Analysis of Dysarthric Speech using Distinctive Feature Recognition

Imprecise articulatory breakdown is one of the characteristics of dysarthric speech. This work attempts to develop a framework to automatically identify problematic articulatory patterns of dysarthric speakers in terms of distinctive features (DFs), which are effective for describing speech production. The identification of problematic articulatory patterns aims to assist speech therapists in developing intervention strategies. A multilayer perceptron (MLP) system is trained with nondysarthric speech data for DF recognition. Agreement rates between the recognized DF values and the canonical values based on phonetic transcriptions are computed. For nondysarthric speech, our system achieves an average agreement rate of 85.7%. The agreement rate of dysarthric speech declines, ranging between 1% to 3% in mild cases, 4% to 7% in moderate cases, and 7% to 12% in severe cases, when compared with non-dysarthric speech. We observe that the DF disagreement patterns are consistent with the analysis of a speech therapist.