Abstract
This paper reports a study on static pattern classification technique using Support Vector Machine based Decision Directed Acyclic Graph (DDAG) algorithm for the classification of Malayalam Consonant – Vowel (CV) speech unit utterances. Wavelet Transform (WT) based Normalized Wavelet Hybrid Features (NWHF) by combining both Classical Wavelet Decomposition (CWD) and Wavelet Packet Decomposition (WPD) along with z – score normalization are used to evaluate the performance of the present classifier in speaker independent environment. From the experimental study it is reported that present DDAGSVM algorithms perform well for Malayalam CV speech unit recognition compared to ANN and k – NN in additive noisy condition.
Keywords
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, New York (1995)
Jain, A.K., Duin, R.P.W., Mao, J.: Statisitical Pattern Recognition: A Review. IEEE Trans. Pattern Analysis and Machine Intelligence 22(1), 4–37 (2000)
Hearst, M.A.: Support Vector Machines. IEEE Intelligent Systems, 18–28 (1998)
Platt, J., Cristianini, N., Sbawe-Taylor, J.: Large Margin DAGS for Multiclass Classification. In: Advance in Neural Information Processing System, vol. 12, pp. 547–553 (1999)
Mallat, S.: A wavelet Tour of Signal Processing, The Sparse Way. Academic, NewYork (2009)
Soman, K.P., Ramachandran, K.I.: Insight into Wavelets, from Theory to Practice. Prentice Hall of India (2005)
Mallat, S.: A Theory for Multi resolution Signal Decomposition: The Wavelet Representation. IEEE Trans. on Pattern Analysis and Machine Intelligence 11, 674–693 (1989)
Vetterly, M., Herley, C.: Wavelets and Filter banks: Theory and Design. IEEE Trans. on Signal Processing 40(9), 2207–2232 (1992)
Shensa, M.J.: Affine Wavelets: Wedding the Atrous and mallat Algorithms. IEEE Trans. on Signal Processing 40, 2464–2482 (1992)
Mallat, S.: Multi frequency Channel Decomposition of Images and Wavelet Models. IEEE. Trans on Acoustics, Speech and Signal Processing 37, 2091–2110 (1989)
Daubechies, I.: Ten Lectures on Wavelets. Soc. Appl. Math., Philadelphia (1992)
Lindsay, R.W., Percival, D.B., Andrew Rothrock, D.: The Discrete Wavelet Transform and the scale analysis of the surface properties of Sea Ice. IEEE Trans. on Geo Science and Remote Sensing 34(3), 771–787 (1996)
Sheng, Y.: Wavelet Transform-The Transforms and Application Handbook. CRC Press LLC (2000)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. A Wiley-Inter Science Publications (2006)
Tan, Y., Wang, J.: A Support Vector Machine with a Hybrid Kernel and Minimal Vapnik – Chervonenkins Dimension. IEEE Trans. on Knowledge and Data Engineering 10(4), 385–395 (2004)
Vapnik, V.N.: An Overview of Statistical Learning Theory. IEEE Trans. on Neural Networks 10(5), 988–999 (1999)
Gupta, R., Mittal, A., Singh, K.: A time Series based Feature Extraction Approach for Prediction of Protein Structured Class. EURASIP Journal on Bioinformatics and System Biology (2008)
Osuna, E., Freund, R., Girosi, F.: Training Support Vector Machines: An Application to Face Detection. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 17–19 (1997)
Pontil, M., Verri, A.: Support Vector Machines for 3D Object Recognition. IEEE Trans. Pattern Analysis and Machine Intelligence 20(6), 637–646 (1998)
Thasleema, T.M., Narayanan, N.K.: Normalized Wavelet Hybrid Feature for Consonant Classification in Noisy Environments. In: Aswatha Kumar, M., Selvarani, R., Suresh Kumar, T.V. (eds.) Proceedings of ICAdC. AISC, vol. 174, pp. 285–290. Springer, Heidelberg (2013)
Kwon, O.-W., Chan, K., Lee, T.-W.: Speech Feature Analysis using Variatioanl Bayesian PCA. IEEE Signal Proc. Letters 10(5) (2003)
Samouelian, A.: Knowledge based Approach to Consonant Recognition. In: IEEE International Conf. on ASSP, pp. 77–80 (1994)
Cutajar, M., Gatt, E., Grech, I., Casha, O., Micallef, J.: Neural Network Architectures for Speaker Independent Phoneme Recognition. In: 7th International Symposium on Image and Signal Processing Analysis, Croatia, pp. 90–95 (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Thasleema, T.M., Narayanan, N.K. (2012). Wavelet Transform Based Consonant - Vowel (CV) Classification Using Support Vector Machines. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7664. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34481-7_31
Download citation
DOI: https://doi.org/10.1007/978-3-642-34481-7_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34480-0
Online ISBN: 978-3-642-34481-7
eBook Packages: Computer ScienceComputer Science (R0)