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Metadados | Descrição | Idioma |
---|---|---|
Autor(es): dc.contributor | Dhirubhai Ambani Institute of Information and Communication Technology (DA-IICT) | - |
Autor(es): dc.contributor | Arizona State University | - |
Autor(es): dc.contributor | Universidade Estadual Paulista (Unesp) | - |
Autor(es): dc.creator | Gupta, Siddhant | - |
Autor(es): dc.creator | Patil, Ankur T. | - |
Autor(es): dc.creator | Purohit, Mirali | - |
Autor(es): dc.creator | Parmar, Mihir | - |
Autor(es): dc.creator | Patel, Maitreya | - |
Autor(es): dc.creator | Patil, Hemant A. | - |
Autor(es): dc.creator | Guido, Rodrigo Capobianco [UNESP] | - |
Data de aceite: dc.date.accessioned | 2022-02-22T00:53:33Z | - |
Data de disponibilização: dc.date.available | 2022-02-22T00:53:33Z | - |
Data de envio: dc.date.issued | 2021-06-25 | - |
Data de envio: dc.date.issued | 2021-06-25 | - |
Data de envio: dc.date.issued | 2021-07-01 | - |
Fonte completa do material: dc.identifier | http://dx.doi.org/10.1016/j.neunet.2021.02.008 | - |
Fonte completa do material: dc.identifier | http://hdl.handle.net/11449/208481 | - |
Fonte: dc.identifier.uri | http://educapes.capes.gov.br/handle/11449/208481 | - |
Descrição: dc.description | Recently, 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. | - |
Descrição: dc.description | Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) | - |
Descrição: dc.description | Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) | - |
Descrição: dc.description | Speech Research Lab Dhirubhai Ambani Institute of Information and Communication Technology (DA-IICT) | - |
Descrição: dc.description | Arizona State University | - |
Descrição: dc.description | Instituto de Biociências Letras e Ciências Exatas Unesp - Univ Estadual Paulista (São Paulo State University), Rua Cristóvão Colombo 2265, Jd Nazareth | - |
Descrição: dc.description | Instituto de Biociências Letras e Ciências Exatas Unesp - Univ Estadual Paulista (São Paulo State University), Rua Cristóvão Colombo 2265, Jd Nazareth | - |
Descrição: dc.description | FAPESP: 2019/04475-0 | - |
Descrição: dc.description | CNPq: 306808/2018-8 | - |
Formato: dc.format | 105-117 | - |
Idioma: dc.language | en | - |
Relação: dc.relation | Neural Networks | - |
???dc.source???: dc.source | Scopus | - |
Palavras-chave: dc.subject | CNN | - |
Palavras-chave: dc.subject | Dysarthria | - |
Palavras-chave: dc.subject | ResNet | - |
Palavras-chave: dc.subject | Severity-level | - |
Palavras-chave: dc.subject | Short-speech segments | - |
Título: dc.title | Residual Neural Network precisely quantifies dysarthria severity-level based on short-duration speech segments | - |
Tipo de arquivo: dc.type | livro digital | - |
Aparece nas coleções: | Repositório Institucional - Unesp |
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