On the impact of normalizing power-based features on robustness against noise for speech recognition

Many power based features have been proposed in previous studies as alternative to the conventional feature, i.e. MFCC, for speech recognition. These features are of interest because they are empirically shown to be more robust than MFCC in noisy environments. Some studies argue that the compressions of power functions which are less sensitive than log for low energy spectra is one of the reasons while others agree that their performances are heavily affected by their normalization techniques. In this paper, the author focuses on investigating the effect of two normalization methods on power based features for the robustness of speech recognition in noisy environments. The paper specifically addresses features based on the q-log function, an example of power functions widely used in Tsallis statistics. The analysis suggests subtracting the mean may not be the best normalization technique due to its non-additive properties which is confirmed by the experimental results on Aurora-2. The results also confirm that the normalization may contribute more to the robustness of the features than the effect of the q-log compression.