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Wavelet Transform Based Consonant - Vowel (CV) Classification Using Support Vector Machines

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Neural Information Processing (ICONIP 2012)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7664))

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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.

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Author information

Authors and Affiliations

  1. Dept. of Information Technology, Kannur Univeristy, Kerala, India, 670567

    T. M. Thasleema & N. K. Narayanan

Authors
  1. T. M. Thasleema
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  2. N. K. Narayanan
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Editor information

Editors and Affiliations

  1. Texas A&M University at Qatar, Education City, P.O. Box 23874, Doha, Qatar

    Tingwen Huang

  2. Department of Control Science and Engineering, Huazhong University of Science and Technology, 1037 Luoyu Road, 430074, Wuhan, Hubei, China

    Zhigang Zeng

  3. College of Computer Science, Chongqing University, 174 Shazhengjie Street, 400044, Chongqing, China

    Chuandong Li

  4. Department of Electronic Engineering, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon, Hong Kong, China

    Chi Sing Leung

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© 2012 Springer-Verlag Berlin Heidelberg

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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

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  • 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)

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