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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In  Proc. Int. Conf. on Spoken Language Processing

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