In this paper we focus on the challenging task of noise robustness for large vocabulary Continuous Speech Recognition (LVCSR) systems in non-stationary noise environments. We have extended our Model-Based Feature Enhancement (MBFE) algorithm - that we earlier successfully applied to small vocabulary CSR in the AURORA-2 framework - to cope with the new demands that are imposed by the large vocabulary size in the AURORA-4 task. To incorporate a priori knowledge of the background noise, we combine scalable Hidden Markov Models (HMMs) of the cepstral feature vectors of both clean speech and noise, using a Vector Taylor Series approximation in the power spectral domain. Then, a global MMSE-estimate of the clean speech is calculated based on this combined HMM. This technique is easily embeddable in the feature extraction module of a recogniser and is intrinsically suited for the removal of non-stationary additive noise. Our approach is validated on the AURORA-4 task, revealing a significant gain in noise robustness over the baseline.