We were just talking about , um ,  some , uh , some work that we 're interested in. Kind of inspired by the stuff by Larry Saul with the , uh  uh , learning articulatory feature in  I think , in the case of his paper  with sonorance based on , uh , multi - band information where you have a  a combination of gradient learning an and , uh , EM.
Mm - hmm.
Um , and    Um , so , I think that , you know , this is a , uh  this is a neat data set. Um , and then , uh , as we mentioned before , we also have the  the new , uh , digit set coming up from recordings in this room. So , there 's a lot of things to work with. Um and , uh what I like about it , in a way , is that , uh , the results are still so terrible. Uh   Uh   I mean , they 're much better than they were , you know. We 're talking about thirty to sixty percent , uh , error rate reduction. That 's  that 's really great stuff to  to do that in relatively short time. But even after that it 's still , you know , so poor that  that , uh , no one could really use it. So , um I think that 's great that  because  and y also because again , it 's not something  sometimes we 've gotten terrible results by taking some data , and artificially , you know , convolving it with some room response , or something  we take a very  Uh , at one point , uh , Brian and I went downstairs into the  the basement where it was  it was in a hallway where it was very reverberant and we  we made some recordings there. And then we   we , uh  uh , made a simulation of the  of the room acoustics there and  and applied it to other things ,
Mm - hmm.