And uh because in the ideal case we would be going for posterior probabilities , if we had  uh enough data to really get posterior probabilities and if the  if we also had enough data so that it was representative of the test data then we would in fact be doing the right thing to train everything as hard as we can. But um this is something that 's more built up along an idea of robustness from  from the beginning and so you don't necessarily want to train everything up towards the
So where did he get his  uh his tar his uh high - level targets about what 's sonorant and what 's not ?
From uh canonical mappings  um at first
OK.