  We present an approach to automatic detection of Alzheimer's type dementia
based on characteristics of spontaneous spoken language dialogue consisting of
interviews recorded in natural settings. The proposed method employs additive
logistic regression (a machine learning boosting method) on content-free
features extracted from dialogical interaction to build a predictive model. The
model training data consisted of 21 dialogues between patients with Alzheimer's
and interviewers, and 17 dialogues between patients with other health
conditions and interviewers. Features analysed included speech rate,
turn-taking patterns and other speech parameters. Despite relying solely on
content-free features, our method obtains overall accuracy of 86.5\%, a result
comparable to those of state-of-the-art methods that employ more complex
lexical, syntactic and semantic features. While further investigation is
needed, the fact that we were able to obtain promising results using only
features that can be easily extracted from spontaneous dialogues suggests the
possibility of designing non-invasive and low-cost mental health monitoring
tools for use at scale.
