Through the Speech and Vocal Signals Hidden Secrets: An Explainable Methodology for Neurological Diseases Early Detection.

Neurodegenerative diseases progressively damage brain and nervous systems impairing their functionality. Early diagnosis can improve the efficacy of treatments and patient's life quality. Biomarkers extracted from the human voice can be a simple, efficient, and non-invasive methodology to screen neurodegenerative diseases such as Parkinson's (PD) and multiple sclerosis (MS). Nevertheless, there is still a lack of reliable and clinically approved methodologies required in large-scale patient applications. We define a methodology for features extracted from voice signals as non-invasive indices for early diagnosis of neurodegenerative diseases. We combine and analyze vowels and speech using a set of machine learning (ML) algorithms trained on a combined set of signal features such as acoustic, articulation, and cepstral ones. The methodology has been fully implemented and applied to a dataset of normophonic and pathological voice signals. Experimental results proved that methodology is able to distinguish healthy from pathological voices, with reliable performances, such as accuracy of 97.5%, sensitivity of 98.5%, precision of 97.0%, F1-score of 98.0%, the Matthews correlation coefficient of 0.95, and AUC of 0.98. Finally, the proposed methodology provides explainability tasks for neurological biomarkers identification from speech and vocal features, confirming its reliability. A github repository with data sample and code is available at https://github.com/PatriziaVizza/SpeechAndVocalSignalsAnalysis.