Um. Well , I 've  I 've talked to some of you already. Um , but I 'm , uh , looking into extending the work done by Larry Saul and John Allen and uh Mazin Rahim. Um , they  they have a system that 's , uh , a multi - band , um , system but their multi - band is  is a little different than the way that we 've been doing multi - band in the past , where um  Where we 've been @ @  uh taking  um   sub - band features and i training up these neural nets and  on  on phonetic targets , and then combining them some somehow down the line , um , they 're  they 're taking sub - band features and , um , training up a detector that detects for , um , these phonetic features for example , um , he presents um , uh , a detector to detect sonorance. And so what  what it basically is  is , um  it 's  there 's  at the lowest level , there  it 's  it 's an OR ga I mean , it 's an AND gate. So , uh , on each sub - band you have several independent tests , to test whether um , there 's the existence of sonorance in a sub - band. And then , um , it c it 's combined by a soft AND gate. And at the  at the higher level , for every  if , um  The higher level there 's a soft OR gate. Uh , so if  if this detector detects um , the presence of  of sonorance in any of the sub - bands , then the detect uh , the OR gate at the top says , " OK , well this frame has evidence of sonorance. "
What are  what are some of the low level detectors that they use ?
And these are all  Oh , OK. Well , the low level detectors are logistic regressions. Um , and the , uh
So that , by the way , basically is a  is one of the units in our  in our  our neural network.