On Real-Time Mean-and-Variance Normalization of Speech Recognition Features

This work aims at gaining an insight into the mean and variance normalization technique (MVN), which is commonly used to increase the robustness of speech recognition features. Several versions of MVN are empirically investigated, and the factors affecting their performance are considered. The reported experimental work with real-world speech data (Speecon) particularly focuses on the recursive updating of MVN parameters, paying attention to the involved algorithmical delay. First, we propose a decoupling of the look-ahead factor (which determines the delay) and the initial estimation of mean and variance, and show that the latter is a key factor for the recognition performance. Then, several kinds of initial estimations that make sense in different application environments are tested, and their performance is compared.