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In this work, we aim to establish a Bayesian adaptive learning framework byfocusing on estimating latent variables in deep neural network (DNN) models.Latent variables indeed encode both transferable distributional information andstructural 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 trainingand testing conditions. The prior knowledge transfer is accomplished throughVariational Bayes (VB). In addition, we also investigate Maximum a Posteriori(MAP) based Bayesian adaptation. Experimental results on device adaptation inacoustic scene classification show that our proposed approaches can obtain goodimprovements on target devices, and consistently outperforms other cut-edgingalgorithms.

Bayesian adaptive learning to latent variables via Variational Bayes and Maximum a Posteriori