Spoken Language Biomarkers for Detecting Cognitive Impairment
  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.
