This paper represents a method for assessment of speech skill of dysarthria disordered patients repeatedly, which is a motor speech disorder comes from neurological injury of the motor element of the motor-speech system. This structure comprises two major features: feature representation and prediction. In the feature representation, the speech sound is changed into a phone series with an regular speech recognition technique and joint with a canonical phone sequence from an intonation dictionary using a biased finite state transducer to obtain the pronunciation mappings such as match, substitution, and deletion. Next, in the prediction process, a structured sparse linear model exists with phonological information that concurrently addresses phonologically structured sparse feature selection and transparency prediction is identified. The TORGO database of dysarthric pronunciation consists of aligned acoustical and measured 3D articulatory features from speakers with either cerebral palsy (CP) or amyotrophic lateral sclerosis (ALS), which are two of the most common causes of speech disability. The major aim of this paper is to supply a flexible and suitable environment for the dysathria speech disordered peoples by recognizing their speech to make others understand them