Share This Article:

Identification of Noisy Utterance Speech Signal using GA-Based Optimized 2D-MFCC Method and a Bispectrum Analysis

Abstract Full-Text HTML Download Download as PDF (Size:229KB) PP. 193-199
DOI: 10.4236/jsea.2012.512B037    3,257 Downloads   4,526 Views  


One-dimensional Mel-Frequency Cepstrum Coefficients (1D-MFCC) in conjunction with a power spectrum analysis method is usually used as a feature extraction in a speaker identification system. However, as this one dimensional feature extraction subsystem shows low recognition rate for identifying an utterance speech signal under harsh noise conditions, we have developed a speaker identification system based on two-dimensional Bispectrum data that was theoretically more robust to the addition of Gaussian noise. As the processing sequence of ID-MFCC method could not be directly used for processing the two-dimensional Bispectrum data, in this paper we proposed a 2D-MFCC method as an extension of the 1D-MFCC method and the optimization of the 2D filter design using Genetic Algorithms. By using the 2D-MFCC method with the Bispectrum analysis method as the feature extraction technique, we then used Hidden Markov Model as the pattern classifier. In this paper, we have experimentally shows our developed methods for identifying an utterance speech signal buried with various levels of noise. Experimental result shows that the 2D-MFCC method without GA optimization has a comparable high recognition rate with that of 1D-MFCC method for utterance signal without noise addition. However, when the utterance signal is buried with Gaussian noises, the developed 2D-MFCC shows higher recognition capability, especially, when the 2D-MFCC optimized by Genetics Algorithms is utilized.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

B. Kusumoputro, A. Buono and L. Na, "Identification of Noisy Utterance Speech Signal using GA-Based Optimized 2D-MFCC Method and a Bispectrum Analysis," Journal of Software Engineering and Applications, Vol. 5 No. 12B, 2012, pp. 193-199. doi: 10.4236/jsea.2012.512B037.


[1] Z. Li, J. Sun, J. Han, f. Chu and Y. He, Parametric bispectrum analysis of cracked rotor based on blind identification of time series models, IEEE Proceeding of Intelligent Control and Automation, Vol. 2, 2006, pp.5729-5733.
[2] I. Jouny, E.D. Garber and R.L. Moses, Radar target identification using the bispec-trum: a comparative study, IEEE Trans. Aerospace and Electronic Systems, Vol. 31, No. 1, 1995, pp. 69-77.
[3] E.S. Fonseca, R.C. Guido, A.C. Silvestre and J.C. Pereira, Discrete wavelet transform and support vector machine applied to pathological voice signals identification, IEEE Proceeding of Interna-tional Symposium on Multimedia, 2005
[4] Z. Wang and H. Wang, Voice identification system based on server, IEEE Proceeding of Intern. Conf. on Computer Application and System Modeling, Vol. 9, 2010, pp. 384-387.
[5] M. Abdollahi, E. Valavi and H.A. Noubari, Voice-based gender identification via multiresolution frame classification of spec-tro-temporal maps, IEEE Proceeding of Intern. Joint Conf. on Neural Networks, 2009, pp. 1-4.
[6] T.D. Ganchev, Speaker Recognition, PhD Dissertation, Wire Communications Laboratory, Department of Computer and Electrical Engineering, University of Patras Greece, 2005
[7] B. Kusumoputro, A. Tri-yanto, M.I. Fanany and W. Jatmiko, Speaker identi-fication in noisy environment using bispectrum anal-ysis and probabilistic neural networks, IEEE Pro-ceeding of Intern. Conf. on Computational Intelligence and Multimedia Application, 2001, pp.118-123.
[8] C.L. Nikeas and A.P. Petropulu, Higher order spectra analysis: A Nonlinear Signal Processing Framework, Prentice-Hall, Inc. New Jersey, 1993.
[9] T.E. Ozkurt and T. Akgul, Robust text-independent speaker identification using bispectrum slice, IEEE Proceeding of Signal Processing and Communications Applications, 2004, pp. 418-421.
[10] L. Luo and L.F. Chaparro, Para-metric identification of systems using a frequency slice of the bispectrum, IEEE Proceeding of Intern. Conf. on Acoustic, Speech and Signal Processing, Vol. 3, 1991, pp. 3481-3484
[11] L. Rabiner. A Tutorial on Hidden Markov Model and Selected Applications in Speech Recognition. Proceeding IEEE, Vol 77 No. 2. February 1989.
[12] Cornaz, C. dan U. Hunkeler. An Automatic Speaker Recognition System. Mini-Project., access : August, 15, 2008.
[13] Zbigniew M. Genetic Algorithms + Data structures = Evolution Programs, 3th Edition, Springer, 1996.

comments powered by Disqus

Copyright © 2018 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.