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In this paper, we introduce a new formulation of the REMOS (REverberation MOdeling for Speech recognition) concept from an uncertainty decoding perspective. Based on a convolutive observation model that relaxes the conditional independence assumption of hidden Markov models, REMOS effectively adapts automatic speech recognition (ASR) systems to noisy and strongly reverberant environments. While uncertainty...
The generic REMOS (REverberation MOdeling for robust Speech recognition) concept is extended in this contribution to cope with additional noise components. REMOS originally embeds an explicit reverberation model into a hiddenMarkov model (HMM) leading to a relaxed conditional independence assumption for the observed feature vectors. During recognition, a nonlinear optimization problem is to be solved...
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