10:00 - 17:00 | Tue 30 Oct | Foyer | B1P-D
Parkinson’s disease (PD) is known as neurodegenerative disorder causing speech impairment in patients.Therefore, voice recording has been used as useful tool for diagnosis of PD. For the first time in this study, we have tested the effectiveness of deep convolutional neural network (DCNN) in distinguishing between Parkinson’s and healthy voices using spectral features from sustained phoneme /a/ (as pronounced in “car”). Various designs of the DCNN architecture were investigated on two seconds of raw pathological and healthy voices. The best network achieved accuracy of 75.7% for distinguish between Parkinson and healthy samples. This study also investigated the effect of parameters such as audio length, frame size, number of convolutional layers and feature maps as well as topology of fully connected layers on the accuracy of the classification outcome.
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