Normalizing the Speech Modulation Spectrum for Robust Speech Recognition

This paper presents a novel feature normalization technique for robust speech recognition. The proposed technique normalizes the temporal structure of the feature to reduce the feature variation due to environmental interferences. Specifically, it normalizes the utterance-dependent feature modulation spectrum to a reference function by filtering the feature using a square-root Wiener filter in the temporal domain. We show experimentally that the proposed technique when combined with mean and variance normalization technique (MVN) reduces the word error rate significantly on the AURORA-2 task, with relative error rate reduction 69.11% compared to the baseline.