Yeah.
So ,  it seems to me that , you know , if you take away the  the   the difference of this  primary features ,  and , say , you use  as we had talked about maybe doing  you use P - RASTA - PLP or something for the  the primary features ,  um , then this feature discovery ,  uh , uh , thing  is just what he 's talking about doing , too , except that he 's talking about doing them in order to discover  intermediate categories that correspond  to these  uh , uh , what these sub - features are  are  are  are showing you. And , um ,  the other difference is that , um ,  he 's doing this in a  in a multi - band setting , which means that he 's constraining himself  to look across time in some f relatively limited , uh , uh , spectral extent. Right ? And whereas in  in this case you 're saying " let 's just do it unconstrained ". So they 're  they 're really pretty related and maybe they 'll be  at some point where we 'll see the  the connections a little better and  connect them.
Hmm.
Mm - hmm. Um. Yeah , so  so that 's the  that 's the first part  uh , one  one of the ideas to get at some   some patterns of intermediate categories. Um ,  the other one  was ,  um , to ,  uh , come up with a  a  a model   um , a graphical model ,  that treats  the intermediate categories  as hidden  hidden variables , latent variables , that we don't know anything about , but that through ,  um , s statistical training and the EM algorithm ,  um , at the end of the day ,  we have , um  we have learned something about these  these latent , um  latent variables which happen to correspond to  intermediate categories. Um.   Yeah , and so those are the  the two directions that I 'm  I 'm looking into right now. And , uh ,  um    Yeah. I guess that 's  that 's it.