nips nips2000 nips2000-123 nips2000-123-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Hagai Attias, John C. Platt, Alex Acero, Li Deng
Abstract: 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.