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Quantification of Voice Type Components Present in Human Phonation Using a Modified Diffusive Chaos Technique.

Boquan Liu1, Evan Polce1, Hayley Raj1

  • 11 Department of Surgery-Division of Otolaryngology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA.

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A novel diffusive chaos method effectively categorizes voice types by analyzing voice type component profiles (VTCPs). This approach offers a more detailed and objective assessment of voice disorders than traditional acoustic measures.

Keywords:
acoustic analysisdiffusive chaosnonlinear dynamicsotolaryngologyvoice disorders

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Area of Science:

  • Acoustic analysis of voice signals
  • Biophysics of voice production
  • Signal processing for biomedical applications

Background:

  • Human voice signals comprise complex mixtures of periodic, modulated, aperiodic, and stochastic elements.
  • Existing signal typing methods for voice disorders lack comprehensive analysis of these components.
  • A need exists for objective, clinically useful tools to evaluate voice quality.

Purpose of the Study:

  • To introduce a novel diffusive chaos method for analyzing voice signals.
  • To detect the distribution of four voice types within a signal.
  • To develop comprehensive voice type component profiles (VTCPs) for objective voice evaluation.

Main Methods:

  • 135 voice samples from the Disordered Voice Database were analyzed.
  • Samples were classified into types 1, 2, 3, and 4 using spectrogram analysis.
  • The diffusive chaos method was applied to generate VTCPs for each voice sample.

Main Results:

  • The proportions of voice type component 1 (VTC1) varied significantly across traditional voice types (P < .001).
  • Three of four VTCs for type 3 voices differed significantly from type 4 voices (P < .001).
  • Spectrum convergence ratio did not show significant variation between voice types 1, 2, or 3.

Conclusions:

  • The diffusive chaos method effectively generates VTCPs for disordered voices of varying severity.
  • VTCPs offer a more detailed assessment of voice changes over time compared to single acoustic parameters.
  • This method quantifies stochastic noise components, such as those causing breathiness.