Novel Speech Signal Processing Algorithms for High-Accuracy Classification of Parkinson's Disease
Science Oxford · University of Oxford · +3 more institutions
Abstract
There has been considerable recent research into the connection between Parkinson's disease (PD) and speech impairment. Recently, a wide range of speech signal processing algorithms (dysphonia measures) aiming to predict PD symptom severity using speech signals have been introduced. In this paper, we test how accurately these novel algorithms can be used to discriminate PD subjects from healthy controls. In total, we compute 132 dysphonia measures from sustained vowels. Then, we select four parsimonious subsets of these dysphonia measures using four feature selection algorithms, and map these feature subsets to a binary classification response using two statistical classifiers: random forests and support…
Citation impact
- FWCI
- 15.53
- Percentile
- 100%
- References
- 49
Authors
5- ATAthanasios TsanasCorresponding
Science Oxford, University of Oxford
- MAMax A. Little
University of Oxford, Massachusetts Institute of Technology
- PMPatrick McSharry
University of Oxford
- JSJennifer Spielman
Denver Center for the Performing Arts, University of Colorado Boulder
- LOLorraine O. Ramig
Denver Center for the Performing Arts, University of Colorado Boulder
Topics & keywords
- Statistical classification
- Feature selection
- Random forest
- Binary classification
- Feature (linguistics)
- Support vector machine
- Speech processing
- Computer science
- Reduced inequalities