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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Cernak, M., Orozco-Arroyave, J., Rudzicz, F., Christensen, H., Vásquez-Correa, J., Nöth, E.: Characterisation of voice quality of Parkinson’s disease using differential phonological posterior features. Comput. Speech Lang. 46, 196–208 (2017)
Chandrasekaran, C., Trubabnova, A., Sébastien, S., Caplier, A., Ghazanfar, A.: The natural statistics of audiovisual speech. PLoS Comput. Biol. 5, e1000436 (2009)
Chenausky, K., MacAuslan, J., Goldhor, R.: Acoustic analysis of PD speech. Parkinson’s Dis. (2011)
Duffy, J.: Motor Speech Disorders: Substrates, Differential Diagnosis, and Management. Elsevier Health Sciences, Amsterdam (2013)
Forrest, K., Weismer, G., Turner, G.: Kinematic, acoustic, and perceptual analyses of connected speech produced by Parkinsonian and normal geriatric adults. J. Acoust. Soc. Am. 85, 2608 (1989)
Godino-Llorente, J., Shattuck-Hufnagel, S., Choi, S., Moro-Velazquez, L., Gomez-Garcia, J.: Towards the identification of idiopathic Parkinson’s disease from the speech. New articulatory kinetic biomarkers. PloS one 12, e0189583 (2017)
He, L., Dellwo, V.: Amplitude envelope kinematics of speech signal: parameter extraction and applications. In: 28. Konferenz Elektronische Sprachsignalverarbeitung 2017, Saarbrücken (2017)
Hornykiewicz, O.: Biochemical aspects of Parkinson’s disease. Neurology 51, S2–S9 (1998)
Lai, B., Joseph, K.: Epidemiology of Parkinson’s disease. BC Med. J. 43, 133–137 (2001)
Lansford, K., Liss, J., Caviness, J., Utianski, R.: A cognitive-perceptual approach to conceptualizing speech intelligibility deficits and remediation practice in hypokinetic dysarthria. In: Communication Impairments in Parkinson’s Disease, vol. 2011 (2011)
Moro-Velazquez, L., et al.: A forced gaussians based methodology for the differential evaluation of Parkinson’s disease by means of speech processing. Biomed. Signal Process. Control 48, 205–220 (2019)
Orozco-Arroyave, J.R., Arias-Londoño, J.D., Vargas-Bonilla, J.F., Gonzalez-Rátiva, M.C., Nöth, E.: New spanish speech corpus database for the analysis of people suffering from Parkinson’s disease. In: Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC 2014), pp. 342–347 (2014)
Reynolds, D.: Comparison of background normalization methods for text-independent speaker verification. In: Proceedings of the European Conference on Speech Communication and Technology, pp. 963–966 (1997)
Reynolds, D., Quatieri, T., Dunn, R.: Gaussian Mixture Models, pp. 659–663. Springer, Boston (2009). https://doi.org/10.1007/978-0-387-73003-5
Rodriguez-Oroz, M., et al.: Initial clinical manifestations of Parkinson’s disease: features and pathophysiological mechanisms. In: The Lancet Neurology, vol. 8, pp. 1128–1139. Lippincott-Raven (2009)
Rueda, A., Vásquez-Correa, J., Rios-Urrego, C., Orozco-Arroyave, J., Krishnan, S., Nöth, E.: Feature representation of pathophysiology of parkinsonian dysarthria. In: Proceedings of INTERSPEECH, pp. 3048–3052 (2019)
Rusz, J.: Detecting speech disorders in early Parkinson’s disease by acoustic analysis. J. Acoust. Soc. Am. (2018)
Südhof, T.: Basic neurochemistry: molecular, cellular and medical aspects. In: Neurology 1998. Lippincott-Raven (1999)
Vásquez-Correa, J.C., Garcia-Ospina, N., Orozco-Arroyave, J.R., Cernak, M., Nöth, E.: Phonological posteriors and GRU recurrent units to assess speech impairments of patients with Parkinson’s disease. In: Sojka, P., Horák, A., Kopeček, I., Pala, K. (eds.) TSD 2018. LNCS (LNAI), vol. 11107, pp. 453–461. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00794-2_49
Vásquez-Correa, J., Klumpp, P., Orozco-Arroyave, J., Nöth, E.: Phonet: a tool based on gated recurrent neural networks to extract phonological posteriors from speech. Proc. Interspeech 2019, 549–553 (2019)
Vásquez-Correa, J., Orozco-Arroyave, J., Bocklet, T., Nöth, E.: Towards an automatic evaluation of the dysarthria level of patients with Parkinson’s disease. J. Commun. Disord. 76, 21–36 (2018)
Vásquez-Correa, J.C., Rios-Urrego, C.D., Rueda, A., Orozco-Arroyave, J.R., Krishnan, S., Nöth, E.: Articulation and empirical mode decomposition features in diadochokinetic exercises for the speech assessment of Parkinson’s disease patients. In: Nyström, I., Hernández Heredia, Y., Milián Núñez, V. (eds.) CIARP 2019. LNCS, vol. 11896, pp. 688–696. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-33904-3_65
You, C., Lee, K.A., Li, H.: GMM-SVM kernel with a Bhattacharyya-based distance for speaker recognition. IEEE Trans. Audio Speech Lang. Process. 18, 1300–1312 (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Miller, G.F., Vásquez-Correa, J.C., Nöth, E. (2020). Assessing the Dysarthria Level of Parkinson’s Disease Patients with GMM-UBM Supervectors Using Phonological Posteriors and Diadochokinetic Exercises. In: Sojka, P., Kopeček, I., Pala, K., Horák, A. (eds) Text, Speech, and Dialogue. TSD 2020. Lecture Notes in Computer Science(), vol 12284. Springer, Cham. https://doi.org/10.1007/978-3-030-58323-1_39
Download citation
DOI: https://doi.org/10.1007/978-3-030-58323-1_39
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-58322-4
Online ISBN: 978-3-030-58323-1
eBook Packages: Computer ScienceComputer Science (R0)