Quantifying prediction uncertainties in automatic speaker verification systems

For modern automatic speaker verification (ASV) systems, explicitly quantifying the confidence for each prediction strengthens the system’s reliability by indicating in which case the system is with trust. However, current paradigms do not take this into consideration. We thus propose to express confidence in the prediction by quantifying the uncertainty in ASV predictions. This is achieved by developing a novel Bayesian framework to obtain a score distribution for each input. The mean of the distribution is used to derive the decision while the spread of the distribution represents the uncertainty arising from the plausible choices of the model parameters. To capture the plausible choices, we sample the probabilistic linear discriminant analysis (PLDA) back-end model posterior through Hamiltonian Monte-Carlo (HMC) and approximate the embedding model posterior through stochastic Langevin dynamics (SGLD) and Bayes-by-backprop. Given the resulting score distribution, a further quantification and decomposition of the prediction uncertainty are achieved by calculating the score variance, entropy, and mutual information. The quantified uncertainties include the aleatoric uncertainty and epistemic uncertainty (model uncertainty). We evaluate them by observing how they change while varying the amount of training speech, the duration, and the noise level of testing speech. The experiments indicate that the behaviour of those quantified uncertainties reflects the changes we made to the training and testing data, demonstrating the validity of the proposed method as a measure of uncertainty. • The paper emphasises the need for quantifying and separating uncertainties in ASV. • The paper proposes a novel framework incorporating various Bayesian learning methods. • The major cause of epistemic uncertainty is training data size and test data length. • The noise level in the test utterance increases the aleatoric uncertainty in ASV.