  Dysarthria is a disability that causes a disturbance in the human speech
system and reduces the quality and intelligibility of a person's speech.
Because of this effect, the normal speech processing systems can not work
properly on impaired speech. This disability is usually associated with
physical disabilities. Therefore, designing a system that can perform some
tasks by receiving voice commands in the smart home can be a significant
achievement. In this work, we introduce gammatonegram as an effective method to
represent audio files with discriminative details, which is used as input for
the convolutional neural network. On the other word, we convert each speech
file into an image and propose image recognition system to classify speech in
different scenarios. Proposed CNN is based on the transfer learning method on
the pre-trained Alexnet. In this research, the efficiency of the proposed
system for speech recognition, speaker identification, and intelligibility
assessment is evaluated. According to the results on the UA dataset, the
proposed speech recognition system achieved 91.29% accuracy in
speaker-dependent mode, the speaker identification system acquired 87.74%
accuracy in text-dependent mode, and the intelligibility assessment system
achieved 96.47% accuracy in two-class mode. Finally, we propose a multi-network
speech recognition system that works fully automatically. This system is
located in a cascade arrangement with the two-class intelligibility assessment
system, and the output of this system activates each one of the speech
recognition networks. This architecture achieves an accuracy of 92.3% WRR. The
source code of this paper is available.
