Deep Neural Quantization for Speech Detection of Parkinson Disease

Among all the diseases that nowadays people all around the world suffer, Parkinson's Disease is one of those neuro-degenerative disorders heavily impacting, and unfortu-nately expected to increase, the well-being of, especially, elderly individuals. Besides traditional medical treatments, timely and unobtrusive ways to accurately detect the onset of this disease can rely on Machine Learning (ML) and Deep Learning (DL) techniques, also because of their ability to efficiently extract information from multidimensional data on heterogeneous platforms (including, for instance, constrained Internet of Things devices). This paper presents an experimental performance evaluation of several floating point and quantized ML and DL models which can be deployed efficiently on a tiny microcontroller, namely a STM32U5 micro controller device (available in the STMicroelectronics device cloud). They have been applied to a public Italian voice speech dataset in order to classify the Parkinson Disease in three classes of patients. The experimental results demonstrate the applicability of Neural Network (NN)-based approaches for detecting the disease, as well as the deployability of traditional ML models on tiny resource-constrained devices, allowing a substantial flash memory usage reduction (when compared to non-quantized models) while keeping relatively high accuracy.