Second order vector taylor series based robust speech recognition

Vector Taylor Series (VTS) model based compensation approach has been successfully applied to various robust speech recognition tasks. In this paper, a novel method to derive the formula to calculate the static and dynamic statistics based on second-order VTS (sVTS) is presented, which provides a new insight on the VTS approximation. Lengthy derivation could therefore be avoided when high order VTS is used and the proposed approach is more compact and easier to implement compared to previous high order VTS approaches. Experiments on Aurora 4 showed that the proposed sVTS based model compensation approach obtained 16.7% relative WER reduction over traditional first-order VTS (fVTS) approach.