Advertisement

Analysis of the Multifractal Nature of Speech Signals

  • Diana Cristina González
  • Lee Luan Ling
  • Fábio Violaro
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7441)

Abstract

Frame duration is an essential parameter to ensure correct application of multifractal signal processing. This paper aims to identify the multifractal nature of speech signals through theoretical study and experimental verification. One important part of this pursuit is to select adequate ranges of frame duration that effectively display evidence of multifractal nature. An overview of multifractal theory is given, including definitions and methods for analyzing and estimating multifractal characteristics and behavior. Based on these methods, we evaluate the utterances from two different Portuguese speech databases by studying their singularity curves (τ(q) and f(α)).We conclude that the frame duration between 50 and 100 ms is more suitable and useful for multifractal speech signal processing in terms of speaker recognition performance [11].

Keywords

Multifractal Spectrum Hölder Exponent Speech Signals Scaling Analysis Multifractal Characteristics 

References

  1. 1.
    Campell, J.: Speaker Recognition: A Tutorial. Proceeding of the IEEE 85(9) (1998)Google Scholar
  2. 2.
    Reynolds, D.A., Rose, R.C.: Robust Text-Independent Speaker Identification Using Mixture Speaker Model. IEEE Trans. Speech Audio Processing 3(1), 72–82 (1995)CrossRefGoogle Scholar
  3. 3.
    Langi, A., Kinsner, W.: Consonant Characterization Using Correlation Fractal Dimension for Speech Recognition. In: Proc. on IEEE Western Canada Conference on Communications, Computer, and Power in the Modem Environment, Winnipeg, Canada, vol. 1, pp. 208–213 (1995)Google Scholar
  4. 4.
    Jayant, N., Noll, P.: Digital Coding of Waveforms: Principles and Applications to Speech and Video, 688 p. Prentice-Hall, Englewood Cliffs (1984)Google Scholar
  5. 5.
    Sant’Ana, R., Coelho, R., Alcaim, A.: Text-Independent Speaker Recognition Based on the Hurst Parameter and the Multidimensional Fractional Brownian Motion Model. IEEE Trans. on Audio, Speech, and Language Processing 14(3), 931–940 (2006)CrossRefGoogle Scholar
  6. 6.
    Zhou, Y., Wang, J., Zhang, X.: Research on Speaker Recognition Based on Multifractal Spectrum Feature. In: Second International Conference on Computer Modeling and Simulation, pp. 463–466 (2010)Google Scholar
  7. 7.
    Maragos, P.: Fractal Aspects of Speech Signals: Dimension and Interpolation. In: Proc. IEEE ICASSP, vol. 1, pp. 417–420 (1991)Google Scholar
  8. 8.
    Langitt, A., Soemintapurat, K., Kinsners, W.: Multifractal Processing of Speech Signals Information, Communications and Signal Processing. In: Han, Y., Quing, S. (eds.) ICICS 1997. LNCS, vol. 1334, pp. 527–531. Springer, Heidelberg (1997)Google Scholar
  9. 9.
    Kinsner, W., Grieder, W.: Speech Segmentation Using Multifractal Measures and Amplification of Signal Features. In: Proc. 7th IEEE Int. Conf. on Cognitive Informatics (ICCI 2008), pp. 351–357 (2008)Google Scholar
  10. 10.
    Adeyemi, O.A.: Multifractal Analysis of Unvoiced Speech Signals. ETD Collection for University of Rhode Island. Paper AAI9805227 (1997)Google Scholar
  11. 11.
    González, D.C., Lee, L.L., Violaro, F.: Use of Multifractal Parameters for Speaker Recognition. M. Eng. thesis, FEEC/UNCAMP, Campinas, Brazil (2011)Google Scholar
  12. 12.
    Sténico, J.W., Lee, L.L.: Estimation of Loss Probability and an Admission Control Scheme for Multifractal Network Traffic. M. Eng. thesis, FEEC/UNCAMP, Campinas, Brazil (2009)Google Scholar
  13. 13.
    Riedi, R.H., Crouse, M.S., Ribeiro, V.J., Baraniuk, R.G.: A Multifractal Wavelet Model with Application to Network Traffic. IEEE Trans. on Information Theory 45(3), 992–1018 (1999)MathSciNetzbMATHCrossRefGoogle Scholar
  14. 14.
    Krishna, M.P., Gadre, V.M., Dessay, U.B.: Multifractal Based Network Traffic Modeling. Kluwer Academic Publishers., Ed. Bombay (2003)Google Scholar
  15. 15.
    Ynoguti, C., Violaro, F.: Continuous Speech Recognition Using Hidden Markov Models. D. Eng. thesis, FEEC/UNCAMP, Campinas, Brazil (1999)Google Scholar
  16. 16.
    Holmes, J., Holmes, W.: Speech Synthesis and Recognition, 2nd edn. Tayor & Francis, London (2001)Google Scholar
  17. 17.
    Research Center INRIA Saclay, http://fraclab.saclay.inria.fr/

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Diana Cristina González
    • 1
  • Lee Luan Ling
    • 1
  • Fábio Violaro
    • 1
  1. 1.DECOM – FEECUniversidade Estadual de Campinas (UNICAMP)Brazil

Personalised recommendations