Um  Yeah. It was  it was , uh , the same system. Mm - hmm.
OK.
It was the same system. Mmm. Oh , yeah. A third thing is that , um ,  I play a little bit with the , um   finding what was different between , um , And there were a couple of differences , like the LDA filters were not the same. Um , he had the France Telecom blind equalization in the system. Um , the number o of MFCC that was  were used was different. You used thirteen and we used fifteen. Well , a bunch of differences. And , um , actually the result that he  he got were much better on TI - digits especially. So I 'm kind of investigated to see what was the main factor for this difference. And it seems that the LDA filter is  is  was hurting. Um ,  so when we put s some noise compensation the , um , LDA filter that  that 's derived from noisy speech is not more  anymore optimal. And it makes a big difference , um ,  on TI - digits trained on clean. Uh , if we use the  the old LDA filter , I mean the LDA filter that was in the proposal , we have , like , eighty - two point seven percent recognition rate , um , on noisy speech when the system is trained on clean speech. But  and when we use the filter that 's derived from clean speech we jumped  so from eighty - two point seven to eighty - five point one , which is a huge leap.
Mm - hmm.