What is the effect of highly correlated data on a Convolutional Neural Network? A speech audio sample can be converted to MFCC coefficients for further analysis. I wanted to know the effect of correlated data on a CNN. I know the process of computing the MFCC coefficient, which applies Discrete Cosine Transform (DCT) to decorrelate the filter bank coefficients and yield a compressed representation of the filter banks. 

I wanted to argue on the usage of filter banks vs MFCC when we want to further analyze using a CNN. 

Filter banks result in highly correlated data.
MFCC result in decorrelated data.

Which of the two should i choose if i plan to train a CNN model for speech classification?
I actually don't believe it really matters.  When applying the same idea to visual problems, you'll find that the raw RGB vs a DCT compressed version lead to similar results as seen here.  If there exist a negative, one may argue that the correlated data may have a slower learning rate.  Nevertheless, the best way to figure out the effect is to give both a try.