Super-gaussianity of Speech Spectral Coefficients as a Potential Biomarker for Dysarthric Speech Detection

Abstract

Parkinson's disease (PD) and Amyotrophic Lateral Sclerosis (ALS) are progressive neurodegenerative diseases which, among other symptoms, cause dysarthria of speech. To assist the clinical diagnosis and treatment of neurological diseases, several studies have addressed the characterization and classification of healthy and dysarthric speech. However, most contributions deal with PD speech, with significantly fewer results presented for ALS speech. The objective of this paper is to show that ALS speech has a similar statistical distribution as PD speech, with the complex spectral coefficients being significantly less super-Gaussian than healthy speech spectral coefficients. In addition, a method to exploit the super-Gaussianity of speech signals as a feature to classify healthy and dysarthric speech is presented and evaluated. The proposed approach is evaluated on a French database of healthy and dysarthric (PD and ALS) speech. Experimental results show that the use of the super-Gaussianity of speech signals yields a significantly higher classification accuracy than state-of-the-art features such as fundamental frequency, jitter, shimmer, harmonics-to-noise ratio, or Mel frequency cepstral coefficients.