A Feature Compensation Approach using High-Order Vector Taylor Series Approximation of An Explicit Distortion Model for Noisy Speech Recognition

Jun Du; Qiang Huo

A Feature Compensation Approach using High-Order Vector Taylor Series Approximation of An Explicit Distortion Model for Noisy Speech Recognition

Jun Du ,
Qiang Huo

Proc. of INTERSPEECH 2008 | January 2008

Published by International Speech Communication Association

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This paper presents a new feature compensation approach to noisy speech recognition by using high-order vector Taylor series (HOVTS) approximation of an explicit model of environmental distortions. Formulations for maximum likelihood (ML) estimation of noise model parameters and minimum meansquared error (MMSE) estimation of clean speech are derived. Experimental results on Aurora2 database demonstrate that the proposed approach achieves consistently significant improvement in recognition accuracy compared to traditional first-order VTS based feature compensation approach.

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