  We present our submission to the ICASSP-SPGC-2023 ADReSS-M Challenge Task,
which aims to investigate which acoustic features can be generalized and
transferred across languages for Alzheimer's Disease (AD) prediction. The
challenge consists of two tasks: one is to classify the speech of AD patients
and healthy individuals, and the other is to infer Mini Mental State
Examination (MMSE) score based on speech only. The difficulty is mainly
embodied in the mismatch of the dataset, in which the training set is in
English while the test set is in Greek. We extract paralinguistic features
using openSmile toolkit and acoustic features using XLSR-53. In addition, we
extract linguistic features after transcribing the speech into text. These
features are used as indicators for AD detection in our method. Our method
achieves an accuracy of 69.6% on the classification task and a root mean
squared error (RMSE) of 4.788 on the regression task. The results show that our
proposed method is expected to achieve automatic multilingual Alzheimer's
Disease detection through spontaneous speech.
