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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 15/2021

09.02.2021 | Original Article

Insight into an unsupervised two-step sparse transfer learning algorithm for speech diagnosis of Parkinson’s disease

verfasst von: Yongming Li, Xinyue Zhang, Pin Wang, Xiaoheng Zhang, Yuchuan Liu

Erschienen in: Neural Computing and Applications | Ausgabe 15/2021

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Abstract

Speech diagnosis of Parkinson’s disease (PD) as a non-invasive and simple diagnosis method is particularly worth exploring. However, the number of samples of speech-based PD is relatively small, and there exist discrepancies in the distribution between subjects. In order to solve the two problems, a novel unsupervised two-step sparse transfer learning is proposed in this paper to tackle with PD speech diagnosis. In the first step, convolution sparse coding with the coordinate selection of samples and features is designed to learn speech structure from the source domain to replenish sample information of the target domain. In the second step, joint local structure distribution alignment is designed to maintain the neighbor relationship between the respective samples of the training set and test set, and reduce the distribution difference between the two domains at the same time. Two representative public PD speech datasets and one real-world PD speech dataset were exploited to verify the proposed method on PD speech diagnosis. Experimental results demonstrate that each step of the proposed method has a positive effect on the PD speech classification results, and it also delivers superior performance over the existing relative methods.
Vorheriger Artikel Self-guided deep deterministic policy gradient with multi-actor
Nächster Artikel A deep embedding model for knowledge graph completion based on attention mechanism

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Metadaten
Titel
Insight into an unsupervised two-step sparse transfer learning algorithm for speech diagnosis of Parkinson’s disease
verfasst von
Yongming Li
Xinyue Zhang
Pin Wang
Xiaoheng Zhang
Yuchuan Liu
Publikationsdatum
09.02.2021
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 15/2021
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-021-05741-0

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