Normalization and Adaptation by Consistently Employing Map Estimation


The purpose of this paper is to develop a feature and model space adaptation method that improves speech recognition performance regardless of the amount of adaptation data. We propose a maximum a posteriori (MAP)-based Bayesian estimation approach that is consistently applied to normalization of feature spaces and adaptation of model parameters (i.e… (More)

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