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. |
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| Projects |
Idiap MOSPEEDI |
| Authors | |
| Crossref by |
Janbakhshi_ICASSP_2021 |
| Added by: | [ADM] |
| Total mark: | 0 |
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