ID 原文 译文
20025 仿真结果验证了该算法的有效性。 Simulation results verify the effectiveness of the proposed method.
20026 基于语音数据分析的帕金森病(PD)诊断存在样本量小、训练与测试数据分布差异明显的问题。为了解决这些问题，需要从降维和样本扩充两个方面同时进行。 To solve the problems that there are few labeled data in speech data for diagnosis of Parkinson’s Disease (PD), and the distributed condition of the training and the test data is different, the two aspects of dimension reduction and sample augment are considered.
20027 因此，该文提出结合加噪加权卷积稀疏迁移学习和样本特征并行优选的PD分类算法。 A novel transfer learning algorithm is proposed based on noise weighting sparse coding combined with speech sample / feature parallel selection.
20028 该算法可从源域的公共语音库中学习有利于表达PD语音特征的有效结构信息，同时完成降维和样本间接扩充。 The algorithm can learn the structural information from the source domain and express the effective PD features, and achieves dimension reduction and sample augment simultaneously.
20029 样本特征并行优选考虑到了样本和语音特征间的关系，从而有助于获取高质量的特征。 Considering the relationship between the samples and features, the higher quality features can be extracted.
20030 首先，对公共语音库进行特征提取构造公共特征库； Firstly, the features are extracted from the public dataset and the feature data set is constructed as source domain.
20031 然后，以公共特征库对PD目标域的训练数据集及测试数据集进行稀疏编码，这里分别采用传统稀疏编码(SC)与卷积稀疏编码(CSC)两种稀疏编码方法； Then the training data and test data of the targetdomain are sparsely represented based on source domain. Spares representing includs traditional SparseCoding(SC) and Convolutional Sparse Coding(CSC);
20032 接着，对编码后的语音样本段和特征数据进行同时优选； Next, the sparse representing data are screened accordingto sample feature selection simultaneously, so as to improve the accuracy of the PD classification;
20033 最后，采用支撑向量机(SVM)进行分类。 Finally, theSupport Vector Machine(SVM) classifier is adopted.
20034 实验结果表明，该算法针对受试者的分类准确率最高值达到了95.0%，均值达到了86.0%，较相关被比较算法有较大提高。 Experiments show that it achieves the highest classification accuracy of 95.0% and the average classification accuracy of 86.0%, and obtains obvious improvement according to the subjects, compared with the relevant algorithms.