Calibration of probabilistic age recognition

The task in automatic age recognition in speech technology typically is one of regression, i.e., predicting the age of a speaker from his/her speech. In this paper we are interested in the probabilistic interpretation of the posterior distribution of the predicted age. We review a number of measures for assessing the probabilistic properties of the posterior distribution, and link these to detection theory, which is very well understood from the automatic speaker recognition literature. We show that the Gaussian posterior distributions predicted by least square support vector regression behave well, and that there is only a small room for improvement of their posterior distributions under the Gaussian assumption.