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 [BibTeX] [Marc21]
AUTOMATIC DYSARTHRIC SPEECH DETECTION EXPLOITING PAIRWISE DISTANCE-BASED CONVOLUTIONAL NEURAL NETWORKS
Type of publication: Idiap-RR
Citation: Janbakhshi_Idiap-RR-32-2020
Number: Idiap-RR-32-2020
Year: 2020
Month: 12
Institution: Idiap
Note: Submitted
Abstract: 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.
Keywords:
Projects Idiap
MOSPEEDI
Authors Janbakhshi, Parvaneh
Kodrasi, Ina
Bourlard, Hervé
Crossref by Janbakhshi_ICASSP_2021
Added by: [ADM]
Total mark: 0
Attachments
  • Janbakhshi_Idiap-RR-32-2020.pdf (MD5: 9899aabcf3fef2a12feaafbc231f2ee7)
Notes