Schönweiler R, Wübbelt P, Hess M, Ptok M
Klinik für Phoniatrie und Pädaudiologie, Medizinische Hochschule Hannover.
Laryngorhinootologie. 2001 Mar;80(3):117-22. doi: 10.1055/s-2001-11883.
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.
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.
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.
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.
本研究旨在分析著名的RBH评级程序（一种基于粗糙度、气息声和嘶哑程度的4级量表）的嗓音质量感知评级是否与声学嗓音参数相关。
在一项有31名评估者参与的多中心研究中，对120份来自健康嗓音和嘶哑嗓音受试者的嗓音样本进行RBH指数评级。多变量回归树分析将感知评级分类为“金标准”。采用特征提取方法对嗓音样本进行声学分析。对回归树中“相对重要性”最高的选定声学参数训练前馈网络。基于最佳分类器，开发了一个由50个同时运行的网络组成的计算机程序。
正确分类的平均概率为0.65 - 0.85，这意味着其显著性水平高于随机概率（0.25）。该程序的分类在粗糙度与气息声组合类别中与先验值匹配率为40%，在至少一个领域中的匹配率为65%。
本文所述的新方法提供了一种基于心理声学的嘶哑嗓音“客观”分类，这似乎能够在未来对新参数（如GNE）进行分析，甚至可能改善当前结果。
