Abstract
Speech disorders are common symptoms among Parkinson’s disease patients and affect the speech of patients in different aspects. Currently, there are few studies that consider the phonological dimension of Parkinson’s speech. In this work, we use a recently developed method to extract phonological features from speech signals. These features are based on the Sound Patterns of English phonological model. The extraction is performed using pre-trained Deep Neural Networks to infer the probabilities of phonological features from short-time acoustic features. An i-vector extractor is trained with the phonological features. The extracted i-vectors are used to classify patients and healthy speakers and assess their neurological state and dysarthria level. This approach could be helpful to assess new specific speech aspects such as the movement of different articulators involved in the speech production process.
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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 Sklodowska-Curie Grant Agreement No. 766287.
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Garcia-Ospina, N., Arias-Vergara, T., Vásquez-Correa, J.C., Orozco-Arroyave, J.R., Cernak, M., Nöth, E. (2018). Phonological i-Vectors to Detect 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_50
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