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Transfer learning to detect parkinson’s disease from speech in different languages using convolutional neural networks with layer freezing

Rios-Urrego CD, Vasquez Correa J, Orozco Arroyave JR, Nöth E (2020)

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Publication Type: Conference contribution

Publication year: 2020

Journal

Publisher: Springer Science and Business Media Deutschland GmbH

Book Volume: 12284 LNAI

Pages Range: 331-339

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_36

Abstract

Parkinson’s Disease is a neurodegenerative disorder characterized by motor symptoms such as resting tremor, bradykinesia, rigidity and freezing of gait. The most common symptom in speech is called hypokinetic dysarthria, where speech is characterized by monotone intensity, low pitch variability and poor prosody that tends to fade at the end of the utterance. This study proposes the classification of patients with Parkinson’s Disease and healthy controls in three different languages (Spanish, German, and Czech) using a transfer learning strategy. The process is further improved by freezing consecutive different layers of the architecture. We hypothesize that some convolutional layers characterize the disease and others the language. Therefore, when a fine-tuning in the transfer learning is performed, it is possible to find the topology that best adapts to the target language and allows an accurate detection of Parkinson’s Disease. The proposed methodology uses Convolutional Neural Networks trained with Mel-scale spectrograms. Results indicate that the fine-tuning of the neural network does not provide good performance in all languages while fine-tuning of individual layers improves the accuracy by up to 7%. In addition, the results show that Transfer Learning among languages improves the performance in up to 18% when compared to a base model used to initialize the weights of the network.

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How to cite

APA:

Rios-Urrego, C.D., Vasquez Correa, J., Orozco Arroyave, J.R., & Nöth, E. (2020). Transfer learning to detect parkinson’s disease from speech in different languages using convolutional neural networks with layer freezing. 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. 331-339). Brno, CZ: Springer Science and Business Media Deutschland GmbH.

MLA:

Rios-Urrego, Cristian David, et al. "Transfer learning to detect parkinson’s disease from speech in different languages using convolutional neural networks with layer freezing." 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. 331-339.

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