Temporal Structure Normalization of Speech Feature for Robust Speech Recognition

摘要

This letter presents a new feature normalization technique to normalize the temporal structure of speech features. The temporal structure of the features is partially represented by its power spectral density (PSD). We observed that the PSD of the features varies with the corrupting noise and signal-to-noise ratio. To reduce the PSD variation due to noise, we propose to normalize the PSD of features to a reference function by filtering the features. Experimental results on the AURORA-2 task show that the proposed approach when combined with the mean and variance normalization improves the speech recognition accuracy significantly; the system achieves 69.11% relative error rate reduction over the baseline.