  The risk of Parkinson's disease (PD) is extremely serious, and PD speech
recognition is an effective method of diagnosis nowadays. However, due to the
influence of the disease stage, corpus, and other factors on data collection,
the ability of every samples within one subject to reflect the status of PD
vary. No samples are useless totally, and not samples are 100% perfect. This
characteristic means that it is not suitable just to remove some samples or
keep some samples. It is necessary to consider the sample transformation for
obtaining high quality new samples. Unfortunately, existing PD speech
recognition methods focus mainly on feature learning and classifier design
rather than sample learning, and few methods consider the sample
transformation. To solve the problem above, a PD speech sample transformation
algorithm based on multitype reconstruction operators is proposed in this
paper. The algorithm is divided into four major steps. Three types of
reconstruction operators are designed in the algorithm: types A, B and C.
Concerning the type A operator, the original dataset is directly reconstructed
by designing a linear transformation to obtain the first dataset. The type B
operator is designed for clustering and linear transformation of the dataset to
obtain the second new dataset. The third operator, namely, the type C operator,
reconstructs the dataset by clustering and convolution to obtain the third
dataset. Finally, the base classifier is trained based on the three new
datasets, and then the classification results are fused by decision weighting.
In the experimental section, two representative PD speech datasets are used for
verification. The results show that the proposed algorithm is effective.
Compared with other algorithms, the proposed algorithm achieves apparent
improvements in terms of classification accuracy.
