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Detection of Voice Pathology using Fractal Dimension in a Multiresolution Analysis of Normal and Disordered Speech Signals

  • Patient Facing Systems
  • Published:
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Abstract

Voice disorders are associated with irregular vibrations of vocal folds. Based on the source filter theory of speech production, these irregular vibrations can be detected in a non-invasive way by analyzing the speech signal. In this paper we present a multiband approach for the detection of voice disorders given that the voice source generally interacts with the vocal tract in a non-linear way. In normal phonation, and assuming sustained phonation of a vowel, the lower frequencies of speech are heavily source dependent due to the low frequency glottal formant, while the higher frequencies are less dependent on the source signal. During abnormal phonation, this is still a valid, but turbulent noise of source, because of the irregular vibration, affects also higher frequencies. Motivated by such a model, we suggest a multiband approach based on a three-level discrete wavelet transformation (DWT) and in each band the fractal dimension (FD) of the estimated power spectrum is estimated. The experiments suggest that frequency band 1–1562 Hz, lower frequencies after level 3, exhibits a significant difference in the spectrum of a normal and pathological subject. With this band, a detection rate of 91.28 % is obtained with one feature, and the obtained result is higher than all other frequency bands. Moreover, an accuracy of 92.45 % and an area under receiver operating characteristic curve (AUC) of 95.06 % is acquired when the FD of all levels is fused. Likewise, when the FD of all levels is combined with 22 Multi-Dimensional Voice Program (MDVP) parameters, an improvement of 2.26 % in accuracy and 1.45 % in AUC is observed.

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Acknowledgments

This project was funded by the National Plan for Science, Technology, and Innovation (MAARIFAH), King Abdulaziz City for Science and Technology, Kingdom of Saudi Arabia, Award Number (12-MED-2474-02).

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Authors and Affiliations

  1. Digital Speech Processing Group, Department of Computer Engineering, King Saud University, Riyadh, 11543, Saudi Arabia

    Zulfiqar Ali, Mansour Alsulaiman & Ghulam Muhammad

  2. Centre for Intelligent Signal and Imaging Research, Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, Tronoh, 31750, Perak, Malaysia

    Zulfiqar Ali & Irraivan Elamvazuthi

Authors
  1. Zulfiqar Ali
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  2. Irraivan Elamvazuthi
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  3. Mansour Alsulaiman
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  4. Ghulam Muhammad
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Corresponding author

Correspondence to Zulfiqar Ali.

Additional information

This article is part of the Topical Collection on Patient Facing Systems

Appendix

Appendix

The list of files that do not contain MDVP parameters in the MEEI database:

  • JFG08AN.RES

  • KXH30AN.RES

  • LES15AN.RES

  • TAB21AN.RES

  • WPB30AN.RES

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Ali, Z., Elamvazuthi, I., Alsulaiman, M. et al. Detection of Voice Pathology using Fractal Dimension in a Multiresolution Analysis of Normal and Disordered Speech Signals. J Med Syst 40, 20 (2016). https://doi.org/10.1007/s10916-015-0392-2

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  • DOI: https://doi.org/10.1007/s10916-015-0392-2

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