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

Cross-Lingual Transfer Learning for Alzheimer's Detection From Spontaneous Speech