Automated Dysarthria Severity Classification for Improved Objective Intelligibility Assessment of Spastic Dysarthric Speech

In this paper, automatic dysarthria severity classification is explored as a tool to advance objective intelligibility prediction of spastic dysarthric speech. A Mahalanobis distance-based discriminant analysis classifier is developed based on a set of acoustic features formerly proposed for intelligibility prediction and voice pathology assessment. Feature selection is used to sift salient features for both the disorder severity classification and intelligibility prediction tasks. Experimental results show that a two-level severity classifier combined with a 9-dimensional intelligibility prediction mapping can achieve 0.92 correlation and 12.52 root-mean-square error with subjective intelligibility ratings. The effects of classification errors on intelligibility accuracy are also explored and shown to be insignificant.