Sensitivity analysis of noise robustness methods

This work addresses the problem of noise robustness from the standpoint of the sensitivity to noise estimation errors. Since the noise is usually estimated in the power-spectral domain, we show that the implied error in the cepstral domain has interesting properties. These properties allow us to compare two key methods used in noise robust speech recognition: spectral subtraction and parallel model combination. We show that parallel model combination has an advantage over spectral subtraction because it is less sensitive to noise estimation errors. Experimental results on the Aurora2 database confirm our theoretical findings, with parallel model combination clearly outperforming spectral subtraction and other well-known signal-based robustness methods. Our Aurora2 results with parallel model combination, a basic MFCC front-end and a simple noise estimation are close to the best results obtained on this database with very complex signal processing schemes. theoretical findings.