Hmm.
You can throw something in. You can do pretty well. But I remember , in fact , when you were working on that , and you worked on for few months , as I recall , and you got to , say ninety - three percent , and getting to ninety - four   really really hard.
Mm - hmm. Another year.
Yeah , yeah. So , um  And th th the other tricky thing is , since we are , uh , even though we 're not  we don't have a strict prohibition on memory size , and  and computational complexity , uh , clearly there 's some limitation to it. So if we have to  if we say we have to have a pitch detector , say , if we  if we 're trying to incorporate pitch information , or at least some kind of harmonic  harmonicity , or something , this is another whole thing , take a while to develop. Anyway , it 's a very very interesting topic. I mean , one  I think one of the  a lot of people would say , and I think Dan would also , uh , that one of the things wrong with current speech recognition is that we  we really do throw away all the harmonicity information. Uh , we try to get spectral envelopes. Reason for doing that is that most of the information about the phonetic identity is in the spectral envelopes are not in the harmonic detail. But the harmonic detail does tell you something. Like the fact that there is harmonic detail is  is real important. So. Um. So , uh. So I think  Yeah. So  wh that  so the  the other suggestion that just came up was , well what about having him  work on the , uh ,  multilingual super f superset  kind of thing. Uh , coming up with that and then , you know , training it  training a net on that , say , um , from  from , uh  from TIMIT or something. Is that  or uh , for multiple databases. What  what would you  what would you think it would  wh what would this task consist of ?