  In this study we developed an automated system that evaluates speech and
language features from audio recordings of neuropsychological examinations of
92 subjects in the Framingham Heart Study. A total of 265 features were used in
an elastic-net regularized binomial logistic regression model to classify the
presence of cognitive impairment, and to select the most predictive features.
We compared performance with a demographic model from 6,258 subjects in the
greater study cohort (0.79 AUC), and found that a system that incorporated both
audio and text features performed the best (0.92 AUC), with a True Positive
Rate of 29% (at 0% False Positive Rate) and a good model fit (Hosmer-Lemeshow
test > 0.05). We also found that decreasing pitch and jitter, shorter segments
of speech, and responses phrased as questions were positively associated with
cognitive impairment.
