Yeah. So wh yeah , where this fits into the rest in  in my mind , I guess , is that um  we 're looking at different ways that we can combine  uh different kinds of  of rep front - end representations  um in order to get robustness under difficult or even , you know , typical conditions. And part of it , this robustness , seems to come from  uh multi - stream or multi - band sorts of things and Saul seems to have  a reasonable way of looking at it , at least for one    one um articulatory feature. The question is is can we learn from that  to change some of the other methods we have , since  I mean , one of the things that 's nice about what he had I thought was that  that it  it um  the decision about how strongly to train the different pieces is based on uh a  a reasonable criterion with hidden variables rather than  um just assuming  that you should train e e every detector uh with equal strength  towards uh it being this phone or that phone. Right ? So it  so um  he 's got these um uh uh
Hmm.
he " AND 's " between these different  features. It 's a soft " AND " , I guess but in  in principle  you  you wanna get a strong concurrence of all the different things that indicate something and then he " OR 's " across the different  soft " OR 's " across the different uh  multi - band channels. And um  the weight yeah , the target for the training of the " AND "  " AND ' ed " things  is something that 's kept  uh as a hidden variable , and is learned with EM. Whereas what we were doing is  is uh  taking  the phone target and then just back propagating from that
So he doesn't have