Automatic identification of pathological voice quality based on the GRBAS categorization

Abstract

Acoustic analysis-based automatic detection of voice pathologies enables non-invasive, low-cost and objective assessments of the presence of disorders, which might assist in accelerating and improving the diagnosis and clinical treatment given to patients. In this paper, we focus on the automatic assessment of pathological voice quality by identifying the four attributes of Roughness, Breathiness, Asthenia, and Strain based on the GRBAS categorization. The proposed method adopts higher-order local auto-correlation (HLAC) features, which are calculated from the excitation source signal obtained by an automatic topology-generated AR-HMM analysis, and identifies the four attributes using a feed-forward neural network (FFNN)-based classifier. In the experiments, an average F-measure of 87.25% was obtained for a speaker- based identification task, which confirms the feasibility of the proposed method.