Estimating dysphonia severity in continuous speech: application of a multi-parameter spectral/cepstral model

Clin Linguist Phon. 2009 Nov;23(11):825-41. doi: 10.3109/02699200903242988.

Abstract

The purpose of the study was to identify a sub-set of spectral/cepstral-based analysis methods that would most effectively predict dysphonia severity (as estimated via auditory-perceptual analysis) in samples of continuous speech. Acoustic estimates of dysphonia severity were used as an objective treatment outcomes measure in a set of pre- vs post-treatment speech samples. Pre- and post-treatment continuous speech samples from 104 females with primary muscle tension dysphonia (MTD) were rated by listeners using a 100 point visual analogue scale (VAS) and analysed acoustically with spectral/cepstral-based measures. Stepwise linear regression produced a three-factor model consisting of the cepstral peak prominence (CPP); the mean ratio of low-to-high frequency spectral energy; and the standard deviation of the ratio of low-to-high frequency spectral energy that was strongly correlated with perceived dysphonia severity ratings (R = .85; R2 = .73). Mean differences between predicted vs perceptual ratings for pre- and post-treatment speech samples were < 6 points on the 100 point VAS; mean absolute differences between predicted and perceived ratings were < 16 points on the 100 point VAS (equivalent to within one scale value on commonly used 7-point equal-appearing interval rating scales). A multi-parameter acoustic model consisting of spectral/cepstral-based measures shows considerable promise as an objective measure of dysphonia severity in continuous speech, even across the diverse voice types and severities observed in pre- and post-treatment MTD speech samples.

MeSH terms

  • Dysphonia / etiology
  • Dysphonia / physiopathology*
  • Dysphonia / therapy
  • Female
  • Humans
  • Linear Models
  • Muscle Tonus
  • Pain Measurement
  • Phonetics
  • Severity of Illness Index*
  • Speech Acoustics
  • Speech Therapy
  • Speech*