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.

翻译：随着全球人口快速老龄化，阿尔茨海默病（Alzheimer's disease, AD）在老年人群中尤为突出，其起病隐匿，导致认知领域（如记忆、沟通等）出现渐进性且不可逆的衰退。基于语音的AD检测为大规模筛查和及时疾病干预提供了可能。预训练模型的最新进展促使AD检测建模从低层特征向高层表征转变。本文提出了几种高效方法，用于从高层声学特征和语言特征中提取更有效的AD相关线索。基于这些特征，本文还通过建模受试者描述与认知任务之间的关系，提出了一种新颖的任务导向方法。实验在ADReSS数据集上以二分类设置进行，并在未见测试集上评估模型性能。结果及与近期文献的对比表明，所提出的声学、语言和任务导向方法具有高效性和优越性能。研究结果还揭示了语义与句法信息的重要性，以及基于有前景的纯音频和任务导向方法实现AD检测任务自动化与泛化的可行性。