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

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dc.contributor.authorGupta, Siddhant
dc.contributor.authorPatil, Ankur T.
dc.contributor.authorPurohit, Mirali
dc.contributor.authorParmar, Mihir
dc.contributor.authorPatel, Maitreya
dc.contributor.authorPatil, Hemant A.
dc.contributor.authorGuido, Rodrigo Capobianco [UNESP]
dc.contributor.institutionDhirubhai Ambani Institute of Information and Communication Technology (DA-IICT)
dc.contributor.institutionArizona State University
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.date.accessioned2021-06-25T11:12:50Z
dc.date.available2021-06-25T11:12:50Z
dc.date.issued2021-07-01
dc.description.abstractRecently, we have witnessed Deep Learning methodologies gaining significant attention for severity-based 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.en
dc.description.affiliationSpeech Research Lab Dhirubhai Ambani Institute of Information and Communication Technology (DA-IICT)
dc.description.affiliationArizona State University
dc.description.affiliationInstituto de Biociências Letras e Ciências Exatas Unesp - Univ Estadual Paulista (São Paulo State University), Rua Cristóvão Colombo 2265, Jd Nazareth
dc.description.affiliationUnespInstituto de Biociências Letras e Ciências Exatas Unesp - Univ Estadual Paulista (São Paulo State University), Rua Cristóvão Colombo 2265, Jd Nazareth
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.description.sponsorshipConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.description.sponsorshipIdFAPESP: 2019/04475-0
dc.description.sponsorshipIdCNPq: 306808/2018-8
dc.format.extent105-117
dc.identifierhttp://dx.doi.org/10.1016/j.neunet.2021.02.008
dc.identifier.citationNeural Networks, v. 139, p. 105-117.
dc.identifier.doi10.1016/j.neunet.2021.02.008
dc.identifier.issn1879-2782
dc.identifier.issn0893-6080
dc.identifier.scopus2-s2.0-85102061061
dc.identifier.urihttp://hdl.handle.net/11449/208481
dc.language.isoeng
dc.relation.ispartofNeural Networks
dc.sourceScopus
dc.subjectCNN
dc.subjectDysarthria
dc.subjectResNet
dc.subjectSeverity-level
dc.subjectShort-speech segments
dc.titleResidual Neural Network precisely quantifies dysarthria severity-level based on short-duration speech segmentsen
dc.typeArtigo
unesp.author.orcid0000-0002-0924-8024[7]

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Artigos - Ciência da Computação e Estatística - IBILCE