Abstract
Parkinson’s disease is a neurological disease which is incurable according to current clinical knowledge. Therefore, early detection and provision of appropriate treatment are of primary importance. Speech is one of the biomarkers that enable the detection of Parkinson’s disease affection. Numerous researches are based on recordings from controlled environments; nonetheless fewer apply real circumstances. In the present study, three objectives were examined: recording fragmentation (paragraph, sentences, time-based), variable encodings (Pulse-Code Modulation [PCM], GSM-Full Rate [FR], G.723.1) and majority voting on 8 kHz records using multiple classifiers. Support Vector Machine (SVM), Long Short-Term Memory (LSTM), i-vector and x-vector classifiers were evaluated in contrast with SVM as baseline. The highest results in accuracy and F1-score were achieved using i-vector models. Although variable encodings generally caused decrease in Parkinson-disease recognition, decline was within 2% - 3% at best. Moreover, fragmentation did not yield a clear outcome though some classifiers performed with the very similar efficiency along the differently fragmented sets. Majority voting did produce a slight increase in classification performance compared to as if no aggregation is used.
Highlights
Parkinson’s disease (PD) is the second most common neurological disorder which is affecting the elderly population
It is noteworthy that this change was within 1% - 2% for i-vector while at most 15% for Long Short-Term Memory (LSTM)
The x-vector and LSTM show a decrease in performance when the models trained with PCM and tested with G.723.1 instead of GSM-FR
Summary
Parkinson’s disease (PD) is the second most common neurological disorder which is affecting the elderly population. It is characterized by neuron death in the substantia-nigra area and accumulation of intracellular protein (α-synuclein) [1]. Several medicines can be applied to ease motor symptoms: dopamine precursors (e.g. Levodopa) or agonists (e.g. apomorphine) can be used to increase the amount of dopamine hormone. A number of therapies (such as movement and speech therapies) have been used to relieve symptoms and improve quality of life [4]. The use of speech therapy can improve the patient’s articulation and speech intelligibility. Medications and therapies are necessary for the rest of the patient’s life as they are only suitable for symptomatic induction. Tele-monitoring systems based on speech are getting more renowned due to the low cost [6]
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