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A MAP APPROACH TO NOISE COMPENSATION OF SPEECH

By  and Philip N. Garner and Philip N. Garner

Abstract

We show that estimation of parameters for the popular Gaussian model of speech in noise can be regularised in a Bayesian sense by use of simple prior distributions. For two example prior distributions, we show that the marginal distribution of the uncorrupted speech is non-Gaussian, but the parameter estimates themselves have tractable solutions. Speech recognition experiments serve to suggest values for hyper-parameters, and demonstrate that the theory is practically applicable

Topics: Speech processing, noise
Year: 2009
OAI identifier: oai:CiteSeerX.psu:10.1.1.170.3440
Provided by: CiteSeerX
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