Martin D, Fitch J, Wolfe V
Auburn University at Montgomery, AL, USA.
J Speech Hear Res. 1995 Aug;38(4):765-71. doi: 10.1044/jshr.3804.765.
We hypothesized that acoustic measures would predict dysphonic severity with differential results for pathological voice types. An instructional program based upon synthesized voice signals was developed to facilitate an awareness of prototypical voice types. Eighty phonatory samples representing normal subjects as well as patients with unilateral vocal fold paralysis, vocal nodules, and functional dysphonia were analyzed acoustically on the basis of four measures: average fundamental frequency (F0), jitter, shimmer, and harmonic/noise ratio (H/N ratio). Following training, 29 listeners classified 62% of the phonatory samples on the basis of breathy, hoarse, rough, and normal. Dysphonic severity of rough voices was predicted more successfully by H/N ratio (r2 = .73) than by shimmer (r2 = .43). Dysphonic severity of breathy voices was predicted only by the combined features of less jitter, more shimmer, and lower H/N ratio (r2 = .74). No combination of acoustic variables was successful in the prediction of the hoarse voice type.
我们假设声学指标能够预测嗓音障碍的严重程度,且对不同病理嗓音类型会有不同结果。开发了一个基于合成语音信号的教学程序,以促进对典型嗓音类型的认识。对代表正常受试者以及单侧声带麻痹、声带小结和功能性发声障碍患者的80个发声样本,基于平均基频(F0)、抖动、闪烁和谐波/噪声比(H/N比)这四项指标进行声学分析。训练后,29名听众根据呼吸声、嘶哑声、粗糙声和正常声对62%的发声样本进行了分类。与闪烁(r2 = 0.43)相比,粗糙嗓音的嗓音障碍严重程度通过H/N比(r2 = 0.73)能更成功地预测。呼吸声的嗓音障碍严重程度仅通过较少抖动、较多闪烁和较低H/N比的组合特征来预测(r2 = 0.74)。没有任何声学变量的组合能够成功预测嘶哑嗓音类型。