论文标题
多级与一级分类器在自发语音分析中以阿尔茨海默氏病诊断为导向
Multi-class versus One-class classifier in spontaneous speech analysis oriented to Alzheimer Disease diagnosis
论文作者
论文摘要
大多数医疗发展都需要能够识别相对于目标组或对照组异常的样本，从某种意义上说，它们可能属于新的，以前看不见的类或不是类数据。在这种情况下，当没有足够的数据来训练两级单级分类时，看起来像是可用的解决方案。另一方面，非线性方法可以提供非常有用的信息。我们项目的目的是通过使用从语音信号中提取的新生物标志物进行的自动分析来为AD的早期诊断和更好的严重性估计做出贡献。在这种情况下选择的方法是针对自发语音和情感反应分析的语音生物标志物。在这种方法中，分析了单级分类器和两级分类器。有关离群值和分形尺寸特征的信息的使用可改善系统性能。
Most of medical developments require the ability to identify samples that are anomalous with respect to a target group or control group, in the sense they could belong to a new, previously unseen class or are not class data. In this case when there are not enough data to train two-class One-class classification appear like an available solution. On the other hand non-linear approaches could give very useful information. The aim of our project is to contribute to earlier diagnosis of AD and better estimates of its severity by using automatic analysis performed through new biomarkers extracted from speech signal. The methods selected in this case are speech biomarkers oriented to Spontaneous Speech and Emotional Response Analysis. In this approach One-class classifiers and two-class classifiers are analyzed. The use of information about outlier and Fractal Dimension features improves the system performance.
