Self-Organizing Map for the Classification of Normal and Disordered Female Voices The goal of this research was to train a self-organizing map (SOM) on various acoustic measures (amplitude perturbation quotient, degree of voice breaks, rahmonic amplitude, soft phonation index, standard deviation of the fundamental frequency, and peak amplitude variation) of the sustained vowel /a/ to enhance visualization of the multidimensional nonlinear ... Research Article
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Research Article  |   April 1999
Self-Organizing Map for the Classification of Normal and Disordered Female Voices
 
Author Affiliations & Notes
  • Daniel E. Callan
    Department of Communicative Disorders University of Wisconsin-Madison
  • Ray D. Kent
    Department of Communicative Disorders University of Wisconsin-Madison
  • Nelson Roy
    Department of Communication Disorders Minot State University Minot, ND
  • Stephen M. Tasko
    Department of Communicative Disorders University of Wisconsin-Madison
  • Daniel E. Callan, ATR Human Information Processing Research Laboratories, 2-2 Hikaridai, Seika-cho, Sorakugun, Kyoto 619-0288.
    Daniel E. Callan, ATR Human Information Processing Research Laboratories, 2-2 Hikaridai, Seika-cho, Sorakugun, Kyoto 619-0288.×
  • Corresponding author: E-mail: dcallan@hip.atr.co.jp
  • * Currently affiliated with ATR Human Information Processing Research Laboratories, Kyoto, Japan.
    Currently affiliated with ATR Human Information Processing Research Laboratories, Kyoto, Japan.×
Article Information
Speech, Voice & Prosodic Disorders / Voice Disorders / Speech, Voice & Prosody / Speech / Research Articles
Research Article   |   April 1999
Self-Organizing Map for the Classification of Normal and Disordered Female Voices
Journal of Speech, Language, and Hearing Research, April 1999, Vol. 42, 355-366. doi:10.1044/jslhr.4202.355
History: Received June 1, 1998 , Accepted October 30, 1998
 
Journal of Speech, Language, and Hearing Research, April 1999, Vol. 42, 355-366. doi:10.1044/jslhr.4202.355
History: Received June 1, 1998; Accepted October 30, 1998

The goal of this research was to train a self-organizing map (SOM) on various acoustic measures (amplitude perturbation quotient, degree of voice breaks, rahmonic amplitude, soft phonation index, standard deviation of the fundamental frequency, and peak amplitude variation) of the sustained vowel /a/ to enhance visualization of the multidimensional nonlinear regularities inherent in the input data space. The SOM was trained using 30 spasmodic dysphonia exemplars, 30 pretreatment functional dysphonia exemplars, 30 post-treatment functional dysphonia exemplars, and 30 normal voice exemplars. After training, the classification performance of the SOM was evaluated. The results indicated that the SOM had better classification performance than that of a stepwise discriminant analysis over the original data. Analysis of the weight values across the SOM, by means of stepwise discriminant analysis, revealed the relative importance of the acoustic measures in classification of the various groups. The SOM provided both an easy way to visualize multidimensional data, and enhanced statistical predictability at distinguishing between the various groups (over that conducted on the original data set). We regard the results of this study as a promising initial step into the use of SOMs with multiple acoustic measures to assess phonatory function.

Acknowledgments
This work was supported in part by NIH Research Grant 5 R01 DC 00319-11 as well as by the National Center for Voice and Speech Grant P60 00976 from the National Institute on Deafness and Other Communication Disorders.
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