Objective Severity Assessment from Disordered Voice Using Estimated Glottal Airflow

In clinical practice, the severity of disordered voice is typically rated by a professional with auditory-perceptual judgment. The present study aims to automate this assessment procedure, in an attempt to make the assessment objective and less laborintensive. In the automated analysis, glottal airflow is estimated from the analyzed voice signal with an inverse filtering algorithm. Automatic assessment is realized by a regressor that predicts from temporal and spectral features of the glottal airflow. A regressor trained on overtone amplitudes and harmonic richness factors extracted from a set of continuous-speech utterances was applied to a set of sustained-vowel utterances, giving severity predictions (on a scale of ratings from 0 to 100) with an average error magnitude of 14.