Abstract

In this paper we address the problem of robustness of speech
recognition systems in noisy environments. The goal is to
estimate the parameters of a HMM that is matched to a noisy
environment, given a HMM trained with clean speech and
knowledge of the acoustical environment. We propose a
method based on truncated vector Taylor series that
approximates the performance of a system trained with that
corrupted speech. We also provide insight on the
approximations used in the model of the environment and
compare them with the lognormal approximation in PMC.

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