Skip to main content
Springer Nature Link
Log in

Wavelet Speech Feature Extraction Using Mean Best Basis Algorithm

  • Conference paper
Advances in Nonlinear Speech Processing (NOLISP 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5933))

Included in the following conference series:

Abstract

This paper presents Mean Best Basis algorithm, an extension of the well known Best Basis Wickerhouser’s method, for an adaptive wavelet decomposition of variable-length signals. A novel approach is used to obtain a decomposition tree of the wavelet-packet cosine hybrid transform for speech signal feature extraction. Obtained features are tested using the Polish language hidden Markov model phone classifier.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Datta, S., Farooq, O.: Phoneme Recognition Using Wavelet Based Features. An International Journal on Information Sciences 150 (2003)

    Google Scholar 

  2. Tan, B.T., Fu, M., Spray, A., Dermody, Ph.: The Use of Wavelet Transforms in Phoneme Recognition. In: 4th International Conference on Spoken Language Processing ICSLP (1996)

    Google Scholar 

  3. Gałka, J., Kepiński, M., Ziółko, M.: Speech Signals in Wavelet-Fourier Domain. In: 5th Open Seminar on Acoustics - Speech Analysis, Synthesis and Recognition In Technology, Linguistics And Medicine. Archives of Acoustics, vol. 28(3) (2003)

    Google Scholar 

  4. Gałka, J., Kepiński, M.: WFT context-sensitive speech signal representation. In: Kłopotek, M.A., Wierzchoń, S.T., Trojanowski, K. (eds.) IIPWM 2006. Advances in Soft Computing, pp. 97–105. Springer, Heidelberg (2006)

    Google Scholar 

  5. Ganchev, T., Siafarikas, M., Fakotakis, N.: Speaker Verification Based on Wavelet Packets. In: Sojka, P., Kopeček, I., Pala, K. (eds.) TSD 2004. LNCS (LNAI), vol. 3206, pp. 299–306. Springer, Heidelberg (2004)

    Google Scholar 

  6. Datta, S., Long, C.J.: Wavelet Based Feature Extraction for Phoneme Recognition. In: 4th International Conference on Spoken Language Processing ICSLP (1996)

    Google Scholar 

  7. Datta, S., Farooq, O.: Mel Filter-Like Admissible Wavelet Packet Structure for Speech Recognition. IEEE Signal Processing Letters 8(7), 196–198 (2001)

    Article  Google Scholar 

  8. Datta, S., Farooq, O.: Wavelet Based Robust Sub-band Features for Phoneme Recognition. IEE Proceedings: Vision, Image and Signal Processing 151(3), 187–193 (2004)

    Article  Google Scholar 

  9. Datta, S., Farooq, O.: Mel-Scaled Wavelet Filter Based Features for Noisy Unvoiced Phoneme Recognition. In: ICSLP 2002, pp. 1017–1020 (2002)

    Google Scholar 

  10. Gowdy, J.N., Tufekci, Z.: Mel-Scaled Discrete Wavelet Coefficients for Speech Recognition. In: 25th IEEE International Conference on Acoustics, Speech, and Signal Processing - ICASSP 2000, vol. 3, pp. 1351–1354. IEEE Press, New York (2000)

    Google Scholar 

  11. Sarikaya, R., Hansen, J.H.L.: High Resolution Speech Feature Parameterization for Monophone – Based Stressed Speech Recognition. IEEE Signal Processing Letters 7(7), 182–185 (2000)

    Article  Google Scholar 

  12. Sarikaya, R., Gowdy, J.N.: Subband Based Classification of Speech Under Stress. In: 23rd IEEE International Conference on Acoustics, Speech, and Signal Processing – ICASSP 1998, vol. 1, pp. 569–572. IEEE Press, New York (1998)

    Google Scholar 

  13. Evangelista, G., Cavaliere, S.: Discrete Frequency Warped Wavelets: Theory and Applications. IEEE Transactions on Signal Processing 46(4), 874–875 (1998)

    Article  MathSciNet  Google Scholar 

  14. Wickerhauser, M.V., Coifman, R.R.: Entropy-Based Algorithms for Best Basis Selection. IEEE Transactions on Information Theory 38(2), part 2, 713–718 (1992)

    Article  MATH  Google Scholar 

  15. Łukasik, E.: Classification of Voiceless Plosives Using Wavelet Packet Based Approaches. In: EUSIPCO 2000, pp. 1933–1936 (2000)

    Google Scholar 

  16. Daubechies, I.: Ten Lectures on Wavelets. SIAM, Philadelphia (1992)

    MATH  Google Scholar 

  17. Vetterli, M., Ramchandran, K., Herley, C.: Wavelets, Subband Coding, and Best Bases. Proceedings of the IEEE 84(4), 541–560 (1996)

    Article  Google Scholar 

  18. Wickerhauser, M.V.: Designing a Custom Wavelet Packet Image Compression Scheme with Applications to Fingerprints and Seismic Data. In: Perspectives in Mathematical Physics: Conference in Honor of Alex Grossmann, pp. 153–157. CFML, CRC Press (1998)

    Google Scholar 

  19. Wickerhauser, M.V., Odgaard, P.F., Stoustrup, J.: Wavelet Packet Based Detection of Surface Faults on Compact Discs. In: 6th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes. IFAC, vol. 6, part 1 (2006)

    Google Scholar 

  20. Vetterli, M., Ramchandran, K.: Best Wavelet Packet Bases Using Rate-Distortion Criteria. In: IEEE International Symposium on Circuits and Systems – ISCAS 1992, p. 971. IEEE Press, New York (1992)

    Google Scholar 

  21. Young, S., et al.: HTK Book. Cambridge University Engineering Department, Cambridge (2005)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electronics, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059, Kraków, Poland

    Jakub Gałka & Mariusz Ziółko

Authors
  1. Jakub Gałka
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mariusz Ziółko
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Computer Science, Escola Politecnica Superior, Universidat de Vic, c/. Sagrada Familia, 7, 08500, Vic (Barcelona), Spain

    Jordi Solé-Casals

  2. Department of Computer Science, Escola Politecnica Superior, Universitat de Vic, c./. Sagrada Familia, 7, 08500, Vic (Barcelona), Spain

    Vladimir Zaiats

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Gałka, J., Ziółko, M. (2010). Wavelet Speech Feature Extraction Using Mean Best Basis Algorithm. In: Solé-Casals, J., Zaiats, V. (eds) Advances in Nonlinear Speech Processing. NOLISP 2009. Lecture Notes in Computer Science(), vol 5933. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11509-7_17

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-11509-7_17

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-11508-0

  • Online ISBN: 978-3-642-11509-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation