Dysarthria is a motor speech disorder, characterized by slurred or slow speech resulting in low intelligibility. Automatic recognition of dysarthric speech is beneficial to enable people with dysarthria to use speech as a mode of interaction with electronic devices. In this paper we propose a mechanism to adapt the tempo of sonorant part of dysarthric speech to match that of normal speech, based on the severity of dysarthria. We show a significant improvement in recognition of tempo-adapted dysasrthic speech, using a Gaussian Mixture Model (GMM) - Hidden Markov Model (HMM) recognition system as well as a Deep neural network (DNN) - HMM based system. All evaluations were done on Universal Access Speech Corpus.