de Krom G
Research Institute for Language and Speech, University of Utrecht, The Netherlands.
J Speech Hear Res. 1995 Aug;38(4):794-811. doi: 10.1044/jshr.3804.794.
This study deals with the relation between listeners' ratings of pathological breathiness and roughness and certain characteristics of the voice spectrum. Two general research questions were addressed: First, which spectral parameters may serve as useful predictors of breathiness and roughness? Second, does the type of speech fragment used for analysis have an effect on the obtained regression model? Listener ratings of breathiness and roughness were obtained for three types of vowel fragments: a vowel onset segment, a mid-vowel (post-onset) segment, and a vowel segment covering the onset and the acoustically more stable post-onset parts. Results indicated that the harmonics-to-noise ratio was the best single predictor of both rated breathiness and roughness, explaining up to 54% of the true rating variance. By combining different predictors, between 75% and 80% of the breathiness variance could be explained for all three types of fragments. For roughness, a strong effect of fragment type was observed, with most variance explained in vowel onset fragments (71%), and least in post-onset fragments (52%). The effect of fragment type was also observed when regression analyses were performed with six predictors based on a factor analysis of the acoustic data.
本研究探讨了听众对病理性呼吸音和粗糙音的评分与声谱某些特征之间的关系。研究了两个一般性问题：第一，哪些频谱参数可作为呼吸音和粗糙音的有效预测指标？第二，用于分析的语音片段类型对所得回归模型是否有影响？针对三种元音片段获取了听众对呼吸音和粗糙音的评分：元音起始段、元音中段（起始后）段以及涵盖起始部分和声学上更稳定的起始后部分的元音段。结果表明，谐波噪声比是评定呼吸音和粗糙音的最佳单一预测指标，可解释高达54%的真实评分方差。通过组合不同预测指标，对于所有三种片段类型，可解释75%至80%的呼吸音方差。对于粗糙音，观察到片段类型有强烈影响，元音起始片段中可解释的方差最多（71%），起始后片段中最少（52%）。当基于声学数据的因子分析使用六个预测指标进行回归分析时，也观察到了片段类型的影响。
