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

Bocklet T, Rusz J, Nöth E, Ruzickova H, Stemmer G (2011)

Publication Type: Conference contribution, Conference Contribution

Publication year: 2011

Original Authors: Bocklet Tobias, Nöth Elmar, Stemmer Georg, Ruzickova Hana, Rusz Jan

Pages Range: 478-483

Conference Proceedings Title: 2011 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)

Event location: Waikoloa, HI US

ISBN: 9781467303675

URI: http://www5.informatik.uni-erlangen.de/Forschung/Publikationen/2011/Bocklet11-DOP.pdf

DOI: 10.1109/ASRU.2011.6163978

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. © 2011 IEEE.

Authors with CRIS profile

Tobias Bocklet Professur für Informatik (Mustererkennung) Elmar Nöth Professur für Informatik (Mustererkennung)

How to cite

APA:

Bocklet, T., Rusz, J., Nöth, E., Ruzickova, H., & Stemmer, G. (2011). Detection of persons with Parkinson's disease by acoustic, vocal, and prosodic analysis. In IEEE Signal Processing Society (Eds.), 2011 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU) (pp. 478-483). Waikoloa, HI, US.

MLA:

Bocklet, Tobias, et al. "Detection of persons with Parkinson's disease by acoustic, vocal, and prosodic analysis." Proceedings of the 2011 IEEE Workshop on Automatic Speech Recognition and Understanding, ASRU 2011, Waikoloa, HI Ed. IEEE Signal Processing Society, 2011. 478-483.

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