Lee YeonWoo, Park HeeJune, Bae Inho, Kim GeunHyo
Department of Otorhinolaryngology-Head and Neck Surgery and Biomedical Research Institute, Pusan National University Hospital, Busan, Republic of Korea.
Deptartment of Speech and Hearing Therapy, Catholic University of Pusan, Busan, Republic of Korea.
J Voice. 2023 Jan;37(1):142.e5-142.e12. doi: 10.1016/j.jvoice.2020.10.020. Epub 2020 Nov 14.
The purposes of this study were (1) to analyze the usefulness of self-report questionnaires, acoustic analysis, and auditory perceptual assessment for screening voice problems; and (2) to develop a new model for predicting a comprehensive voice severity using multi-assessment.
A total of 306 voice samples were analyzed in this study (typical group, n = 72; dysphonia group, n = 234). We performed a receiver operating characteristic analysis to determine the cutoff values of auditory perceptual assessments (visual analog scale), acoustic parameters (spectral- and cepstral-based analyses), and self-report questionnaires for screening voice disorders. We also performed a stepwise multiple regression analysis to verify which combination of parameters (acoustic parameters, and self-report questionnaires) could best predict perceived voice severity.
We verified that most of the variables analyzed were useful for voice evaluation, and found to be useful for screening voice problems. Of these, a five-variable model was a useful to predict perceived voice severity (mean R = .807). The five-variable model consisted of acoustic parameters based on cepstral analysis (cepstral peak prominences in connected speech and sustained vowel task, and low versus high-frequency spectral energy ratio in connected speech task) and self-report questionnaires (total score of the Voice Handicap Index, and rumination score of the Voice Catastrophization Index).
We verified that most of the variables were useful for screening dysphonia and five-variable model was a useful to predict perceived voice severity. The five-variable model could be used as an objective criterion for predicting voice severity.
本研究的目的是：（1）分析自我报告问卷、声学分析和听觉感知评估在筛查嗓音问题方面的实用性；（2）开发一种使用多评估预测综合嗓音严重程度的新模型。
本研究共分析了306个嗓音样本（典型组，n = 72；发声障碍组，n = 234）。我们进行了受试者工作特征分析，以确定听觉感知评估（视觉模拟量表）、声学参数（基于频谱和cepstral的分析）和自我报告问卷用于筛查嗓音障碍的临界值。我们还进行了逐步多元回归分析，以验证哪些参数组合（声学参数和自我报告问卷）能最好地预测感知到的嗓音严重程度。
我们验证了所分析的大多数变量对嗓音评估有用，且对筛查嗓音问题有用。其中，一个五变量模型可有效预测感知到的嗓音严重程度（平均R = 0.807）。该五变量模型由基于cepstral分析的声学参数（连贯言语和持续元音任务中的cepstral峰值突出度，以及连贯言语任务中的低频与高频频谱能量比）和自我报告问卷（嗓音障碍指数总分和嗓音灾难化指数的沉思分数）组成。
我们验证了大多数变量对筛查发声障碍有用，且五变量模型可有效预测感知到的嗓音严重程度。该五变量模型可作为预测嗓音严重程度的客观标准。
