Yeah. But another way of looking at it  might be that  I mean , what w we we are coming up with feature sets after all. So another way of looking at it is that  um , the mel cepstru mel  spectrum , mel cepstrum ,  any of these variants , um , give you the smooth spectrum. It 's the spectral envelope. By going back to the FFT ,  you 're getting something that is  more like the raw data. So the question is , what characterization  and you 're playing around with this  another way of looking at it is what characterization  of the difference between  the raw data  and this smooth version  is something that you 're missing that could help ? So , I mean , looking at different statistical measures of that difference , coming up with some things and just trying them out and seeing if you add them onto the feature vector does that make things better or worse in noise , where you 're really just i i the way I 'm looking at it is not so much you 're trying to f find the best  the world 's best voiced - unvoiced , uh , uh , classifier ,
Mm - hmm.
Mmm.
but it 's more that ,  you know , uh , uh , try some different statistical characterizations of that difference back to the raw data