A MAP Approach to Noise Compensation of Speech
| Type of publication: | Idiap-RR |
| Citation: | Garner_Idiap-RR-08-2009 |
| Number: | Idiap-RR-08-2009 |
| Year: | 2009 |
| Month: | 6 |
| Institution: | Idiap |
| 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. |
| Keywords: | |
| Projects: |
IM2 |
| Authors: | |
| Added by: | [ADM] |
| Total mark: | 0 |
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