  In this work we explore how language models can be employed to analyze
language and discriminate between mentally impaired and healthy subjects
through the perplexity metric. Perplexity was originally conceived as an
information-theoretic measure to assess how much a given language model is
suited to predict a text sequence or, equivalently, how much a word sequence
fits into a specific language model. We carried out an extensive
experimentation with the publicly available data, and employed language models
as diverse as N-grams, from 2-grams to 5-grams, and GPT-2, a transformer-based
language model. We investigated whether perplexity scores may be used to
discriminate between the transcripts of healthy subjects and subjects suffering
from Alzheimer Disease (AD). Our best performing models achieved full accuracy
and F-score (1.00 in both precision/specificity and recall/sensitivity) in
categorizing subjects from both the AD class and control subjects. These
results suggest that perplexity can be a valuable analytical metrics with
potential application to supporting early diagnosis of symptoms of mental
disorders.
