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Dysarthria detection based on a deep learning model with a clinically-interpretable layer
Studies have shown deep neural networks (DNN) as a potential tool for classifying dysarthric speakers and controls. However, representations used to train DNNs are largely not clinically interpretable, which limits clinical value. Here, a model with a bottleneck layer is trained to jointly learn a c...
| Autores principales: | , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Acoustical Society of America
2023
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9835557/ https://www.ncbi.nlm.nih.gov/pubmed/36725533 http://dx.doi.org/10.1121/10.0016833 |