An Unsupervised Two-Step Convolution Sparse Transfer Learning Algorithm for Parkinson's Disease Speech Diagnosis

Parkinson's disease(PD) speech diagnosis has a small sample problem. Although it is possible to transfer learning with the help of relevant speech datasets. The introduction of other samples will lead to the distribution difference between samples of different subjects, so the classification accuracy is greatly affected. Therefore, in this paper, to solve the problems above, we propose a novel unsupervised two-step convolutional sparse transfer leaning algorithm. The algorithm is divided into two steps: fast convolutional sparse coding with coordinate selection of samples and features(FCSC&SF), joint local structure distribution alignment(JLSDA). In the FCSC&SF, speech structure among public speech dataset is quickly learned by fast convolution sparse coding(FCSC), and transferred into the target dataset, after that, the more valuable information is obtained by coordinate selection of samples and features. JLSDA is designed to maintain the local structure information in the two domains, and reduce the distribution difference between the two domains at the same time. The experimental results showed that each step of the proposed algorithm has a positive effect on the classification results; compared with the representative relevant algorithms, the accuracy of the proposed method is significantly higher at 97.5%. © 2022, Chinese Institute of Electronics. All right reserved.