So that was stuff he was doing with you. Yeah.
I mean , it 's  I guess one thing we 're learning is that the amount  We have eight meetings there because we couldn't use the non - native  all non - native meetings and  it 's , well , probably below threshold on enough data for us for the things we 're looking at because the  prosodic features are  very noisy and so you  you need a lot of data in order to model them. Um , so we 're starting to see some patterns and we 're hoping that maybe with ,  I don't know , double or triple the data  with twenty meetings or so , that we would start to get better results. But we did find that some of the features that , I gue Jane would know about , that are expressing sort of the  distance of , um ,  boundaries from peaks in the utterance and  some  local , um , range  pitch range effects , like how close people are to their floor , are showing up in these classifiers , which are also being given some word features that are cheating , cuz they 're true words. Um , so these are based on forced alignment. Word features like , um , word frequency and whether or not something 's a backchannel and so forth. So , we 're starting to see , I think , some interesting patterns.
So the dominant features , including everything , were those  those quasi - cheating things. Right ? Where these are
Sometimes not.