Mm - hmm.
What I did was  I took the LDA filter 's design using clean speech , uh , mainly because the speech is already cleaned up after the enhancement so , instead of using this , uh , narrow  narrow band LDA filter that we submitted uh , I got new filters. So that seems to be giving  uh , improving over their uh , system. Slightly. But , not very significantly. And uh , that was uh , showing any improvement over  final  by plugging in an LDA. And uh , so then after  after that I  I added uh , on - line normalization also on top of that. And that  there  there also I n I found that I have to make some changes to their time constant that I used because th it has a  a mean and variance update time constant and  which is not suitable for the enhanced speech , and whatever we try it on with proposal - one. But um , I didn't  I didn't play with that time constant a lot , I just t g I just found that I have to reduce the value  I mean , I have to increase the time constant , or reduce the value of the update value. That 's all I found So I have to. Uh , Yeah. And uh , uh , the other  other thing what I tried was , I just um , uh , took the baseline and then ran it with the endpoint inf uh th information , just the Aurora baseline , to see that how much the baseline itself improves by just supplying the information of the  I mean the w speech and nonspeech. And uh , I found that the baseline itself improves by twenty - two percent by just giving the wuh.
Uh , can you back up a second , I  I  I missed something , uh , I guess my mind wandered. Ad - ad When you added the on - line normalization and so forth , uh , uh things got better again ?
Yeah. No.