Darouie Akbar, Aghajanzadeh Mahshid, Dabirmoghaddam Payman, Salehi Abolfazl, Rahgozar Mehdi
Speech Therapy Department, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran.
Speech Therapy Department, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran; Department of Speech Therapy, School of Rehabilitation, Tehran University of Medical Sciences, Tehran, Iran.
J Voice. 2019 Mar;33(2):226-231. doi: 10.1016/j.jvoice.2017.11.007. Epub 2017 Dec 18.
In instrumental voice assessment, multiparametric models reflect the multidimensional nature of voice and are therefore better than models that reflect only a single dimension of voice. The Dysphonia Severity Index (DSI) is one of the most common multiparametric models. In voice assessment, race, language, and structural and physiological features affect the acoustic, aerodynamic, and voice range profile measures. Given these differences, this study was conducted to design and evaluate a multiparametric and objective model for assessing the severity of dysphonia in Persian-speaking populations.
This study examined 300 participants with several types of dysphonia (104 women and 196 men) and 100 healthy individuals (63 women and 37 men). Five acoustic parameters, three aerodynamic parameters, and seven voice range profile parameters were measured for designing the model. Perceptual evaluation was performed using the grade, roughness, breathiness, asthenia, strain scale. The logistic regression analysis was used to determine the factors affecting the DSI and each component's coefficient.
Of the 15 parameters assessed, shimmer, vital capacity, semitone range, and voice onset time of /pa/ remained in the model with their coefficients. This section presents the DSI model for the examined population. The discriminant analysis showed that this combination corresponds to 47.8 of the perceptual assessment: DSI = 0.289 (shimmer) + 0.0001 (VC) - 0.059 (STR) - 13.278 (VOT_Pa).
In this study, the DSI corresponded to the physiological, linguistic, and racial characteristics of the Persian-speaking population with or without voice disorder.
在嗓音仪器评估中，多参数模型反映了嗓音的多维性质，因此比仅反映嗓音单一维度的模型更好。嗓音障碍严重程度指数（DSI）是最常见的多参数模型之一。在嗓音评估中，种族、语言以及结构和生理特征会影响声学、空气动力学和嗓音音域剖面图测量。鉴于这些差异，本研究旨在设计并评估一种用于评估说波斯语人群嗓音障碍严重程度的多参数客观模型。
本研究检查了300名患有几种类型嗓音障碍的参与者（104名女性和196名男性）以及100名健康个体（63名女性和37名男性）。为设计该模型，测量了五个声学参数、三个空气动力学参数和七个嗓音音域剖面图参数。使用等级、粗糙度、气息声、无力感、紧张度量表进行了感知评估。采用逻辑回归分析来确定影响DSI的因素以及每个成分的系数。
在所评估的15个参数中，闪烁微扰、肺活量、半音音域以及/pɑ/的起音时间及其系数保留在了模型中。本节展示了针对所检查人群的DSI模型。判别分析表明，这种组合对应于47.8%的感知评估：DSI = 0.289（闪烁微扰）+ 0.0001（肺活量）- 0.059（紧张度）- 13.278（/pɑ/起音时间）。
在本研究中，DSI与说波斯语的有声带疾病或无声带疾病人群的生理、语言和种族特征相符。
