Multivariate Statistical Analysis of Flat Vowel Spectra With a View to Characterizing Dysphonic Voices The aim of this article is to show how dysphonic voices can be characterized by means of a multivariate statistical analysis of flat vowel spectra. The spectral contour was obtained by means of a wavelet transform of the logarithmic magnitude spectrum, which was subsequently flattened to remove interspeaker variability related ... Research Article
Research Article  |   December 01, 2000
Multivariate Statistical Analysis of Flat Vowel Spectra With a View to Characterizing Dysphonic Voices
 
Author Affiliations & Notes
  • Jean Schoentgen
    National Fund for Scientific Research Belgium
  • Mounir Bensaid
    Laboratory of Experimental Phonetics Université Libre de Bruxelles Brussels, Belgium
  • Fabrizio Bucella
    Fonds pour la Formation à la Recherche dans l'Industrie et l'Agriculture Belgium
  • Corresponding author: e-mail: jschoent@ulb.ac.be
  • Contact author: Jean Schoentgen, Laboratory of Experimental Phonetics, CP 110, Université Libre de Bruxelles, 50, Av. F.-D. Roosevelt, B-1050 Brussels, Belgium. Email: jschoent@ulb.ac.be
    Contact author: Jean Schoentgen, Laboratory of Experimental Phonetics, CP 110, Université Libre de Bruxelles, 50, Av. F.-D. Roosevelt, B-1050 Brussels, Belgium. Email: jschoent@ulb.ac.be×
Article Information
Speech, Voice & Prosodic Disorders / Voice Disorders / Speech, Voice & Prosody / Speech / Research Articles
Research Article   |   December 01, 2000
Multivariate Statistical Analysis of Flat Vowel Spectra With a View to Characterizing Dysphonic Voices
Journal of Speech, Language, and Hearing Research, December 2000, Vol. 43, 1493-1508. doi:10.1044/jslhr.4306.1493
History: Received January 21, 2000 , Accepted March 30, 2000
 
Journal of Speech, Language, and Hearing Research, December 2000, Vol. 43, 1493-1508. doi:10.1044/jslhr.4306.1493
History: Received January 21, 2000; Accepted March 30, 2000

The aim of this article is to show how dysphonic voices can be characterized by means of a multivariate statistical analysis of flat vowel spectra. The spectral contour was obtained by means of a wavelet transform of the logarithmic magnitude spectrum, which was subsequently flattened to remove interspeaker variability related to the excitation and vocal tract filter functions. The results of the statistical analysis of flat spectra were the following. Firstly, principal components analysis produced markers that separated noisy from clean spectra. Secondly, the heuristic search for harmonic peaks or interharmonic dips could be omitted. Thirdly, conventional spectral markers of noise appeared as special instances of the markers that were derived statistically. Fourthly, the levels of visually assigned hoarseness and the first two principal components were significantly correlated. The assignment of different levels of (visual) hoarseness to different vowel timbres could be explained by the variability associated with the spectral contour.

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