Prediction of cognitive performance in an animal fluency task based on rate and articulatory markers

Neurophysiological changes in the brain associated with early dementia can disrupt articulatory timing and precision in speech production. Motivated by this observation, we address the hypothesis that speaking rate and articulatory coordination, as manifested through formant frequency tracks, can predict performance on an animal fluency task administerd to the elderly. Specifically, using phoneme-based measures of speaking rate and articulatory coordination derived from formant cross-correlation measures, we investigate the capability of speech features, estimated from paragraph-recall and naturalistic free speech, to predict animal fluency assessment scores. Using a database consisting of audio from elderly subjects over a 4 year period, we develop least-squares regression models of our cognitive performance measures. The best performing model combined speaking rate and formant features, resulting in a correlation (R) of 0.61 and a root mean squared error (RMSE) of 5.07 with respect to a 934 score range. Vocal features thus provide a reduction by about 30% in MSE from a baseline (mean score) in predicting cognitive performance derived from the animal fluency assessment.