From  and so if you 're splitting things up into utterances  So , for instance , in a dialogue system ,  where you 're gonna be asking , uh , you know , th for some information , there 's some initial th something. And , you know , the first time out you  you might have some general average. But you  you d you don't have very much information yet. But at  after they 've given one utterance you 've got something. You can compute your mean cepstra from that ,
Mm - hmm.
and then can use it for the next thing that they say , uh , so that , you know , the performance should be better that second time. Um ,  and I think the heuristics of exactly how people handle that and how they handle their training I 'm sure vary from place to place. But I think the  ideally , it seems to me anyway , that you  you would wanna do the same thing in training as you do in test. But that 's  that 's just , uh , a prejudice. And I think anybody working on this with some particular task would experiment.
Right. I g I guess the question I had was , um , amount of data e u was the amount of data that you 'd give it to , um  update this estimate. Because say you  if you have say five thousand utterances in your training set ,  um , and you  you keep the mean from the last utterance , by the time it gets to the five thousandth utterance