[Psychoacoustic scaling of acoustic voice parameters by multicenter voice ratings]

TY - JOUR

T1 - [Psychoacoustic scaling of acoustic voice parameters by multicenter voice ratings]

AU - Schönweiler, R

AU - Wübbelt, P

AU - Hess, Markus

AU - Ptok, M

PY - 2001

Y1 - 2001

N2 - BACKGROUND: The purpose of the study was to analyze if perceptual voice quality ratings of the well-known RBH rating procedure (a 4-point scale of roughness, breathiness, and hoarseness) covary with acoustical voice parameters. METHODS: 120 voice samples from subjects with healthy and hoarse voices were rated on the RBH-index in a multicenter study with 31 raters. Multivariate regression tree analysis classified the perceptual ratings as "gold standard". Voice samples were acoustically analyzed with a feature extraction method. Feedforward-networks were trained to selected acoustical parameters having highest "relative importance" in the regression trees. Based on the best classifier, a computer program consisting of 50 simultaneous working networks was developed. RESULTS: Mean probabilities for correct classifications were found at 0.65-0.85, implying a significance level over chance (0.25). Classifications of the program matched in 40% with a priori values in the categories roughness combined with breathiness, and in 65% in at least one domain. CONCLUSIONS: The new method described here provides a psychoacoustically based "objective" classification of hoarse voices, which seems to enable future analysis of new parameters (like GNE), which may even improve the present results.

AB - BACKGROUND: The purpose of the study was to analyze if perceptual voice quality ratings of the well-known RBH rating procedure (a 4-point scale of roughness, breathiness, and hoarseness) covary with acoustical voice parameters. METHODS: 120 voice samples from subjects with healthy and hoarse voices were rated on the RBH-index in a multicenter study with 31 raters. Multivariate regression tree analysis classified the perceptual ratings as "gold standard". Voice samples were acoustically analyzed with a feature extraction method. Feedforward-networks were trained to selected acoustical parameters having highest "relative importance" in the regression trees. Based on the best classifier, a computer program consisting of 50 simultaneous working networks was developed. RESULTS: Mean probabilities for correct classifications were found at 0.65-0.85, implying a significance level over chance (0.25). Classifications of the program matched in 40% with a priori values in the categories roughness combined with breathiness, and in 65% in at least one domain. CONCLUSIONS: The new method described here provides a psychoacoustically based "objective" classification of hoarse voices, which seems to enable future analysis of new parameters (like GNE), which may even improve the present results.

M3 - SCORING: Zeitschriftenaufsatz

VL - 80

SP - 117

EP - 122

JO - LARYNGO RHINO OTOL

JF - LARYNGO RHINO OTOL

SN - 0935-8943

IS - 3

M1 - 3

ER -