CFP last date
22 April 2024
Call for Paper
May Edition
IJCA solicits high quality original research papers for the upcoming May edition of the journal. The last date of research paper submission is 22 April 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

Robust Speaker Identification using Denoised Wave Atom and GMM

by Mohammed Alhanjouri, Mohammed A. H. Lubbad, Mahmoud Z. Alkurdi
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 67 - Number 5
Year of Publication: 2013
Authors: Mohammed Alhanjouri, Mohammed A. H. Lubbad, Mahmoud Z. Alkurdi
10.5120/11391-6687

Mohammed Alhanjouri, Mohammed A. H. Lubbad, Mahmoud Z. Alkurdi . Robust Speaker Identification using Denoised Wave Atom and GMM. International Journal of Computer Applications. 67, 5 ( April 2013), 17-23. DOI=10.5120/11391-6687

@article{ 10.5120/11391-6687,
author = { Mohammed Alhanjouri, Mohammed A. H. Lubbad, Mahmoud Z. Alkurdi },
title = { Robust Speaker Identification using Denoised Wave Atom and GMM },
journal = { International Journal of Computer Applications },
issue_date = { April 2013 },
volume = { 67 },
number = { 5 },
month = { April },
year = { 2013 },
issn = { 0975-8887 },
pages = { 17-23 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume67/number5/11391-6687/ },
doi = { 10.5120/11391-6687 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:23:52.178337+05:30
%A Mohammed Alhanjouri
%A Mohammed A. H. Lubbad
%A Mahmoud Z. Alkurdi
%T Robust Speaker Identification using Denoised Wave Atom and GMM
%J International Journal of Computer Applications
%@ 0975-8887
%V 67
%N 5
%P 17-23
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper introduces the use of Wave atom transformation as an efficient speech noise filter with Gaussian mixture models (GMM) for robust text-independent speaker identification. The individual Gaussian components of a GMM are shown to represent some general speaker identity. The focus of this work is on applications which require high robustness of noise and high identification rates using short utterance from noisy (Natural Noise) numerical speech and alphabetical words speech. A Full experimental evaluation of the Gaussian mixture speaker model is conducted on a 10 speakers. The experiments examine algorithmic issues (Preprocessing (Denoising by Wave Atom), Feature Extraction (MFCC), Training using GMM, Pattern Matching (Maximum likelihood estimation ML), Decision Rule (Expectation Maximization EM)). The Proposed algorithm attains 95% identification accuracy using 5 seconds noisy speech utterances without Wave atom preprocessing it attains 90% identification accuracy using 5 seconds noisy speech utterances. Proposed denoisy algorithm increases the identification ratio by 5% for noisy speech signals, this ratio is interesting enough.

References
  1. D. Reynolds, R. Rose, "Robust text-independent speaker identification using Gaussian mixture speaker models", IEEE Trans. Speech Audio Process. , vol. 3, no. 1, pp. 72-83, Jan. 1995.
  2. N M Ramaligeswararao, Dr. V Sailaja and Dr. K. Srinivasa Rao," Text Independent Speaker Identification using Integrated Independent Component Analysis with Generalized Gaussian Mixture Model" (IJACSA) International Journal of Advanced Computer Science and Applications,Vol. 2, No. 12, 2011
  3. Leena mary and yegnanaryana(2008), "Extraction and representation of prosodic feature for language and speaker recognition" SPEECH COMMUNICATION 50(10):782-796. Michael Charles (1999), "Orthogonal GMM in Speaker Recognition," Proceedings of IEEE International Conference on Acoustics Speech and Signal Processing", pp. 845-848.
  4. Jyothi et al (2011), "Text independent speaker identification with finite multivariate generalized Gaussian mixture model with distant microphone speech" proceeding of the international journel of computer applications (IJCA)14(4):5-9.
  5. H. Gish et a (1985), "Investigation Of Text-dependent Speaker Identification Over Telephone Channels," in Proc. IEEE ICASSP, pp. 379-382.
  6. Ning Wang , P. C. Ching(2011), "Nengheng Zheng, and Tan Lee, "Robust Speaker Recognition Using Denoised Vocal Source and Vocal Tract Features speaker verification," IEEE Transaction on Audio Speech and Language processing, Vol. 1, No. 2, pp. 25-35.
  7. Nitin Trivedi, Dr. Vikesh Kumar, Saurabh Singh, Sachin Ahuja, Raman Chadha, 2011. Speech Recognition by Wavelet Analysis, International Journal of Computer Applications (0975 – 8887) Volume 15– No. 8.
  8. B. Jawerth and W. Sweldens, 1994, "An overview of wavelet based multiresolution analysis," SIAM Review, vol. 36, no. 3, pp. 377–412.
  9. D. L. Donoho, I. M. Johnstone, G. Kerkyacharian, and D. Picard, 1995 "Wavelet shrinkage: Asymptotic?," Journal of the Royal Statistics Society, vol. 57, pp. 301–369.
  10. S. Mallat, 1999, A wavelet Tour of Signal Processing, Second Edition, Academic Press, Orlando-SanDiego.
  11. J. P. Antoine and R. Murenzi, 1996, Two-dimensional directional wavelets and the scale-angle representation, Sig. Process. , vol. 52, pp. 259-281,
  12. Demanety and L. Ying, 2007. Wave atoms and sparsely of oscillatory patterns, appear in Appl. Comput. Harm. Anal, VoL 23, Issue 3, pp. 368-387.
Index Terms

Computer Science
Information Sciences

Keywords

Wave Atom Transformation MFCC Gaussian Mixture Model GMM Wavelet Transformation Speaker recognition

Powered by PhDFocusTM