Vector taylor series expansion with auditory masking for noise robust speech recognition

Abstract

In this paper, we address the problem of speech recognition in the presence of additive noise. We investigate the applicability and efficacy of auditory masking in devising a robust front end for noisy features. This is achieved by introducing a masking factor into the Vector Taylor Series (VTS) equations. The resultant first order VTS approximation is used to compensate the parameters of a clean speech model and a Minimum Mean Square Error (MMSE) estimate is used to estimate the clean speech features. The proposed algorithms are validated through experiments on a noise corrupted TIMIT speech recognition database. We show significant performance gain for the proposed method as compared to the traditional VTS algorithm.