Design and Validation of a New Diagnostic Tool for the Differentiation of Pathological Voices in Parkinsonian Patients.

Pathological speech, in its many forms, is a symptom of numerous serious diseases affecting millions of people worldwide, including more than 10 million Parkinson patients. Here, a powerful method is proposed for detecting pathological speech, using a two-dimensional (2D) convolutional neural network (CNN). Spectrograms are extracted from voice recordings of healthy and Parkinson diagnosed patients, which are fed into the CNN architecture.