  In this work, we aim to establish a Bayesian adaptive learning framework by
focusing on estimating latent variables in deep neural network (DNN) models.
Latent variables indeed encode both transferable distributional information and
structural relationships. Thus the distributions of the source latent variables
(prior) can be combined with the knowledge learned from the target data
(likelihood) to yield the distributions of the target latent variables
(posterior) with the goal of addressing acoustic mismatches between training
and testing conditions. The prior knowledge transfer is accomplished through
Variational Bayes (VB). In addition, we also investigate Maximum a Posteriori
(MAP) based Bayesian adaptation. Experimental results on device adaptation in
acoustic scene classification show that our proposed approaches can obtain good
improvements on target devices, and consistently outperforms other cut-edging
algorithms.
