  Speech-based automatic detection of Alzheimer's disease (AD) and depression
has attracted increased attention. Confidence estimation is crucial for a
trust-worthy automatic diagnostic system which informs the clinician about the
confidence of model predictions and helps reduce the risk of misdiagnosis. This
paper investigates confidence estimation for automatic detection of AD and
depression based on clinical interviews. A novel Bayesian approach is proposed
which uses a dynamic Dirichlet prior distribution to model the second-order
probability of the predictive distribution. Experimental results on the
publicly available ADReSS and DAIC-WOZ datasets demonstrate that the proposed
method outperforms a range of baselines for both classification accuracy and
confidence estimation.
