Alzheimer's disease is a common cognitive disorder in the elderly. Early and accurate diagnosis of Alzheimer's disease (AD) has a major impact on the progress of research on dementia. At present, researchers have used machine learning methods to detect Alzheimer's disease from the speech of participants. However, the recognition accuracy of current methods is unsatisfactory, and most of them focus on using low-dimensional handcrafted features to extract relevant information from audios. This paper proposes an Alzheimer's disease detection system based on the pre-trained framework Wav2vec 2.0 (Wav2vec2). In addition, by replacing the loss function with the Soft-Weighted CrossEntropy loss function, we achieved 85.45\% recognition accuracy on the same test dataset.

翻译：阿尔茨海默病是老年人中常见的认知障碍。对阿尔茨海默病（AD）的早期准确诊断对痴呆症研究的进展具有重要影响。目前，研究者已采用机器学习方法从参与者语音中检测阿尔茨海默病。然而，现有方法的识别准确率尚不理想，且多数方法侧重于使用低维手工特征从音频中提取相关信息。本文提出一种基于预训练框架Wav2vec 2.0（Wav2vec2）的阿尔茨海默病检测系统。此外，通过将损失函数替换为软加权交叉熵损失函数，我们同一测试数据集上实现了85.45%的识别准确率。