A novel static parameter calculation method for model compensation

Suliang Bu, Yunxin Zhao, Yanmin Qian, Kai Yu

Abstract: Vector Taylor Series (VTS) based model compensation approach has been successfully applied to various robust speech recognition tasks. In this paper, we propose a novel method of variable transformation to calculate the static statistics. In addition, we provide a detailed explanation of VTS and random variable transformations adopted in some recent papers. Experiments on Aurora 4 showed that the proposed approach obtained 22.8% relative WER reduction over the traditional first-order VTS methods.