Um , yeah , I mean , it was pretty  it was pretty tiny. Yeah.
Uh - huh. So that was encouraging. And , um , that  that  um , that 's encouraging for  for the idea of using it in an interactive system like And , um , another issue I 'm  I 'm thinking about is in the SmartKom system. So say twe twelve seconds in the earlier test seemed like a good length of time , but what happens if you have less than twelve seconds ? And , um  So I w bef before , um  Back in May , I did some experiments using , say , two seconds , or four seconds , or six seconds. In those I trained the models using mean subtraction with the means calculated over two seconds , or four seconds , or six seconds. And , um , here , I was curious , what if I trained the models using twelve seconds but I f I gave it a situation where the test set I was  subtracted using two seconds , or four seconds , or six seconds. And , um  So I did that for about three different conditions. And , um  I mean , I th I think it was , um , four se I think  I think it was , um , something like four seconds and , um , six seconds , and eight seconds. Something like that. And it seems like it  it  it hurts compared to if you actually train the models  using th that same length of time but it  it doesn't hurt that much. Um , u usually less than point five percent , although I think I did see one where it was a point eight percent or so rise in word error rate. But this is , um , w where , um , even if I train on the , uh , model , and mean subtracted it with the same length of time as in the test , it  the word error rate is around , um , ten percent or nine percent. So it doesn't seem like that big a d a difference.
But it  but looking at it the other way , isn't it  what you 're saying that it didn't help you to have the longer time for training , if you were going to have a short time for
That  that 's true. Um ,