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.
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