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 labor-intensive. 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.