Yeah. But I actually stuck most of this in our m last meeting with Guenter. Um , but I 'll just  Um , so the last week , uh , I showed some results with only SpeechDat - Car which was like some fifty - six percent. And , uh , I didn't h I mean , I  I found that the results  I mean , I wasn't getting that r results on the TI - digit. So I was like looking into " why , what is wrong with the TI - digits ? ". Why  why I was not getting it. And I found that , the noise estimation is a reason for the TI - digits to perform worse than the baseline. So , uh , I actually , picked th I mean , the first thing I did was I just scaled the noise estimate by a factor which is less than one to see if that  because I found there are a lot of zeros in the spectrogram for the TI - digits when I used this approach. So the first thing I did was I just scaled the noise estimate. And I found  So the  the results that I 've shown here are the complete results using the new  Well , the n the new technique is nothing but the noise estimate scaled by a factor of point five. So it 's just an ad - hoc  I mean , some intermediate result , because it 's not optimized for anything. So the results  The trend  the only trend I could see from those results was like the  the p the current noise estimation or the , uh , noise composition scheme is working good for like the car noise type of thing. Because I 've  the only  only  p very good result in the TI - digits is the noise  car noise condition for their test - A , which is like the best I could see that uh , for any non - stationary noise like " Babble " or " Subway " or any  " Street " , some " Restaurant " noise , it 's like  it 's not performing w very well. So , the   So that  that 's the first thing I c uh , I could make out from this stuff. And
Yeah , I think what is important to see is that there is a big difference between the training modes.
Yeah.
Uh - huh. If you have clean training , you get also a fifty percent improvement.