Cover Image
QR Code

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...

Full description

Published in:Journal of Speech, Language & Hearing Research Vol. 42; no. 2; pp. 355 - 367
Main Authors: Callan DE, Kent RD, Roy N, Tasko SM
Format: research tables/charts Journal Article
Published: American Speech-Language-Hearing Association Apr1999
Online Access:View this record in EBSCOhost
fields @attributes:
  recordID: 1
pdfLink:
plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=107211690&site=ehost-live
header:
  @attributes:
    shortDbName: ccm
    uiTerm: 107211690
    longDbName: CINAHL Complete
    uiTag: AN
  controlInfo:
    bkinfo:
    dissinfo:
    jinfo:
      jid:
        10924388
        1SM
      jtl: Journal of Speech, Language & Hearing Research
      issn: 10924388
      maglogo: N
    pubinfo:
      dt: Apr1999
      vid: 42
      iid: 2
      pid: 42
      pub: American Speech-Language-Hearing Association
      place: Rockville, Maryland
    artinfo:
      ui:
        107211690
        107211690
        1999061886
        10.1044/jslhr.4202.355
        NLM10229452
        107211690
      ppf: 355
      ppct: 12
      formats:
        fmt:
          – @attributes:
              type: T
          – @attributes:
              type: P
      tig:
        atl: Self-organizing map for the classification of normal and disordered female voices.
      aug:
        au:
          Callan DE
          Kent RD
          Roy N
          Tasko SM
        affil: Department of Communicative Disorders, University of Wisconsin-Madison
      sug:
        subj:
          Voice Quality Evaluation
          Clinical Assessment Tools
          Acoustics
          Voice Classification
          Funding Source
          Case Control Studies
          Voice Disorders
          Female
          Neural Networks (Computer)
          Discriminant Analysis
          Instrument Construction
          Descriptive Statistics
          Human
          Female
      ab: 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.
      pubtype: Academic Journal
      doctype:
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
    refInfo:
    holdings:
      @attributes:
        islocal: N