We just , uh , you know , trained up systems to be more discriminant. And , uh , we did this , uh , RASTA - like filtering which was done in the log domain and was tending to handle convolutional noise. We did  we actually did nothing about additive noise. So , um , the , uh , spectral sub subtraction schemes a couple places did seem to seem to do a nice job. And so , uh , we 're talking about putting  putting some of that in while still keeping some of our stuff. I think you should be able to end up with a system that 's better than both but clearly the way that we 're operating for this other stuff does involved some latency to  to get rid of most of that latency. To get down to forty or fifty milliseconds we 'd have to throw out most of what we 're doing. And  and , uh , I don't think there 's any good reason for it in the application actually. I mean , you 're  you 're  you 're speaking to a recognizer on a remote server and , uh , having a  a  a quarter second for some processing to clean it up. It doesn't seem like it 's that big a deal.
Mm - hmm.
These aren't large vocabulary things so the decoder shouldn't take a really long time , and.
And I don't think anybody 's gonna notice the difference between a quarter of a second of latency and thirty milliseconds of latency.