MEASURING UNCERTAINTY IN DEEP REGRESSION MODELS: THE CASE OF AGE ESTIMATION FROM SPEECH

Age estimation from speech recently received a lot of attention. Approaches such as i-vectors and deep learning have been successfully applied to this task achieving great performance. However, one drawback of those methods is that they produce a hard age estimation without any kind of confidence measure about the quality of the prediction. Designing systems with the ability to provide a confidence measure about their output is extremely valuable for several applications where the cost of making bad decisions is worse than making no decision, e.g., forensics. In this paper, we propose a novel framework to jointly predict the age and its estimation uncertainty in a context of neural regression model. This model is trained using probabilistic fashion instead of using the classical minimum mean square error objective used for regression tasks. The probabilistic output corresponds to a Gaussian posterior. The proposed neural network will estimate both the posterior mean which corresponds to the predicted age and the variance which quantifies the uncertainty of the prediction. We evaluated our approach on two different datasets NIST SRE 2008 - 2010 and Switchboard.