Extended VTS for noise-robust speech recognition

Model compensation is a standard way of improving speech recognisers´ robustness to noise. Currently popular schemes are based on vector Taylor series (VTS) compensation. They often use the continuous time approximation to compensate dynamic parameters. In this paper, the accuracy of dynamic parameter compensation is improved by representing the dynamic features as a linear transformation of a window of static features. A modified version of VTS compensation is applied to the distribution of the window of static features and, importantly, their correlations. These compensated distributions are then transformed to standard static and dynamic distributions. The proposed scheme outperformed the standard VTS scheme by about 10% relative.