Orozco-Arroyave, J.R., Vásquez-Correa, J.C., Vargas-Bonilla, J.F. et al. (13 more authors) (2018) NeuroSpeech. SoftwareX, 8. pp. 69-70.
Abstract
NeuroSpeech is a software for modeling pathological speech signals considering different speech dimensions: phonation, articulation, prosody, and intelligibility. Although it was developed to model dysarthric speech signals from Parkinson's patients, its structure allows other computer scientists or developers to include other pathologies and/or measures. Different tasks can be performed: (1) modeling of the signals considering the aforementioned speech dimensions, (2) automatic discrimination of Parkinson's vs. non-Parkinson's, and (3) prediction of the neurological state according to the Unified Parkinson's Disease Rating Scale (UPDRS) score. The prediction of the dysarthria level according to the Frenchay Dysarthria Assessment scale is also provided.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2017 Published by Elsevier B.V. This is an author produced version of a paper subsequently published in SoftwareX. Uploaded in accordance with the publisher's self-archiving policy. Article available under the terms of the CC-BY-NC-ND licence (https://creativecommons.org/licenses/by-nc-nd/4.0/) |
Keywords: | Speech processing; Dysarthria; Parkinson’s disease; Phonation; Articulation; Prosody; Intelligibility; Python; Software |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) > School of Health and Related Research (Sheffield) > ScHARR - Sheffield Centre for Health and Related Research |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 04 Jan 2018 11:42 |
Last Modified: | 14 Jul 2020 10:16 |
Published Version: | https://doi.org/10.1016/j.softx.2017.08.004 |
Status: | Published |
Publisher: | Elsevier |
Refereed: | Yes |
Identification Number: | 10.1016/j.softx.2017.08.004 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:125611 |