Dysarthria is a neurological motor disorder caused by cranial damage that interferes with the muscles involved in the correct pronunciation of sounds and intelligible speech. Computer Aided Pronunciation training (CAPT) systems traditionally used for the pronunciation assessment of L2 language learners can offer a method to detect and score mispronounced sounds in dysarthric speakers as a way of evaluation without human intervention. In this work, a phonetic level DNN-HMM based Goodness of Pronunciation (GoP) for pronunciation scoring, on native Tamil Dysarthric speakers corpus is presented. The scores are calculated using the posteriors of the subphonemic elements called senones with a focus on their prevalence across phones and their transitions across HMM states. The phonetic-level scores obtained for speakers of different levels of severity help establish speaker-specific trends in pronunciation through an objective log-likelihood metric, in contrast to subjective evaluations by Speech Language Therapists (SLTs).