Latoszek Ben Barsties V, De Bodt Marc, Gerrits Ellen, Maryn Youri
Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium; Institute of Health Studies, HAN University of Applied Sciences, Nijmegen, The Netherlands.
Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium; Department of Otorhinolaryngology and Head & Neck Surgery, University Hospital, Antwerp, Belgium; Faculty of Medicine & Health Sciences, University of Ghent, Ghent, Belgium.
J Voice. 2018 Mar;32(2):149-161. doi: 10.1016/j.jvoice.2017.04.017. Epub 2017 May 29.
In voice assessment, the evaluation of voice quality is a major component in which roughness has received wide acceptance as a major subtype of abnormal voice quality. The aim of the present study was to develop a new multivariate acoustic model for the evaluation of roughness.
In total, 970 participants with dysphonia and 88 participants with normal voice were included. Concatenated voice samples of continuous speech and sustained vowel [a:] were perceptually judged on roughness severity. Acoustic analyses were conducted on the voiced segments of the continuous speech sample plus sustained vowel as well. A stepwise multiple linear regression analysis was applied to construct an acoustic model of the best acoustic predictors. Concurrent validity, diagnostic accuracy, and cross-validation were verified on the basis of Spearman correlation coefficient (r), several estimates of the receiver operating characteristics plus the likelihood ratio, and iterated internal cross-correlations.
Six experts were included for perceptual analysis based on acceptable rater reliability. Stepwise multiple regression analysis yielded a 12-variable acoustic model. A marked correlation was identified between the model and the perceptual judgment (r = 0.731, P = 0.000). The cross-correlations confirmed a high comparable degree of association. However, the receiver operating characteristics and likelihood ratio results showed the best diagnostic outcome at a threshold of 2.92, with a sensitivity of 51.9% and a specificity of 94.9%.
Currently, the newly developed roughness model is not recommended for clinical practice. Further research is needed to detect the acoustic complexity of roughness (eg, multiplophonia, irregularity, chaotic structure, glottal fry, etc).
在嗓音评估中，嗓音质量评估是一个主要组成部分，其中粗糙声作为异常嗓音质量的一种主要亚型已被广泛认可。本研究的目的是开发一种用于评估粗糙声的新的多元声学模型。
总共纳入了970名嗓音障碍参与者和88名嗓音正常的参与者。对连续语音和持续元音[a:]的拼接语音样本进行了粗糙声严重程度的主观判断。对连续语音样本以及持续元音的浊音段也进行了声学分析。应用逐步多元线性回归分析来构建最佳声学预测指标的声学模型。基于Spearman相关系数（r）、几种接收器操作特征估计值以及似然比和迭代内部互相关对同时效度、诊断准确性和交叉验证进行了验证。
基于可接受的评分者信度纳入了6名专家进行主观分析。逐步多元回归分析得出了一个包含12个变量的声学模型。该模型与主观判断之间存在显著相关性（r = 0.731，P = 0.000）。互相关证实了高度可比的关联程度。然而，接收器操作特征和似然比结果显示，在阈值为2.92时诊断结果最佳，灵敏度为51.9%，特异度为94.9%。
目前，新开发的粗糙声模型不建议用于临床实践。需要进一步研究来检测粗糙声的声学复杂性（例如，多重谐音、不规则性、混沌结构、声门嘟噜声等）。
