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

Cross-lingual Alzheimer's Disease detection based on paralinguistic and pre-trained features