  In this paper, we combined linguistic complexity and (dis)fluency features
with pretrained language models for the task of Alzheimer's disease detection
of the 2021 ADReSSo (Alzheimer's Dementia Recognition through Spontaneous
Speech) challenge. An accuracy of 83.1% was achieved on the test set, which
amounts to an improvement of 4.23% over the baseline model. Our best-performing
model that integrated component models using a stacking ensemble technique
performed equally well on cross-validation and test data, indicating that it is
robust against overfitting.
