Parkinson disease occurs when certain clusters of brain cells are unable to generate dopamine which is needed to regulate the number of the motor and non-motor activity of the human body. Besides, contributing to speech, visual, movement, urinary problems, Parkinson disease also increases the risks of depression, anxiety, and panic attacks, disturbances of sleep. Parkinson disease diagnosis via proper interpretation of the vocal and speech data is an important classification problem. In this paper, a Parkinson disease diagnosis is realized by using the speech impairments, which is one of the earliest indicator for Parkinson disease. For this purpose, a deep neural network classifier, which contains a stacked autoencoder and a softmax classifier, is proposed. The several simulations are performed over two databases to demonstrate the effectiveness of the deep neural network classifier. The results of the proposed classifier are compared with the results of the state-of-art classification method. The experimental results and statistical analyses are showed that the deep neural network classifier is very efficient classifier for Parkinson disease diagnosis.