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Parkinson disease (PD)'s speech recognition is an effective way for itsdiagnosis, which has become a hot and difficult research area in recent years.As we know, there are large corpuses (segments) within one subject. However,too large segments will increase the complexity of the classification model.Besides, the clinicians interested in finding diagnostic speech markers thatreflect the pathology of the whole subject. Since the optimal relevant featuresof each speech sample segment are different, it is difficult to find theuniform diagnostic speech markers. Therefore, it is necessary to reconstructthe existing large segments within one subject into few segments even onesegment within one subject, which can facilitate the extraction of relevantspeech features to characterize diagnostic markers for the whole subject. Toaddress this problem, an enveloped deep speech sample learning algorithm forParkinson's subjects based on multilayer fuzzy c-mean (MlFCM) clustering andinterlayer consistency preservation is proposed in this paper. The algorithmcan be used to achieve intra-subject sample reconstruction for Parkinson'sdisease (PD) to obtain a small number of high-quality prototype samplesegments. At the end of the paper, several representative PD speech datasetsare selected and compared with the state-of-the-art related methods,respectively. The experimental results show that the proposed algorithm iseffective signifcantly.

Subject Enveloped Deep Sample Fuzzy Ensemble Learning Algorithm of Parkinson's Speech Data