Journal of Speech, Language, and Hearing Research Vol.42 355-366 April 1999.
© American Speech-Language-Hearing Association
This Article
Right arrow Full Text (PDF)
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Right arrowCustom Print
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow My Folders
Right arrow Download to citation manager
Right arrow reprints & permissions
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Callan, D. E.
Right arrow Articles by Tasko, S. M.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Callan, D. E.
Right arrow Articles by Tasko, S. M.
Related Collections
Right arrow Editors' Awards
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Delicious   Add to Digg   Add to Facebook   Add to Reddit   Add to Technorati   Add to Twitter  
What's this?

Self-Organizing Map for the Classification of Normal and Disordered Female Voices

Daniel E. Callan 1
Ray D. Kent 1
Nelson Roy 2

Stephen M. Tasko 1
1 Department of Communicative Disorders University of Wisconsin-Madison
2 Department of Communication Disorders Minot State University Minot, ND

dcallan{at}hip.atr.co.jp

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.

KEY WORDS: voice, self-organizing map, classification, acoustic, dysphonia

Submitted on June 1, 1998
Accepted on October 30, 1998


Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Delicious Delicious   Add to Digg Digg   Add to Facebook Facebook   Add to Reddit Reddit   Add to Technorati Technorati   Add to Twitter Twitter    What's this?


This article has been cited by other articles:


Home page
 Speech Science and Orofacial DisordersHome page
J. M. Hillenbrand
Acoustic Analysis of Voice: A Tutorial
Speech Science and Orofacial Disorders, October 1, 2011; 21(2): 31 - 43.
[Abstract] [Full Text] [PDF]