Abstract
Parkinson’s disease is a neurodegenerative disorder characterized by a variety of motor symptoms, including several impairments in the speech production process. Recent studies show that deep learning models are highly accurate to assess the speech deficits of the patients; however most of the architectures consider static features computed from a complete utterance. Such an approach is not suitable to model the dynamics of the speech signal when the patients pronounce different sounds. Phonological features can be used to characterize the voice quality of the speech, which is highly impaired in patients suffering from Parkinson’s disease. This study proposes a deep architecture based on recurrent neural networks with gated recurrent units combined with phonological posteriors to assess the speech deficits of Parkinson’s patients. The aim is to model the time-dependence of consecutive phonological posteriors, which follow the sound patterns of English phonological model. The results show that the proposed approach is more accurate than a baseline based on standard acoustic features to assess the speech deficits of the patients.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bengio, Y., Simard, P., Frasconi, P.: Learning long-term dependencies with gradient descent is difficult. IEEE Trans. Neural Netw. 5(2), 157–166 (1994)
Goetz, C.G., et al.: Movement Disorder Society-sponsored revision of the Unified Parkinson’s Disease Rating Scale (MDS-UPDRS): scale presentation and clinimetric testing results. Mov. Disord. 23(15), 2129–2170 (2008)
Cernak, M., Potard, B., Garner, P.N.: Phonological vocoding using artificial neural networks. In: IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP, pp. 4844–4848. IEEE (2015)
Cernak, M., et al.: Characterisation of voice quality of Parkinsons disease using differential phonological posterior features. Comput. Speech Lang. 46, 96–208 (2017)
Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. In: Conference on Empirical Methods in Natural Language Processing, EMNLP, pp. 1724–1734 (2014)
Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Deep Learning and Representation Learning Workshop (2014)
Hlavnicka, J., Cmejla, R., Tykalova, T., Sonka, K., Ruzicka, E., Rusz, J.: Automated analysis of connected speech reveals early biomarkers of Parkinson’s disease in patients with rapid eye movement sleep behaviour disorder. Nat. Sci. Rep. 7(12), 1–13 (2017)
Hornykiewicz, O.: Biochemical aspects of Parkinson’s disease. Neurology 51(2 Suppl. 2), S2–S9 (1998)
Irie, K., Tüske, Z., Alkhouli, T., Schlüter, R., Ney, H.: LSTM, GRU, highway and a bit of attention: an empirical overview for language modeling in speech recognition. In: Proceedings of INTERSPEECH, pp. 3519–3523 (2016)
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)
Logemann, J.A., Fisher, H.B., Boshes, B., Blonsky, E.R.: Frequency and cooccurrence of vocal tract dysfunctions in the speech of a large sample of Parkinson patients. J. Speech Hear. Disord. 43(1), 47–57 (1978)
Orozco-Arroyave, J.R., Vásquez-Correa, J.C., et al.: NeuroSpeech: an open-source software for Parkinson’s speech analysis. Dig. Signal Process. (2017, in press)
Orozco-Arroyave, J.R., et al.: New Spanish speech corpus database for the analysis of people suffering from Parkinson’s disease. In: Language Resources and Evaluation Conference, LREC, pp. 342–347 (2014)
Snoek, J., Larochelle, H., Adams, R.P.: Practical Bayesian optimization of machine learning algorithms. In: Advances in Neural Information Processing Systems, NIPS, pp. 2951–2959 (2012)
Tu, M., Berisha, V., Liss, J.: Interpretable objective assessment of dysarthric speech based on deep neural networks. In: Proceedings of INTERSPEECH, pp. 1849–1853 (2017)
Vásquez-Correa, J.C., Orozco-Arroyave, J.R., Nöth, E.: Convolutional neural network to model articulation impairments in patients with Parkinson’s disease. In: Proceedings of INTERSPEECH, pp. 314–318 (2017)
Acknowledgments
The work reported here was financed by CODI from University of Antioquia by grants Number 2015–7683. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Grant Agreement No. 766287.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Vásquez-Correa, J.C., Garcia-Ospina, N., Orozco-Arroyave, J.R., Cernak, M., Nöth, E. (2018). 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) Text, Speech, and Dialogue. TSD 2018. Lecture Notes in Computer Science(), vol 11107. Springer, Cham. https://doi.org/10.1007/978-3-030-00794-2_49
Download citation
DOI: https://doi.org/10.1007/978-3-030-00794-2_49
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-00793-5
Online ISBN: 978-3-030-00794-2
eBook Packages: Computer ScienceComputer Science (R0)