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.
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