The Design and Assessment of a Multiparametric Model for the Dysphonia Severity Index for Persian-speaking Populations

Abstract

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.