论文标题
在阿尔茨海默氏症中建模神经学任务评分的声学特征
Acoustic-Linguistic Features for Modeling Neurological Task Score in Alzheimer's
论文作者
论文摘要
由于医疗技术的进步，预防性医疗保健以及对老年医学健康的越来越重视，全球平均预期寿命正在增加。因此，必须开发检测和跟踪老年人种群认知功能中与衰老相关的疾病的技术。特别是，鉴于该疾病的患病率和当前方法的成本，与自动检测和评估有关阿尔茨海默氏病（AD）的研究至关重要。由于广告影响了语音和词汇的声学，因此自然语言处理和机器学习为可靠地检测AD提供了有希望的技术。我们比较和对比十个线性回归模型的性能，以预测Adress挑战数据集中的微型心理状态考试得分。我们提取了13000多种手工制作和学习的功能，可捕捉语言和声学现象。使用通过两种方法选择的54个顶部特征的子集：（1）递归消除和（2）相关得分，我们的表现要优于同一任务的最新基线。在评分和评估每个模型选定特征子集的统计显着性时，我们发现，对于给定的任务，手工制作的语言特征比声学和学识渊博的特征更为重要。
The average life expectancy is increasing globally due to advancements in medical technology, preventive health care, and a growing emphasis on gerontological health. Therefore, developing technologies that detect and track aging-associated disease in cognitive function among older adult populations is imperative. In particular, research related to automatic detection and evaluation of Alzheimer's disease (AD) is critical given the disease's prevalence and the cost of current methods. As AD impacts the acoustics of speech and vocabulary, natural language processing and machine learning provide promising techniques for reliably detecting AD. We compare and contrast the performance of ten linear regression models for predicting Mini-Mental Status Exam scores on the ADReSS challenge dataset. We extracted 13000+ handcrafted and learned features that capture linguistic and acoustic phenomena. Using a subset of 54 top features selected by two methods: (1) recursive elimination and (2) correlation scores, we outperform a state-of-the-art baseline for the same task. Upon scoring and evaluating the statistical significance of each of the selected subset of features for each model, we find that, for the given task, handcrafted linguistic features are more significant than acoustic and learned features.
