  Automatic dysarthric speech detection can provide reliable and cost-effective
computer-aided tools to assist the clinical diagnosis and management of
dysarthria. In this paper we propose a novel automatic dysarthric speech
detection approach based on analyses of pairwise distance matrices using
convolutional neural networks (CNNs). We represent utterances through
articulatory posteriors and consider pairs of phonetically-balanced
representations, with one representation from a healthy speaker (i.e., the
reference representation) and the other representation from the test speaker
(i.e., test representation). Given such pairs of reference and test
representations, features are first extracted using a feature extraction
front-end, a frame-level distance matrix is computed, and the obtained distance
matrix is considered as an image by a CNN-based binary classifier. The feature
extraction, distance matrix computation, and CNN-based classifier are jointly
optimized in an end-to-end framework. Experimental results on two databases of
healthy and dysarthric speakers for different languages and pathologies show
that the proposed approach yields a high dysarthric speech detection
performance, outperforming other CNN-based baseline approaches.
