So wha w what  an and you may not be in this case , come to think of it , because , uh , you 're just taking something that 's trained up elsewhere. So , what  what you  what you do in the full procedure  is you , um , uh , have an embedded training. So in fact you  the  the net is trained on , uh , uh , a , uh , Viterbi alignment of the training data that comes from your full system. And so that 's where the feedback comes all around , so that it is actually discriminant. You can prove that it 's  it 's a , uh  If you believe in the Viterbi assumption that , uh , getting the best path , uh , is almost equivalent to getting the best , uh , total probability , um , then you actually do improve that by , uh  by training up on local  local , uh  local frames. But , um , we aren't actually doing that here , because we did  we did that for a hybrid system , and now we 're plugging it into another system and so it isn't   i i i it wouldn't quite apply here.
Do y
So another huge experiment we could do would be to take the tandem features , uh , do SRI forced alignments using those features , and then re - do the net with those.
Mm - hmm.