Formulation of the REMOS concept from an uncertainty decoding perspective

Abstract

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 decoding approaches are typically designed to operate irrespectively of the employed decoding routine of the ASR system, REMOS explicitly considers the additional information provided by the Viterbi decoder. In contrast to previous publications of the REMOS concept, we provide a conclusive derivation of its decoding routine using a Bayesian network representation in order to prove its inherent uncertainty decoding character.