But for the new alignments what you generated , you just copied the channel zero to channel one , right ? Yeah.
Right. Yeah. Um. And eh , hhh actually when we look at  at the VAD ,  for some utterances it 's almost perfect , I mean , it just dropped one frame , the first frame of speech or  So there are some utterances where it 's almost one hundred percent VAD performance.
Hmm.
Uh , but  Yeah. Mmm  Yep. So the next thing is um , I have the spreadsheet for three different system. But for this you only have to look right now on the SpeechDat - Car performance uh , because I didn't test  so  I didn't test the spectral subtraction on TI - digits yet. Uh , so you have three she sheets. One is the um proposal - one system. Actually , it 's not exe exactly proposal - one. It 's the system that Sunil just described. Um , but with uh , Wiener filtering from um , France Telecom included. Um , so this gives like fifty - seven point seven percent , uh , s uh , error rate reduction on the SpeechDat - Car data. Mmm , and then I have two sheets where it 's for a system where  uh , so it 's again the same system. But in this case we have spectral subtraction with a maximum overestimation factor of two point five. Uh , there is smoothing of the gain trajectory with some kind of uh , low - pass filter , which has forty milliseconds latency. And then , after subtraction um , I add a constant to the energies and I have two cases d where  The first case is where the constant is twenty - five DB below the mean speech energy and the other is thirty DB below. Um , and for these s two system we have like fifty - five point , uh , five - percent improvement , and fifty - eight point one. So again , it 's around fifty - six , fifty - seven. Uh