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NIPS
2000

Speech Denoising and Dereverberation Using Probabilistic Models

13 years 5 months ago
Speech Denoising and Dereverberation Using Probabilistic Models
This paper presents a unified probabilistic framework for denoising and dereverberation of speech signals. The framework transforms the denoising and dereverberation problems into Bayes-optimal signal estimation. The key idea is to use a strong speech model that is pre-trained on a large data set of clean speech. Computational efficiency is achieved by using variational EM, working in the frequency domain, and employing conjugate priors. The framework covers both single and multiple microphones. We apply this approach to noisy reverberant speech signals and get results substantially better than standard methods.
Hagai Attias, John C. Platt, Alex Acero, Li Deng
Added 01 Nov 2010
Updated 01 Nov 2010
Type Conference
Year 2000
Where NIPS
Authors Hagai Attias, John C. Platt, Alex Acero, Li Deng
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