Cantor-Cutiva Lady Catherine, Ramani Sai Aishwarya, Walden Patrick R, Hunter Eric J
Department of Communication Sciences and Disorders, University of Iowa, Iowa City, Iowa.
Department of Communicative Sciences and Disorders, Michigan State University, East Lansing, Michigan.
J Voice. 2023 Dec 23. doi: 10.1016/j.jvoice.2023.12.005.
Voice acoustic analysis is important for objectively assessing voice production and diagnosing voice disorders.
This study aimed to investigate the sensitivity of various voice acoustic parameters in differentiating common voice pathology types.
Data from the publicly available Perceptual Voice Qualities Database were analyzed; the database includes recordings of participants with and without voice disorders. A wide range of acoustic parameters was estimated from the recordings, such as alpha ratio, harmonics-to-noise ratio (HNR), cepstral peak prominence smoothed (CPPS), pitch period entropy (PPE), fundamental frequency, jitter, shimmer, and sound pressure levels. The predictive capabilities of the parameters were evaluated using receiver operating characteristic curves. Linear regression analysis determined the associations between parameters and voice disorders. Principal component analysis was conducted to identify important parameters for distinguishing voice disorders.
This study has identified significant differences in acoustic parameters between those with and without voice disorders. Notably, the combination of five parameters-namely, PPE, shimmer, jitter, CPPS, and HNR-was identified as a strong predictor in voice disorder screening. These findings contribute substantially to the field of voice disorders, offering valuable insights for screening and diagnosis.
嗓音声学分析对于客观评估嗓音产生及诊断嗓音障碍至关重要。
本研究旨在探究各种嗓音声学参数在区分常见嗓音病理类型方面的敏感性。
对公开可用的感知嗓音质量数据库中的数据进行分析;该数据库包含有嗓音障碍和无嗓音障碍参与者的录音。从录音中估计了多种声学参数,如阿尔法比率、谐波噪声比(HNR)、平滑的谐波峰值突出度(CPPS)、基音周期熵(PPE)、基频、抖动、闪烁和声压级。使用受试者工作特征曲线评估参数的预测能力。线性回归分析确定参数与嗓音障碍之间的关联。进行主成分分析以识别区分嗓音障碍的重要参数。
本研究已确定有嗓音障碍者和无嗓音障碍者在声学参数上存在显著差异。值得注意的是,五个参数(即PPE、闪烁、抖动、CPPS和HNR)的组合被确定为嗓音障碍筛查的有力预测指标。这些发现对嗓音障碍领域有重大贡献,为筛查和诊断提供了有价值的见解。