HMM Adaptation Using Vector Taylor Series for Noisy Speech Recognition

Alex Acero, Li Deng, T. Kristjansson, and J. Zhang

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.

Details

Publication typeInproceedings
Published inProc. Int. Conf. on Spoken Language Processing
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