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In this paper, we combined linguistic complexity and (dis)fluency featureswith pretrained language models for the task of Alzheimer's disease detectionof the 2021 ADReSSo (Alzheimer's Dementia Recognition through SpontaneousSpeech) challenge. An accuracy of 83.1% was achieved on the test set, whichamounts to an improvement of 4.23% over the baseline model. Our best-performingmodel that integrated component models using a stacking ensemble techniqueperformed equally well on cross-validation and test data, indicating that it isrobust against overfitting.

Alzheimer's Disease Detection from Spontaneous Speech through Combining  Linguistic Complexity and (Dis)Fluency Features with Pretrained Language  Models