Awan Shaheen N, Roy Nelson
Department of Audiology and Speech Pathology, Bloomsburg University, Bloomsburg, Pennsylvania 17815-1301, USA.
J Voice. 2005 Jun;19(2):268-82. doi: 10.1016/j.jvoice.2004.03.005.
The categorization of voice into quality type (ie, normal, breathy, hoarse, rough) is often a traditional part of the voice diagnostic. The goal of this study was to assess the contributions of various time and spectral-based acoustic measures to the categorization of voice type for a diverse sample of voices collected from both functionally dysphonic (breathy, hoarse, and rough) (n=83) and normal women (n=51). Before acoustic analyses, 12 judges rated all voice samples for voice quality type. Discriminant analysis, using the modal rating of voice type as the dependent variable, produced a 5-variable model (comprising time and spectral-based measures) that correctly classified voice type with 79.9% accuracy (74.6% classification accuracy on cross-validation). Voice type classification was achieved based on two significant discriminant functions, interpreted as reflecting measures related to "Phonatory Instability" and "F(0) Characteristics." A cepstrum-based measure (CPP/EXP ratio) consistently emerged as a significant factor in predicting voice type; however, variables such as shimmer (RMS dB) and a measure of low- vs. high-frequency spectral energy (the Discrete Fourier Transformation ratio) also added substantially to the accurate profiling and prediction of voice type. The results are interpreted and discussed with respect to the key acoustic characteristics that contributed to the identification of specific voice types, and the value of identifying a subset of time and spectral-based acoustic measures that appear sensitive to a perceptually diverse set of dysphonic voices.
将嗓音分类为质量类型（即正常、呼吸声、嘶哑、粗糙）通常是嗓音诊断的传统组成部分。本研究的目的是评估各种基于时间和频谱的声学测量方法对从功能性发声障碍（呼吸声、嘶哑和粗糙）患者（n = 83）和正常女性（n = 51）收集的不同嗓音样本进行嗓音类型分类的贡献。在进行声学分析之前，12名评判员对所有嗓音样本的嗓音质量类型进行了评分。判别分析以嗓音类型的模态评分为因变量，生成了一个包含5个变量的模型（包括基于时间和频谱的测量方法），该模型对嗓音类型的正确分类准确率为79.9%（交叉验证时的分类准确率为74.6%）。嗓音类型分类是基于两个显著的判别函数实现的，这两个函数被解释为反映与“发声不稳定性”和“F(0)特征”相关的测量方法。基于cepstrum的测量方法（CPP/EXP比率）一直是预测嗓音类型的一个重要因素；然而，诸如微扰（RMS dB）和低频与高频频谱能量测量方法（离散傅里叶变换比率）等变量也对嗓音类型的准确剖析和预测有很大贡献。针对有助于识别特定嗓音类型的关键声学特征，以及识别对一组在感知上不同的发声障碍嗓音敏感的基于时间和频谱的声学测量方法子集的价值，对结果进行了解释和讨论。
