利用带有任务相关关键词的预训练表示进行阿尔茨海默病检测
Leveraging Pretrained Representations with Task-related Keywords for Alzheimer's Disease Detection
摘要：
随着全球人口的迅速老龄化，阿尔茨海默病（AD）在老年人中尤为突出，其具有潜伏性发作并导致认知领域（记忆、交流等）的逐渐、不可逆的恶化。基于语音的AD检测开辟了广泛筛查和及时疾病干预的可能性。预训练模型的最新进展促使AD检测建模从低级特征转向高级表示。本文提出了几种有效的方法，以提取更好的广告相关线索，从高层次的声学和语言特征。基于这些特点，本文还提出了一种新的面向任务的方法，通过建模之间的关系参与者的描述和认知任务。在ADReSS数据集上进行了二进制分类设置的实验，并在看不见的测试集上对模型进行评估。结果和最近的文献比较表明，建议的声学，语言和面向任务的方法的效率和优越的性能。研究结果还表明，语义和句法信息的重要性，以及自动化和推广的可行性与有前途的音频和面向任务的方法的AD检测任务。
英文摘要：
With the global population aging rapidly, Alzheimer's disease (AD) is particularly prominent in older adults, which has an insidious onset and leads to a gradual, irreversible deterioration in cognitive domains (memory, communication, etc.). Speech-based AD detection opens up the possibility of widespread screening and timely disease intervention. Recent advances in pre-trained models motivate AD detection modeling to shift from low-level features to high-level representations. This paper presents several efficient methods to extract better AD-related cues from high-level acoustic and linguistic features. Based on these features, the paper also proposes a novel task-oriented approach by modeling the relationship between the participants' description and the cognitive task. Experiments are carried out on the ADReSS dataset in a binary classification setup, and models are evaluated on the unseen test set. Results and comparison with recent literature demonstrate the efficiency and superior performance of proposed acoustic, linguistic and task-oriented methods. The findings also show the importance of semantic and syntactic information, and feasibility of automation and generalization with the promising audio-only and task-oriented methods for the AD detection task.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
