Abstract
New research on Voice Diseases and Conditions - Hoarseness is the subject of a report. According to news reporting out of Erlangen, Germany, by NewsRx editors, research stated, “Auditory perceptual evaluation is considered the gold standard for assessing voice quality, but its reliability is limited due to inter-rater variability and coarse rating scales. This study investigates a continuous, objective approach to evaluate hoarseness severity combining machine learning (ML) and sustained phonation.” Funders for this research include Deutsche Forschungsgemeinschaft, Deutsche Forschungsgemeinschaft. Our news journalists obtained a quote from the research from University Hospital Erlangen, “For this purpose, 635 acoustic recordings of the sustained vowel /a/ and subjective ratings based on the roughness, breathiness, and hoarseness scale were collected from 595 subjects. A total of 50 temporal, spectral, and cepstral features were extracted from each recording and used to identify suitable ML algorithms. Using variance and correlation analysis followed by backward elimination, a subset of relevant features was selected. Recordings were classified into two levels of hoarseness, H＜2 and H 2, yielding a continuous probability score y [0,1]. An accuracy of 0.867 and a correlation of 0.805 between the model’s predictions and subjective ratings was obtained using only five acoustic features and logistic regression (LR). Further examination of recordings pre- and post-treatment revealed high qualitative agreement with the change in subjectively determined hoarseness levels. Quantitatively, a moderate correlation of 0.567 was obtained.”
NETL
NSTL
Acoustical Soc Amer Amer Inst Physics