Orozco-Arroyave, J.R., Vásquez-Correa, J.C., Vargas-Bonilla, J.F. et al. (13 more authors) (2018) NeuroSpeech: An open-source software for Parkinson's speech analysis. Digital Signal Processing, 77. pp. 207-221. ISSN 1051-2004
Abstract
A new software for modeling pathological speech signals is presented in this paper. The software is called NeuroSpeech. This software enables the analysis of pathological speech signals considering different speech dimensions: phonation, articulation, prosody, and intelligibility. All the methods considered in the software have been validated in previous experiments and publications. The current version of NeuroSpeech was developed to model dysarthric speech signals from people with Parkinson's disease; however, the structure of the software allows other computer scientists or developers to include other pathologies and/or other measures in order to complement the existing options. Three different tasks can be performed with the current version of the software: (1) the modeling of the speech recordings considering the aforementioned speech dimensions, (2) the automatic discrimination of Parkinson's vs. non-Parkinson's speech signals (if the user has access to recordings of other pathologies, he/she can re-train the system to perform the detection of other diseases), and (3) the prediction of the neurological state of the patient 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 (the user can also train the system to perform the prediction of other kind of scales or degrees of severity).To the best of our knowledge, this is the first software with the characteristics described above, and we consider that it will help other researchers to contribute to the state-of-the-art in pathological speech assessment from different perspectives, e.g., from the clinical point of view for interpretation, and from the computer science point of view enabling the test of different measures and pattern recognition techniques.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2017 Elsevier Inc. |
Keywords: | Parkinson's disease; Dysarthria; Speech processing; 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: | 08 Aug 2017 15:02 |
Last Modified: | 02 Nov 2023 16:23 |
Status: | Published |
Publisher: | Elsevier |
Refereed: | Yes |
Identification Number: | 10.1016/j.dsp.2017.07.004 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:119849 |