CNN Classification of Parkinson's Disease using STFT Spectrum of User's Running Speech

Speech deterioration is a well-established indicator for early detection of Parkinson disease (PD). The remote monitoring of PD symptoms is practicable by analyzing the person's speech. This study demonstrates that deep learning of speech spectrum images of Running speech from Parkinson's disease (PD) patients and healthy controls (HC) results in a convolution neural network model with high accuracy for classifying PD. Earlier work for PD detection was totally based on the computation of speech features. Complicated speech feature computations take time and require a large amount of memory access. Using a spectrum image representation of speech in a deep convolution neural network gives very high accuracy without the need for feature computation, and selection in this work. This is possible because the convolution layer learns the features from the spectrum automatically and in this work, it is proposed to work with PD speech database for Running speech. In this case, the short time Fourier transform (STFT) is a good choice for representing speech as images. The training accuracy achieved in the experiment is 99.48% and validation accuracy are 79.10%. This method performs best and is a well-founded method for classifying PD from running speech signals.