  Research related to automatically detecting Alzheimer's disease (AD) is
important, given the high prevalence of AD and the high cost of traditional
methods. Since AD significantly affects the content and acoustics of
spontaneous speech, natural language processing and machine learning provide
promising techniques for reliably detecting AD. We compare and contrast the
performance of two such approaches for AD detection on the recent ADReSS
challenge dataset: 1) using domain knowledge-based hand-crafted features that
capture linguistic and acoustic phenomena, and 2) fine-tuning Bidirectional
Encoder Representations from Transformer (BERT)-based sequence classification
models. We also compare multiple feature-based regression models for a
neuropsychological score task in the challenge. We observe that fine-tuned BERT
models, given the relative importance of linguistics in cognitive impairment
detection, outperform feature-based approaches on the AD detection task.
