Assessing the dysarthria level of parkinson’s disease patients with gmm-ubm supervectors using phonological posteriors and diadochokinetic exercises
Miller GF, Vasquez Correa J, Nöth E (2020)
Publication Type: Conference contribution
Publication year: 2020
Journal
Publisher: Springer Science and Business Media Deutschland GmbH
Book Volume: 12284 LNAI
Pages Range: 356-365
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Brno
ISBN: 9783030583224
DOI: 10.1007/978-3-030-58323-1_39
Abstract
Parkinson’s disease (PD) is a neuro-degenerative disorder that produces symptoms such as tremor, slowed movement, and a lack of coordination. One of the earliest indicators is a combination of different speech impairments called hypokinetic dysarthria. Some indicators that are prevalent in the speech of Parkinson’s patients include, imprecise production of stop consonants, vowel articulation impairment and reduced loudness. In this paper, we examine those features using phonological posterior probabilities obtained via parallel bidirectional recurrent neural networks. We also utilize information such as the velocity and acceleration curve of the signal envelope, and the peak amplitude slope and variance to model the quality of pronunciation for a given speaker. With our feature set, we train Gaussian Mixture Model based Universal Background Models for a set of training speakers and adapt a model for each individual speaker using a form of Bayesian adaptation. With the parameters describing each speaker model, we train SVM and Random Forest classifiers to discriminate PD patients and Healthy Controls (HC), and to determine the severity of dysarthria for each speaker compared with ratings assessed by expert phoneticians.
Authors with CRIS profile
Juan Vasquez Correa
Lehrstuhl für Informatik 14 (Bild- und Sprachverarbeitung)
Elmar Nöth
Professur für Informatik (Mustererkennung)
How to cite
APA:
Miller, G.F., Vasquez Correa, J., & Nöth, E. (2020). Assessing the dysarthria level of parkinson’s disease patients with gmm-ubm supervectors using phonological posteriors and diadochokinetic exercises. In Petr Sojka, Ivan Kopecek, Karel Pala, Aleš Horák (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 356-365). Brno, CZ: Springer Science and Business Media Deutschland GmbH.
MLA:
Miller, Gabriel Figueiredo, Juan Vasquez Correa, and Elmar Nöth. "Assessing the dysarthria level of parkinson’s disease patients with gmm-ubm supervectors using phonological posteriors and diadochokinetic exercises." Proceedings of the 23rd International Conference on Text, Speech, and Dialogue, TSD 2020, Brno Ed. Petr Sojka, Ivan Kopecek, Karel Pala, Aleš Horák, Springer Science and Business Media Deutschland GmbH, 2020. 356-365.
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