Correlating Perceptual Voice Quality in Adductor Spasmodic Dysphonia With Computer Vision Assessment of Glottal Geometry Dynamics.

Abstract

This study examined the relationship between voice quality and glottal geometry dynamics in patients with adductor spasmodic dysphonia (ADSD). An objective computer vision and machine learning system was developed to extract glottal geometry dynamics from nasolaryngoscopic video recordings for 78 patients with ADSD. General regression models were used to examine the relationship between overall voice quality and 15 variables that capture glottal geometry dynamics derived from the computer vision system. Two experts in ADSD independently rated voice quality for two separate voice tasks for every patient, yielding four different voice quality rating models. All four of the regression models exhibited positive correlations with clinical assessments of voice quality (R 2s = .30-.34, Spearman rho = .55-.61, all with p < .001). Seven to 10 variables were included in each model. There was high overlap in the variables included between the four models, and the sign of the correlation with voice quality was consistent for each variable across all four regression models. We found specific glottal geometry dynamics that correspond to voice quality in ADSD.