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嗓音疾病筛查:确定嗓音声学参数对常见嗓音疾病的预测价值。

Screening of Voice Pathologies: Identifying the Predictive Value of Voice Acoustic Parameters for Common Voice Pathologies.

作者信息

Cantor-Cutiva Lady Catherine, Ramani Sai Aishwarya, Walden Patrick R, Hunter Eric J

机构信息

Department of Communication Sciences and Disorders, University of Iowa, Iowa City, Iowa.

Department of Communicative Sciences and Disorders, Michigan State University, East Lansing, Michigan.

出版信息

J Voice. 2023 Dec 23. doi: 10.1016/j.jvoice.2023.12.005.

Abstract

BACKGROUND

Voice acoustic analysis is important for objectively assessing voice production and diagnosing voice disorders.

AIM

This study aimed to investigate the sensitivity of various voice acoustic parameters in differentiating common voice pathology types.

METHODS

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.

RESULTS AND CONCLUSION

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.

摘要

背景

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。

目的

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。

方法

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。

结果与结论

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。

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本文引用的文献

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Perceptual Voice Qualities Database (PVQD): Database Characteristics.感知语音质量数据库 (PVQD):数据库特征。
J Voice. 2022 Nov;36(6):875.e15-875.e23. doi: 10.1016/j.jvoice.2020.10.001. Epub 2020 Oct 19.
9
Cepstral Peak Prominence Values for Clinical Voice Evaluation.复声强度值在临床嗓音评估中的应用。
Am J Speech Lang Pathol. 2020 Aug 4;29(3):1596-1607. doi: 10.1044/2020_AJSLP-20-00001. Epub 2020 Jul 13.
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Voice changes in Parkinson's disease: What are they telling us?帕金森病中的语音变化:它们在告诉我们什么?
J Clin Neurosci. 2020 Feb;72:1-7. doi: 10.1016/j.jocn.2019.12.029. Epub 2020 Jan 14.

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