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Dysarthria is malfunctioning of motor speech caused by faintness in the humannervous system. It is characterized by the slurred speech along with physicalimpairment which restricts their communication and creates the lack ofconfidence and affects the lifestyle. This paper attempt to increase theefficiency of Automatic Speech Recognition (ASR) system for unimpaired speechsignal. It describes state of art of research into improving ASR for speakerswith dysarthria by means of incorporated knowledge of their speech production.Hybridized approach for feature extraction and acoustic modelling techniquealong with evolutionary algorithm is proposed for increasing the efficiency ofthe overall system. Here number of feature vectors are varied and tested thesystem performance. It is observed that system performance is boosted bygenetic algorithm. System with 16 acoustic features optimized with geneticalgorithm has obtained highest recognition rate of 98.28% with training time of5:30:17.

Hybridized Feature Extraction and Acoustic Modelling Approach for Dysarthric Speech Recognition