Residual Neural Network precisely quantifies dysarthria severity-level based on short-duration speech segments

Autor(es)

Gupta, Siddhant ^[1] ; Patil, Ankur T. ^[1] ; Purohit, Mirali ^[1] ; Parmar, Mihir ^[2] ; Patel, Maitreya ^[1] ; Patil, Hemant A. ^[1] ; Guido, Rodrigo Capobianco ^[3]

Afiliação do(s) autor(es)

^[1] Dhirubhai Ambani Inst Informat & Commun Technol I, Speech Res Lab, Gandhinagar 382007 - India

^[2] Arizona State Univ, Tempe, AZ - USA

^[3] Sao Paulo State Univ, Unesp Univ Estadual Paulista, Inst Biociencias Letras & Ciencias Exatas, Rua Cristovao Colombo 2265, BR-15054000 Sao Jose Do Rio Preto, SP - Brazil

• Tipo de documento: Artigo Científico
• Fonte: NEURAL NETWORKS; v. 139, p. 105-117, JUL 2021.
• Citações Web of Science: 0

Resumo

Recently, we have witnessed Deep Learning methodologies gaining significant attention for severitybased classification of dysarthric speech. Detecting dysarthria, quantifying its severity, are of paramount importance in various real-life applications, such as the assessment of patients' progression in treatments, which includes an adequate planning of their therapy and the improvement of speech-based interactive systems in order to handle pathologically-affected voices automatically. Notably, current speech-powered tools often deal with short-duration speech segments and, consequently, are less efficient in dealing with impaired speech, even by using Convolutional Neural Networks (CNNs). Thus, detecting dysarthria severity-level based on short speech segments might help in improving the performance and applicability of those systems. To achieve this goal, we propose a novel Residual Network (ResNet)-based technique which receives short-duration speech segments as input. Statistically meaningful objective analysis of our experiments, reported over standard Universal Access corpus, exhibits average values of 21.35% and 22.48% improvement, compared to the baseline CNN, in terms of classification accuracy and F1-score, respectively. For additional comparisons, tests with Gaussian Mixture Models and Light CNNs were also performed. Overall, the values of 98.90% and 98.00% for classification accuracy and F1-score, respectively, were obtained with the proposed ResNet approach, confirming its efficacy and reassuring its practical applicability. (C) 2021 Elsevier Ltd. All rights reserved. (AU)

• Processo FAPESP: 19/04475-0 - Análise Paraconsistente de Características dos Sinais de Fala: combatendo os ataques de voice spoofing
• Beneficiário: Rodrigo Capobianco Guido
• Modalidade de apoio: Auxílio à Pesquisa - Regular