Paper Info
Title
Classification of Parkinson's disease Using Pitch Synchronous Speech Analysis.
Abstract
Human speech production is a complex task that demands synchronized cognitive and muscular functioning. Assessment of a Parkinson's disease (PD) patient's speech using computational methods is a growing field of research. Existing methodologies aim at extraction and usage of features from speech to capture perturbations due to PD. In this paper, we propose a novel methodology for feature extraction and analysis. Features are extracted from each pitch cycle of the speech and variances of the features are used for analysis making this a pitch synchronous methodology. Dimensionality problem is addressed by feature selection, which is followed by an unsupervised k-means clustering to perform classification. A dataset containing 40 participants, 22 (7 female and 15 male) PD and 18 (12 female and 6 male) healthy controls (HC) is used for evaluation. The promising results yielded from this study provides support for our hypothesis that pitch synchronous speech analysis can be useful in PD analysis.
Year
DOI
Venue
2018
10.1109/EMBC.2018.8512481
EMBC
Field
DocType
Volume
Computer vision,Synchronous motor,Feature selection,Computer science,Speech recognition,Feature extraction,Curse of dimensionality,Correlation,Artificial intelligence,Cluster analysis,Cognition,Speech production
Conference
2018
Citations 
PageRank 
References 
0
0.34
0
Authors
2
Name
Order
Citations
PageRank
Sai Bharadwaj Appakaya100.68
Ravi Sankar265655.66