Normalize speech features per dimensions or across dimensions I am working on pipelines for audio processing (audio features that will go into a neural net to create some embeddings).

We are using two features : Filter banks, or MFCC.

I was wondering what was the good approach with normalization : 

Should we normalize those frames on each dimension (each frequency / mfcc coefficient), or normalize across all dimensions ? 

This could lead to important differences in the relative importance of features .
Maybe a good starter would be to aggregate those features across your frames into one single feature vector, compute the mean and standard deviation and perform z-score normalization.