Yeah. So , uh  Yep. I mean , that  that was sort of one  I mean , the overall results seemed to be first place in  in  in the case of either , um , artificial reverberation or a modest sized training set. Uh , either way , uh , i uh , it helped a lot. And  But if you had a  a really big training set , a recognizer , uh , system that was capable of taking advantage of a really large training set  I thought that  One thing with the HTK is that is has the  as we 're using  the configuration we 're using is w s is  being bound by the terms of Aurora , we have all those parameters just set as they are. So even if we had a hundred times as much data , we wouldn't go out to , you know , ten or t or a hundred times as many Gaussians or anything. So , um , it 's kind of hard to take advantage of  of  of big chunks of data. Uh , whereas the other one does sort of expand as you have more training data.
Mm - hmm.
Mmm , yeah.
It does it automatically , actually. And so , um , uh , that one really benefited from the larger set. And it was also a diverse set with different noises and so forth. Uh , so , um , that , uh  that seemed to be  So , if you have that  that better recognizer that can  that can build up more parameters , and if you , um , have the natural room , which in this case has a p a pretty bad signal - to - noise ratio , then in that case , um , the right thing to do is just do  u use speaker adaptation. And  and not bother with  with this acoustic , uh , processing. But I think that that would not be true if we did some explicit noise - processing as well as , uh , the convolutional kind of things we were doing.