Yeah.
Hold that thought.
OK , sure.
Let me back up while we 're still on it. The  the other thing I was suggesting , though , is that given that you 're talking about binary features , uh , maybe the first thing to do is just to count and uh count co - occurrences and get probabilities for a discrete HMM cuz that 'd be pretty simple because it 's just  Say , if you had ten  ten events , uh that you were counting , uh each frame would only have a thousand possible values for these ten bits , and uh so you could make a table that would  say , if you had thirty - nine phone categories , that would be a thousand by thirty - nine , and just count the co - occurrences and divide them by the  the uh  uh uh occ uh count the co - occurrences between the event and the phone and divide them by the number of occurrences of the phone , and that would give you the likelihood of the  of the event given the phone. And um then just use that in a very simple HMM and uh you could uh do phone recognition then and uh wouldn't have any of the issues of the uh training of the net or  I mean , it 'd be on the simple side , but