Deep Double-Side Learning Ensemble Model for Few-Shot Parkinson Speech Recognition Article Swipe

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Yongming Li , Lang Zhou , Lingyun Qin , Yuwei Zeng , Yuchuan Liu , Yan Lei , Pin Wang , Fan Li · YOU? · · 2020 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2006.11593

Diagnosis and therapeutic effect assessment of Parkinson disease based on voice data are very important,but its few-shot learning problem is challenging.Although deep learning is good at automatic feature extraction, it suffers from few-shot learning problem. Therefore, the general effective method is first conduct feature extraction based on prior knowledge, and then carry out feature reduction for subsequent classification. However, there are two major problems: 1) Structural information among speech features has not been mined and new features of higher quality have not been reconstructed. 2) Structural information between data samples has not been mined and new samples with higher quality have not been reconstructed. To solve these two problems, based on the existing Parkinson speech feature data set, a deep double-side learning ensemble model is designed in this paper that can reconstruct speech features and samples deeply and simultaneously. As to feature reconstruction, an embedded deep stacked group sparse auto-encoder is designed in this paper to conduct nonlinear feature transformation, so as to acquire new high-level deep features, and then the deep features are fused with original speech features by L1 regularization feature selection method. As to speech sample reconstruction, a deep sample learning algorithm is designed in this paper based on iterative mean clustering to conduct samples transformation, so as to obtain new high-level deep samples. Finally, the bagging ensemble learning mode is adopted to fuse the deep feature learning algorithm and the deep samples learning algorithm together, thereby constructing a deep double-side learning ensemble model. At the end of this paper, two representative speech datasets of Parkinson's disease were used for verification. The experimental results show that the proposed algorithm are effective.

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preprint

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en

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http://arxiv.org/abs/2006.11593

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27

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https://doi.org/10.48550/arxiv.2006.11593

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Title

Deep Double-Side Learning Ensemble Model for Few-Shot Parkinson Speech Recognition

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preprint

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en

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2020

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2020-06-20

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Yongming Li, Lang Zhou, Lingyun Qin, Yuwei Zeng, Yuchuan Liu, Yan Lei, Pin Wang, Fan Li

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https://arxiv.org/abs/2006.11593

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https://arxiv.org/pdf/2006.11593

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Artificial intelligence, Computer science, Deep learning, Feature extraction, Pattern recognition (psychology), Ensemble learning, Feature learning, Feature (linguistics), Cluster analysis, Feature selection, Machine learning, Speech recognition, Philosophy, Linguistics

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Cited by

2

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27

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10

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