Hmm.
That 's  that 's worth looking into. Um , d and , um , ap ap apart from that , I guess the  the main thing I have t ta I have to talk is , um , where I 'm planning to go over the next week. Um. So I 've been working on integrating this mean subtraction approach into the SmartKom system. And there 's this question of , well , so , um , in my tests before with HTK I found it worked  it worked the best with about twelve seconds of data used to estimate the mean , but , we 'll often have less  in the SmartKom system. Um. So I think we 'll use as much data as we have  at a particular time , and we 'll   we 'll concatenate utterances together , um , to get as much data as we possibly can from the user. But ,  um ,  there 's a question of how to set up the models. So um , we could train the models. If we think twelve seconds is ideal we could train the models using twelve seconds to calculate the mean , to mean subtract the training data. Or we could , um , use some other amount. So  like I did an experiment where I , um , was using six seconds in test , um , but , for  I tried twelve seconds in train. And I tried , um , um , the same in train  I 'm a I tried six seconds in train. And six seconds in train  was about point three percent better. Um , and   um , it 's not clear to me yet whether that 's  something significant. So I wanna do some tests and , um ,  actually make some plots of , um  for a particular amount of data and test what happens if you vary the amount of data in train.
Mm - hmm.
Uh , Guenter , I don't know if you t  followed this stuff but this is , uh ,  a uh , uh , long - term  long - term window F F Yeah. Yeah , he  you talked about it.