Applied Chaos Level Test for Validation of Signal Conditions Underlying Optimal Performance of Voice Classification Methods Purpose The purpose of this study is to introduce a chaos level test to evaluate linear and nonlinear voice type classification method performances under varying signal chaos conditions without subjective impression. Study Design Voice signals were constructed with differing degrees of noise to model signal chaos. Within each ... Research Article
Research Article  |   May 17, 2018
Applied Chaos Level Test for Validation of Signal Conditions Underlying Optimal Performance of Voice Classification Methods
 
Author Affiliations & Notes
  • Boquan Liu
    Department of Surgery-Division of Otolaryngology, University of Wisconsin School of Medicine and Public Health, Madison
  • Evan Polce
    Department of Surgery-Division of Otolaryngology, University of Wisconsin School of Medicine and Public Health, Madison
  • Julien C. Sprott
    Department of Physics, University of Wisconsin-Madison
  • Jack J. Jiang
    Department of Surgery-Division of Otolaryngology, University of Wisconsin School of Medicine and Public Health, Madison
  • Disclosure: The authors have declared that no competing interests existed at the time of publication.
    Disclosure: The authors have declared that no competing interests existed at the time of publication.×
  • Correspondence to Jack Jiang: jjjiang@wisc.edu
  • Editor-in-Chief: Krista Wilkinson
    Editor-in-Chief: Krista Wilkinson×
  • Editor: Preeti Sivasankar
    Editor: Preeti Sivasankar×
Article Information
Speech, Voice & Prosody / Speech / Research Articles
Research Article   |   May 17, 2018
Applied Chaos Level Test for Validation of Signal Conditions Underlying Optimal Performance of Voice Classification Methods
Journal of Speech, Language, and Hearing Research, May 2018, Vol. 61, 1130-1139. doi:10.1044/2018_JSLHR-S-17-0250
History: Received June 27, 2017 , Revised November 20, 2017 , Accepted January 30, 2018
 
Journal of Speech, Language, and Hearing Research, May 2018, Vol. 61, 1130-1139. doi:10.1044/2018_JSLHR-S-17-0250
History: Received June 27, 2017; Revised November 20, 2017; Accepted January 30, 2018

Purpose The purpose of this study is to introduce a chaos level test to evaluate linear and nonlinear voice type classification method performances under varying signal chaos conditions without subjective impression.

Study Design Voice signals were constructed with differing degrees of noise to model signal chaos. Within each noise power, 100 Monte Carlo experiments were applied to analyze the output of jitter, shimmer, correlation dimension, and spectrum convergence ratio. The computational output of the 4 classifiers was then plotted against signal chaos level to investigate the performance of these acoustic analysis methods under varying degrees of signal chaos.

Method A diffusive behavior detection–based chaos level test was used to investigate the performances of different voice classification methods. Voice signals were constructed by varying the signal-to-noise ratio to establish differing signal chaos conditions.

Results Chaos level increased sigmoidally with increasing noise power. Jitter and shimmer performed optimally when the chaos level was less than or equal to 0.01, whereas correlation dimension was capable of analyzing signals with chaos levels of less than or equal to 0.0179. Spectrum convergence ratio demonstrated proficiency in analyzing voice signals with all chaos levels investigated in this study.

Conclusion The results of this study corroborate the performance relationships observed in previous studies and, therefore, demonstrate the validity of the validation test method. The presented chaos level validation test could be broadly utilized to evaluate acoustic analysis methods and establish the most appropriate methodology for objective voice analysis in clinical practice.

Acknowledgments
This study was supported by the National Institute on Deafness and Other Communication Disorders under award number DC006019 awarded to Dr. Jack Jiang.
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