Unsupervised training scheme with non-stereo data for empirical feature vector compensation

Abstract

In this paper, a novel training scheme based on unsupervised and non-stereo data is presented for Multi-Environment Modelbased LInear Normalization (MEMLIN) and MEMLIN with cross-probability model based on GMMs (MEMLIN-CPM). Both are data-driven feature vector normalization techniques which have been proved very effective in dynamic noisy acoustic… (More)

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