Deterioration in the quality of a person’s voice and speech is an early sign of Parkinson’s disease (PD). Although a number of computer-based methods have been invested to use patients’ speech for early diagnosis of Parkinson’s disease, they only focus on a fixed pronunciation test, such as the subjects’ monosyllabic pronunciation is analyzed to determine whether they have potential possibility of PD. Moreover, only using traditional speech analysis methods to extract single-view speech features cannot provide a comprehensive feature representation. This paper is dedicated to the study of various pronunciation tests for patients with PD, including the pronunciation of five monosyllabic vowels and a spontaneous dialogue. A triplet multimodel transfer learning network is designed and proposed for identifying subjects with PD in these two groups of tests. First, multisource data extract mel frequency cepstrum coefficient (MFCC) features of speech for preprocessing. Subsequently, a pretrained triplet model represents features from three dimensions as the upstream task of the transfer learning framework. Finally, the pretrained model is reconstructed as a novel model that integrates the triplet model, temporal model, and auxiliary layer as the downstream task, and weights are updated through fine-tuning to identify abnormal speech. Experimental results show that the highest PD detection rates in the two groups of tests are 99% and 90% , respectively, which outperform a large number of internationally popular pattern recognition algorithms and serve as a baseline for other academic researchers in this field.

Currently, hand motion recognition of single‐modality data has been extensively explored for the analysis of various contact and noncontact sensors, and it is recognized that all the existing technologies have both strengths and limitations. As a significant motor symptom, hand tremor is usually utilized for the diagnosis and evaluation of Parkinson’s disease; furthermore, a multimodal analysis of the handwriting pattern of the patient has made up for the one‐sided way of learning the hand movement in a single measurement dimension. Especially, considering a variety of measurement resources, it shows promising performance in recognizing handwriting patterns of Parkinson’s disease. In this work, a novel Spatio‐temporal Siamese neural network (ST‐SiamNN) is proposed to learn the handwriting differences between healthy individuals and patients with Parkinson’s disease, process data onto multiple sensors, and enhance the characteristics of handwriting in Parkinson’s disease. Uniquely, it is a discriminative model of multilabel and multinetwork constructed by a Siamese network, which consists of four modules: a preprocessor for handwritten data enhancement, a Siamese bidirectional memory neural network (SiamBiMNN) for temporal and texture feature extraction and difference enhancement, a Siamese octave convolutional neural network (SiamOctCNN) for spatial feature extraction and difference enhancement, and a decision‐making layer to rejudge the output features of the Siamese networks to obtain more accurate auxiliary diagnosis results. The framework proposed in this article is verified on two handwritten datasets of multiple modalities, i.e., images, smart pen signals, and graphics tablet signals, which are compared with several state‐of‐the‐art studies.