Study for Correlation between Objective and Subjective Voice Parameters in Patients with Dysphonia

Abstract

Background and Objectives Voice evaluation is classified into subjective tests such as auditory perception and self-measurement, and objective tests such as acoustic and aerodynamic analysis. When evaluating dysphonia, subjective and objective test results do not always match. The purpose of this study was to analyze the relationship between subjective and objective evaluation in patients with dysphonia and to identify meaningful parameters by disease. Materials and MethodThe total of 322 patients who visited voice clinic from May 2017 to May 2018 were included in this study. Laryngeal lesions were identified using stroboscopy. Pearson correlation test was performed to analyse correlation between subjective tests including GRBAS scale and voice handicap index, and objective tests including jitter, shimmer, noise to harmonic ratio (NHR), cepstral peak prominence (CPP), maximal phonation time (MPT), mean flow rate, and subglottic pressure. Results In vocal nodule and sulcus vocalis, among GRBAS system, grade and breathiness showed good correlation with CPP, and roughness showed good correlation with jitter or shimmer. In unilateral vocal cord paralysis (UVCP), grade and breathiness showed a very good correlation with CPP, and also good correlation with jitter, shimmer, NHR, and MPT. Also asthenia showed good correlation with CPP and MPT. Vocal polyp has a limited association with other diseases. Conclusion In patients with dysphonia, grade and breathiness showed good correlation with CPP, jitter, and shimmer, and reflect the state of voice change well especially in UVCP, CPP, and MPT.

Machine Learning Assessment of Spasmodic Dysphonia Based on Acoustical and Perceptual Parameters

“…Bhuta et al [ 17 ] found relationships between the voice turbulence index (VTI) and G, NHR with G and R, and soft phonation index (SPI) with G and B. These results were confirmed in more recent papers of Park et al [ 41 ] and Narisimhan et al [ 42 ]. Focusing on SD, Dejonckere et al [ 43 ] found relationships only between B and devoicing measures extracted with AMPEX: PVF (i.e., the proportion of voiced frames), PVS (i.e., the proportion of speech frames), and VL90 (which represents the 90th percentile of the voicing length distribution).…”

“…In 25% of observations, NNE, a measure of noise alternative to HNR, was considered relevant, but its total contribution weight was equal to 2.4% only. However, it is important to specify that these discrepancies with literature results [ 39 , 41 , 42 ] are probably caused by different tasks: while jitter and NNE typically show strong correlations with R when healthy subjects are compared with patients diagnosed with AdSD, probably jitter and NNE do not represent relevant parameters to assess and distinguish among different AdSD severity classes. B ratings and F1 median values showed the strongest relationship.…”