Some Spectral Correlates of Pathological Breathy and Rough Voice Quality for Different Types of Vowel Fragments

TLDR

The study investigates how listeners’ ratings of pathological breathiness and roughness relate to characteristics of the voice spectrum. The authors aim to identify spectral parameters that predict breathiness and roughness and to determine whether the type of vowel fragment influences the regression model. Ratings were collected for three vowel fragment types—onset, mid‑vowel, and combined onset‑post‑onset—to assess spectral predictors. The harmonics‑to‑noise ratio alone explained up to 54 % of rating variance, and combined predictors accounted for 75–80 % of breathiness variance across fragments, while roughness variance varied with fragment type (71 % for onset, 52 % for post‑onset) and was also affected by a six‑predictor factor‑analysis model.

Abstract

This study deals with the relation between listeners' ratings of pathological breathiness and roughness and certain characteristics of the voice spectrum. Two general research questions were addressed: First, which spectral parameters may serve as useful predictors of breathiness and roughness? Second, does the type of speech fragment used for analysis have an effect on the obtained regression model? Listener ratings of breathiness and roughness were obtained for three types of vowel fragments: a vowel onset segment, a mid-vowel (post-onset) segment, and a vowel segment covering the onset and the acoustically more stable post-onset parts. Results indicated that the harmonics-to-noise ratio was the best single predictor of both rated breathiness and roughness, explaining up to 54% of the true rating variance. By combining different predictors, between 75% and 80% of the breathiness variance could be explained for all three types of fragments. For roughness, a strong effect of fragment type was observed, with most variance explained in vowel onset fragments (71%), and least in post-onset fragments (52%). The effect of fragment type was also observed when regression analyses were performed with six predictors based on a factor analysis of the acoustic data.