Alzheimer's disease (AD) is a progressive neurodegenerative disease most often associated with memory deficits and cognitive decline. With the aging population, there has been much interest in automated methods for cognitive impairment detection. One approach that has attracted attention in recent years is AD detection through spontaneous speech. While the results are promising, it is not certain whether the learned speech features can be generalized across languages. To fill this gap, the ADReSS-M challenge was organized. This paper presents our submission to this ICASSP-2023 Signal Processing Grand Challenge (SPGC). The model was trained on 228 English samples of a picture description task and was transferred to Greek using only 8 samples. We obtained an accuracy of 82.6% for AD detection, a root-mean-square error of 4.345 for cognitive score prediction, and ranked 2nd place in the competition out of 24 competitors.

翻译：阿尔茨海默病（AD）是一种进行性神经退行性疾病，通常与记忆缺陷和认知能力下降相关。随着人口老龄化，人们对认知障碍检测的自动化方法产生了浓厚兴趣。近年来，通过自发性语音进行阿尔茨海默病检测的方法备受关注。尽管结果令人鼓舞，但所学语音特征能否跨语言泛化仍不确定。为填补这一空白，ADReSS-M挑战赛应运而生。本文介绍我们在ICASSP-2023信号处理大挑战赛（SPGC）中的参赛方案。该模型基于228个英语图片描述任务样本进行训练，并仅利用8个样本迁移至希腊语。我们在阿尔茨海默病检测中获得了82.6%的准确率，在认知评分预测中均方根误差为4.345，并在24个参赛队伍中排名第2位。