【摘要】 帕金森病(PD)语音识别算法研究对于其及时诊疗具有重要意义，但现有PD语音识别算法面临小样本数据量问题挑战。针对问题，本文提出双面双阶段均值聚类包络和卷积稀疏迁移学习算法。在双阶段学习方面，首先基于源数据集训练多组卷积核，然后通过中间集得到最优卷积核并对目标集进行编码。在深度样本聚类包络方面，首先设计迭代均值聚类算法构建深度样本空间；然后进行样本特征同时选择并训练分类器模型；最后对不同样本空间的分类结果进行融合。实验选取代表性的PD语音数据集进行验证。实验结果表明，本文算法创新部分有效，与10多个经典和最新相关文献算法相比取得了显著改进，准确率达97.8%。此外，本文算法的时间复杂度不高，满足临床应用要求。更多还原

【Abstract】 The research on the Parkinson′s disease(PD) speech recognition algorithm is important for timely diagnosis and treatment. However, the existing public PD speech datasets are characterized by small sample sizes, which is one of the main challenges faced by existing PD speech recognition methods. To address this issue, a novel dual-side two-stage means clustering envelope and convolution sparse transfer learning model is proposed.First, for the dataset side, multiple groups ofconvolution kernels are trained, which is based on the source domain dataset. Then, the optimal convolution kernels are filtered by the encoded intermediate dataset.Finally, the target domain dataset is encoded by the optimalkernels. In regard to deep instances clustering envelope, an iterative mean clustering algorithm is designed to construct the deep instance space. Secondly, various classifiers are developed after sample/feature parallel selection. Finally, the classification results of different instance layers are fused. In the experiment, the representative PD speech datasets are selected for verification. Experimental results show that the main innovative parts of the proposed algorithm are effective.Compared with more than ten classical algorithms, the obvious improvements in terms of classification accuracy are achieved 97.8%. In addition, the proposed algorithmhas potential in clinicalapplications for acceptable time complexity.更多还原