NDLI: Detection of persons with Parkinson's disease by acoustic, vocal, and prosodic analysis

Content Provider	IEEE Xplore Digital Library
Author	Bocklet, T. Noth, E. Stemmer, G. Ruzickova, H. Rusz, J.
Copyright Year	2011
Description	Author affiliation: Phoniatric and Paedaudiologic Department, University Clinics Erlangen, Germany (Bocklet, T.) \|\| Pattern Recognition, University Erlangen-Nuremberg, Germany (Noth, E.; Stemmer, G.) \|\| Department of Neurology and Centre of Clinical Neuroscience, Charles University in Prague, Czech Republic (Ruzickova, H.; Rusz, J.)
Abstract	70% to 90% of patients with Parkinson's disease (PD) show an affected voice. Various studies revealed, that voice and prosody is one of the earliest indicators of PD. The issue of this study is to automatically detect whether the speech/voice of a person is affected by PD. We employ acoustic features, prosodic features and features derived from a two-mass model of the vocal folds on different kinds of speech tests: sustained phonations, syllable repetitions, read texts and monologues. Classification is performed in either case by SVMs. A correlation-based feature selection was performed, in order to identify the most important features for each of these systems. We report recognition results of 91% when trying to differentiate between normal speaking persons and speakers with PD in early stages with prosodic modeling. With acoustic modeling we achieved a recognition rate of 88% and with vocal modeling we achieved 79%. After feature selection these results could greatly be improved. But we expect those results to be too optimistic. We show that read texts and monologues are the most meaningful texts when it comes to the automatic detection of PD based on articulation, voice, and prosodic evaluations. The most important prosodic features were based on energy, pauses and F0. The masses and the compliances of spring were found to be the most important parameters of the two-mass vocal fold model.
Starting Page	478
Ending Page	483
File Size	147972
Page Count	6
File Format	PDF
ISBN	9781467303651
e-ISBN	9781467303675
e-ISBN	9781467303668
DOI	10.1109/ASRU.2011.6163978
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-12-11
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Speech Acoustics Vectors Optimization Springs Computational modeling Feature extraction
Content Type	Text
Resource Type	Article

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