  Robust strategies for Alzheimer's disease (AD) detection are important, given
the high prevalence of AD. In this paper, we study the performance and
generalizability of three approaches for AD detection from speech on the recent
ADReSSo challenge dataset: 1) using conventional acoustic features 2) using
novel pre-trained acoustic embeddings 3) combining acoustic features and
embeddings. We find that while feature-based approaches have a higher
precision, classification approaches relying on pre-trained embeddings prove to
have a higher, and more balanced cross-validated performance across multiple
metrics of performance. Further, embedding-only approaches are more
generalizable. Our best model outperforms the acoustic baseline in the
challenge by 2.8%.
