Effect of Speech and Noise Cross Correlation on AMFCC Speech Recognition Features

When designing noise robust speech recognition feature extraction algorithms, it is common to assume that the noise and speech signal are uncorrelated. This assumption allows the cross correlation terms to be ignored in the equations that describe the operation of these algorithms, thus making the mathematics more tractable. In this paper, we investigate the validity of this assumption in the context of the autocorrelation mel frequency cepstral coefficient (AMFCC) feature extraction algorithm. To carry out the investigation, we designed a modified AMFCC algorithm that forces the cross terms in the noisy signal autocorrelation equation to be zero. We then compared the performance of the modified algorithm to the un-modified algorithm in recognition experiments performed using the AURORA II database. From these evaluations, we show that the assumption is fair in 5 out of six tested noise cases. The difference in recognition accuracy between the AMFCC and modified AMFCC for these five noises was less than 5%.