And so , we haven't done that yet.
Now the other que related question , though , is  is ,  uh , what 's the boot models for these things ?
Th - th the boot models are trained from scratch. So we compute , um  So , we start with a , um , alil alignment that we computed with the b sort of the best system we have. And  and then we train from scratch. So we com we do a , you know , w um   We collect the  uh , the observations from those alignments under each of the feature sets that  that we  train. And then , from there we do , um  There 's a lot of , actually   The way it works , you first train a phonetically - tied mixture model. Um. You do a total of  First you do a context - independent PTM model. Then you switch to a context  You do two iterations of that. Then you do two iterations of  of  of context - dependent phonetically - tied mixtures. And then from that you  you do the  you  you go to a state - clustered model ,
Yeah.