Yeah , yeah.
y  Right ? And then um  then see ,  again , how  We know that there 's a mis there 's a uh  a  a loss in performance when the neural net is trained on conditions that are different than  than , uh we 're gonna test on , but well , if you look over a range of these different tests um , how well do these different ways of combining the straight features with the MLP features , uh stand up over that range ?
Mm - hmm.
That 's  that  that seems like the  the  the real question. And if you know that  So if you just take PLP with uh , the double - deltas. Assume that 's the p the feature. look at these different ways of combining it. And uh , take  let 's say , just take uh multi - English cause that works pretty well for the training.