By explicitly modeling the distortion sources of speech signals, model adaptation based on vector Taylor series (VTS) approaches have been shown to significantly improve the robustness of speech recognizers to environmental noise. However, the computational cost of VTS model adaptation (MVTS) methods hinders them from being widely used. In this paper, we propose to reduce the computation cost of standard MVTS by approximating the Jacobian matrix as a diagonal one (DJ-MVTS). We verified this approximation by showing that the diagonal elements of Jacobian matrixes provide dominant information and the model distortion introduced by this approximation is very small. DJ-MVTS gives similar accuracy as the standard MVTS method with significant computation cost reduction. With the setup in this paper, the proposed DJ-MVTS method achieves higher accuracy with lower computation cost than a featured-based VTS method.

Index Terms: vector Taylor series, Jacobian matrix, robust ASR