تقرير
Automatic dysarthric speech detection exploiting pairwise distance-based convolutional neural networks
| العنوان: | Automatic dysarthric speech detection exploiting pairwise distance-based convolutional neural networks |
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| المؤلفون: | Janbakhshi, P., Kodrasi, I., Bourlard, H. |
| سنة النشر: | 2020 |
| مصطلحات موضوعية: | Electrical Engineering and Systems Science - Audio and Speech Processing |
| الوصف: | 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. Comment: accepted at ICASSP 2021 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2011.07545 |
| رقم الأكسشن: | edsarx.2011.07545 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |