On average, they are 1.35 peaks per word for the trained model against 0.1 peak per word for our baseline condition (untrained model), showing that cosine evolution is linear in the latter but not in the former.
in our baseline condition (untrained model), each MFCC vector equally contributes to the final representation, whereas in our trained model some MFCC vectors are more de-6 consisting only of randomly initialised weights cisive for the final representation than others.