Automated dysarthria severity classification for improved objective intelligibility assessment of spastic dysarthric speech

In this paper, automatic dysarthria severity classifica-tion is explored as a tool to advance objective intelli-gibility prediction of spastic dysarthric speech. A Ma-halanobis distance-based discriminant analysis classifier is developed based on a set of acoustic features for-merly proposed for intelligibility prediction and voice pathology assessment. Feature selection is used to sift salient features for both the disorder severity classifica-tion and intelligibility prediction tasks. Experimental re-sults 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. Index Terms: Intelligibility, dysarthria, diagnosis. 1.