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：阿尔茨海默氏病(AD)是一种进步性神经退化性疾病,通常与记忆力不足和认知能力下降有关。随着人口老化,人们对认知障碍检测的自动化方法非常感兴趣。近年来,一种引起注意的方法是通过自发的言语检测AD。虽然结果很有希望,但还不能确定学习的言语特征能否在各种语言之间普及。为了填补这一空白,组织了ADRESS-M挑战。本文件介绍了我们提交ICASSP-2023信号处理大挑战(SPGC)的呈件。该模型只用8个样本对228个图片描述任务的英语样本进行了培训,并被转移到希腊。我们获得了82.6%的精确度用于自动检测,这是用于认知分数预测的4.345根位差,在24个竞争对手中排名第二。</s>