论文标题
一种新颖的决策树，用于抑郁症的识别
A Novel Decision Tree for Depression Recognition in Speech
论文作者
论文摘要
抑郁症是全球常见的精神障碍，会导致一系列严重的结果。抑郁症的诊断依赖于患者报告的量表和精神病医生访谈，这可能导致主观偏见。近年来，越来越多的研究人员致力于言语中的抑郁症识别，这可能是一个有效且客观的指标。这项研究提出了一种基于决策树的新语音段融合方法，以提高抑郁识别的准确性，并对52名受试者的样本（23名抑郁症患者和29名健康对照组）进行验证。男性和女性在性别依赖模型上的识别精度分别为75.8％和68.5％。可以从数据得出结论，建议的决策树模型可以改善抑郁症分类的性能。
Depression is a common mental disorder worldwide which causes a range of serious outcomes. The diagnosis of depression relies on patient-reported scales and psychiatrist interview which may lead to subjective bias. In recent years, more and more researchers are devoted to depression recognition in speech , which may be an effective and objective indicator. This study proposes a new speech segment fusion method based on decision tree to improve the depression recognition accuracy and conducts a validation on a sample of 52 subjects (23 depressed patients and 29 healthy controls). The recognition accuracy are 75.8% and 68.5% for male and female respectively on gender-dependent models. It can be concluded from the data that the proposed decision tree model can improve the depression classification performance.
