An investigation of the impact of MVA normalization on the advanced front-end features

Feature normalization is a key objective in speech related applications. In this paper, we study the effects of the Mean subtraction, Variance normalization, and Autoregressive Moving Average (ARMA) filtering (MVA) normalization method on the ETSI Advanced Front-End (AFE) features. A series of experiments, on the Aurora-2 task, was conducted to show the impact of MVA normalization for different subsets of AFE feature components. Compared to the AFE baseline system, recognition results show performance improvement when only the logarithmic energy coefficient is normalized. However, the performance is degraded through the normalization of the rest of AFE coefficients. To investigate this degradation, other experiments were performed by eliminating the AFE implemented blind equalization post-processing block. It has shown that one part of this degradation can plausibly be interpreted as the effect of over-normalization caused by the MVA post-processing to the AFE original features. Furthermore, by analyzing the statistical distributions of AFE features we found that the effectiveness of MVA could also be affected by the high intra-frame variability of AFE features.