2014.
  5. Signal Enhancement Using LMS and Normalized LMS - MATLAB & Simulink - MathWorks India.
  6. Michael Lutter “Mel Frequency Cepstral Coefficients (feature extraction/ Mfcc)” The Speech Recognition Wiki25 November 2014.
  7. J.P.Campbell, “Speaker Recognition: A Tutorial”, Proc. Of the IEEE, Vol 85,No. 9, September 1997,pp. 1437-1462.
  8. Vibha Tiwari “MFCC and its applications in speaker recognition” International Journal on Emerging Technologies 1(1): 19-22(2010) ISSN : 0975-8364.
  9. Prof. Ch.Srinivasa Kumar, Dr. P. Mallikarjuna Rao “Design Of An Automatic Speaker Recognition System Using MFCC, Vector Quantization And LBG Algorithm” International Journal on Computer Science and Engineering (IJCSE), Vol. 3 No. 8 August 2011, ISSN : 0975-3397.
  10. Prof. Vaishali M. Karne, Prof. Akhilesh Singh Thakur , Dr. Vibha Tiwari “ Least Mean Square (LMS) Adaptive Filter For Noise Cancellation” International Journal of Application or Innovation in Engineering & Management (IJAIEM) , ISSN 2319 – 4847.
  11. Dr. H B Kekre1, Dr. V A Bharadi, A R Sawant “Speaker Recognition using Vector Quantization by MFCC and KMCG Clustering Algorithm” 2012 International Conference on Communication, Information & Computing Technology (ICCICT), Oct. 19-20.
  12. Sheng Zhang, student Member, IEEE, Jiashu Zhang, and Hongyu Han ”Robust Variable Step-Size Decorrelation Normalized Least-Mean Square Algorithm and its Application to Acoustic Echo Cancellation” IEEE/ACM Transactions on Audio, Speech, and Language Processing.
  13. A. Srinivasan “Speaker Identification and Verification using Vector Quantization and Mel Frequency Cepstral Coefficients” Research Journal of Applied Sciences, Engineering and Technology 4(1): 33-40, 2012 ,ISSN: 2040-7467.
  14. Jayant M. Naik “Speaker Verification: A Tutorial” January 1990 - IEEE Communications Magazine.
  15. Dr. Sadaoki Furui “Speaker recognition” Sada oki Furui (2008 ), Scholarpedia ,3 (4 ):3 7 1 5
  16. “Performance Analysis and Enhancementsof Adaptive Algorithms and Their Applications.” A thesis submitted to the Nanyang Technological University by Shengkui zhao.
  17. “A Novel Windowing Technique for Efficient Computation of MFCC for Speaker Recognition” Md Sahidullah, Student Member, IEEE, Goutam Saha, Member, IEEE.
  18. Paresh M. Chauhan , Nikita P. Desai “Mel Frequency Cepstral Coefficients (MFCC) Based Speaker Identification in Noisy Environment Using Wiener Filter”
  19. Jorge MARTINEZ*, Hector PEREZ, Enrique ESCAMILLA , Masahisa Mabo SUZUKI “ Speaker recognition using Mel Frequency Cepstral Coefficients (MFCC) and Vector Quantization (VQ) Techniques” -1-61284-1325-5/12/$26.00 ©2012 IEEE.
  20. Jyh-Min CHENG and Hsiao-Chuan WANG “A Method Of Estimating The Equal Error Rate For Automatic Speaker Verification” 0-7803-8678-7/04/$20.00 82004 IEEE.
  21. JYOTI DHIMAN, SHADAB AHMAD, KULDEEP GULIA “ Comparison between Adaptive filter Algorithms (LMS, NLMS and RLS)” International Journal of Science, Engineering and Technology Research (IJSETR) Volume 2, Issue 5, May 2013, ISSN: 2278 – 7798.
  22. “Technical Document About FAR, FRR and EER Version 1.0” © 2004 by SYRIS Technology Corp.
Index Terms

Computer Science
Information Sciences

Keywords

Least Mean Square NLMS Adaptive Filter Vector Quantization Equal Error Rate (EER).