Multi Resolution Analysis for Consonant Classification in Noisy Environments

Full Text (PDF, 284KB), PP.15-23

Views: 0 Downloads: 0


T M Thasleema 1,* N K Narayanan 1

1. Department of Information Technology, Kannur University, Kerala, India, 670567

* Corresponding author.


Received: 3 May 2012 / Revised: 31 May 2012 / Accepted: 28 Jun. 2012 / Published: 8 Aug. 2012

Index Terms

Wavelet Transform, Normalized Wavelet Hybrid Features, Daubechies Wavelet, k – Nearest Neighborhood, Artificial Neural Network


This paper investigates on the use of Wavelet Transform (WT) to model and recognize the utterances of Consonant – Vowel (CV) speech units in noisy environments. The peculiarity of the proposed method lies in the fact that using WT, non stationary nature of the speech signal can be accurately considered. A hybrid feature extraction namely Normalized Wavelet Hybrid Feature (NWHF) using the combination of Classical Wavelet Decomposition (CWD) and Wavelet Packet Decomposition (WPD) along with z-score normalization technique are studied here. CV speech unit recognition tasks performed for both noisy and clean speech units using Artificial Neural Network (ANN) and k – Nearest Neighborhood (k – NN) are also presented. The result indicates the robustness of the proposed technique based on WT in additive noisy condition.

Cite This Paper

T M Thasleema, N K Narayanan,"Multi Resolution Analysis for Consonant Classification in Noisy Environments", IJIGSP, vol.4, no.8, pp.15-23, 2012. DOI: 10.5815/ijigsp.2012.08.03 


[1]Forgie J W and Forgie C D, “Results obtained from a Vowel Recognition Computer Program”, Journal of Acoustical Society of America, Vol. 31, pp. 1480 – 1489, 1959. 

[2]Reddy D R, “An approach to Computer speech recognition by Direct Analysis of the speech wave”, Computer Science Dept., Stanford University Technical Report No. C549, 1966. 

[3]Gold B and Morgan N, “Speech and Audio Signal Processing”, New York: John Wiley & Sons Inc., 2000.

[4]Peter Ladefoged, “ Vowels and Consonants- an Introduction to the Sounds of Language”, BlackWell Publishing, 2004. 

[5]Danial Jurafsky, James H Martin, “An Introd uction to Natural Language Processing, Computational Linguistics, and Speech Recognition”, Pearson Education, 2004

[6]Hem P Ramachandran. (2008). Encyclopedia of Language and Linguistics. Pergamon Press: Oxford.

[7]H. Bourlard and S. Dupont, “Subband-based speech recognition,” in Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSPSignal Processing (ICASSP ’97), vol. 2, pp. 1251–1254, Munich, Germany, April 1997.

[8]M. Gupta and A. Gilbert, “Robust speech recognition using wavelet coefficient features,” in Proceedings of IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU ’01), pp. 445–448, Madonna di Campiglio, Trento, Italy, December 2001.

[9]R. Sarikaya, B. L. Pellom, and J. H. L. Hansen, “Wavelet packet transform features with application to speaker Identification,” in Proceedings of the 3rd IEEE Nordic Signal Processing Symposium(NORSIG ’98), pp. 81–84, Vigsø, Denmark, June 1998.

[10]A Grossman, J Morlet & P Gaoupillaud, “Cycle octave and related transforms in seismic signal Analysis”, Geoexploration, Vol. 23, pp. 85-102, 1984.

[11]C torrence and G P Compo, “A practical guide to Wavelet Analysis”, Bull. Amer. Meteorological. Soc., Vol. 79(1), pp. 61 – 78 , 1998.

[12]Hongyu Liao, Mrinal Kr and Bruce F Cockburn, “Efficient Architectures for 1 – D and 2 – D Lifting Based Wavelet Transform”, IEEE Trans. on Signal Processing, Vol. 52(5), pp. 1315 – 1326, 2004.

[13]S Mallat, “A wavelet Tour of Signal Processing, The Sparse Way”, NewYork : Academic, 2009.

[14]K P Soman and K I Ramachandran, “Insight into Wavelets, from Theory to Practice”, Prentice Hall of India, 2005.

[15]S Mallat, “ A Theory for Multi resolution Signal Decomposition : The Wavelet Representation”, IEEE Trans. on Pattern Analysis and machine Intelligence, Vol. 11, pp. 674 – 693 , 1989.

[16]R Kronland – Martinet, J Morlet and A Grossman, “ Analysis of Sound Patterns through Wavelet Transforms”, Int. Journal of Pattern Recognition, Artificial Intelligence, Vol. 1(2), pp. 273 – 302 , 1987.

[17]R R Coifman and M Maggioni, “Diffusion Wavelets”, Appl. Computat. Harmon. Anal, Vol. 21(1), pp. 53 – 94 , 2006.

[18]D K Hammond, P Vandergheynst and R Gribonval, “Wavelets on Graph via Spectral Graph Theory, Appl. Computat. Harmon. Anal, 2010.

[19]Kadambe S and Boudreaux – Bartels G F, “Application of the Wavelet Transform for Pitch Detection of Speech Signal”, IEEE Trans. on Information Theory, Vol. 38, pp. 917 – 924 , 1992.

[20]O Farook, S Dutta and M C Shrotriya, “Wavelet Sub band Based Temporal Features for Robust Hindi Phoneme Recognition”, Int. Journal of Wavelets, Multi resolution and Information Processing (IJWMIP), Vol. 8(6), pp. 847 – 859, 2010.

[21]M Vetterly and C Herley, “Wavelets and Filter banks : Theory and Design”, IEEE Trans. on Signal Processing, Vol. 40(9), pp. 2207 – 2232, 1992.

[22]M J Shensa, “Affine Wavelets: Wedding the Atrous and mallat Algorithms”, IEEE Trans. on Signal Processing, Vol 40, pp. 2464 – 2482, 1992.

[23]S Mallat, “ Multi frequency Channel Decomposition of Images and Wavelet Models”, IEEE. Trans on Acoustics, Speech and Signal Processing, Vol. 37, pp. 2091 – 2110 , 1989.

[24]I Daubechies, “Ten Lectures on Wavelets”, Philadelphia, PA:Soc. Appl. Math, 1992.

[25]Ronald W Lindsay, Donald B Percival and D Andrew Rothrock, “The Discrete Wavelet Transform and the scale analysis of the surface properties of Sea Ice”, IEEE Trans. on Geo Science and Remote Sensing, Vol. 34(3), pp. 771 – 787 , 1996.

[26]P. J. Burt and E. H. Edelson, “The Lapalaciam Pyramid as a Compact Image Code”, IEEE Trans. on Communications, Vol. COM-31, pp. 532 – 540, 1983.

[27]J. Crowley, “A Representation for Visual Information”, Robotic. Inst. Carnegie-Mellon University, Tech. Rep. CMU – RI – TR – 82 – 7, 1987.

[28]Yunlong Sheng, “Wavelet Transform-The Transforms and Application Handbook”, CRC Press LLC, 2000.

[29]Duda . R. O and Hart P. E., “ Pattern Classification and Scene Analysis”, Wiley Inter Science, New York, 1973

[30]Duda R O, Hart P E and David G. Stork ,“Pattern Classification” A Wiley-Inter Science Publications, 2006.

[31]Tou J. T and Gonzalez R. C, “Pattern Recognition Principles”, Addison – Wesley, London, 1974.

[32]Friedmen M and Kandel A, “Introduction to Pattern Recognition: Statistical, Structural, Neural and Fuzzy Logic Approach”, World Scientific, 1999.

[33]Cover T M & Hart P E, “Nearest Neighbor Pattern Classification”, IEEE trans. on Information Theory, Vol. 13 (1), pp. 21 - 27 , 1967.

[34]Min-Chun Yu, “ Multi – Criteria ABC analysis using artificial – intelligence based classification techniques”, Elsevier – Expert Systems With Applications, Vol. 38, pp. 3416 – 3421, 2011.

[35]Hand D J, “Discrimination and classification”, NewYork, Wiley, 1981.

[36]Ray A. K and Chatterjee B, “Design of a Nearest Neighbor Classifier System for Bengali Character Recognition”, Journal of Inst. Elec. Telecom. Eng, Vol. 30, pp 226 – 229, 1984.

[37]Zhang. B and Srihari S N, “Fast k – Nearest Neighbor using Cluster Based Trees”, IEEE trans. on Pattern Analysis and Machine Intelligence, Vol. 26(4), pp. 525 – 528 , 2004.

[38]Pernkopf. F, “Bayesian Network Classifiers versus selective k –NN Classifier”, Pattern Recognition, Vol. 38, pp. 1 – 10, 2005.

[39]Ripley. B. D, “Pattern Recognition and Neural Networks”, Cambridge University Press, 1996.

[40]Haykin S, “Neural Networks: A Comprehensive Foundation”, Prentice Hall of India Pvt. Ltd, 2004.

[41]Simpson. P. K, “Artificial Neural Systems”, Pergamon Press, 1990.

[42]W S McCullough & W H Pitts, “ A logical calculus of ideas immanent in nervous activity”, Bull Math Biophysics, Vol 5, pp. 115 – 133 , 1943.

[43]R P Lippmann, “An introduction to computing with Neural Nets”, IEEE Trans. Acoustic Speech & Signal Processing Magazine., Vol 61., pp 4 – 22 ., 1987.

[44]T Kohonen, “An introduction to Neural Computing, Neural Networks, 1988.

[45]Sankar K Pal & Sushmita Mitra, “Multilayer perceptron, Fuzzy sets, and Classification”, IEEE Trans. Neural Networks.,Vol 3(5)., 1992.