Well , that 'll teach them to roll their window up.
I mean , in these cases , if you go from the  the , uh  I mean , I don't remember the numbers right off , but if you  if you go from the well - matched case to the medium ,  it 's not an enormous difference in the  in the  the training - testing situation , and  and  and it 's a really big  performance drop.
Mm - hmm.
You know , so , um  Yeah , I mean the reference one , for instance  this is back old on , uh  on Italian  uh , was like  six percent error for the well - matched and eighteen for the medium - matched and sixty for the   for highly - mismatched. Uh , and , you know , with these other systems we  we  helped it out quite a bit , but still there 's  there 's something like a factor of two or something between well - matched and medium - matched. And  so I think that  if what you 're   if the goal of this is to come up with robust features , it does mean  So you could argue , in fact , that the well - matched is something you shouldn't be looking at at all , that  that the goal is to come up with features  that will still give you reasonable performance , you know , with again gentle degregra degradation , um , even though the  the testing condition is not the same as the training.