Yeah.
Yeah.
So , uh , you want to go next , Dave ? And we 'll do
Oh. OK , sure. So , um , last week I finally got results from the SRI system about this mean subtraction approach. And , um , we  we got an improvement , uh , in word error rate , training on the TI - digits data set and testing on Meeting Recorder digits of , um ,  six percent to four point five percent , um , on the n on the far - mike data using PZM F , but , um , the near - mike performance worsened , um , from one point two percent to two point four percent. And , um , wh why would that be , um ,  considering that we actually got an improvement in near - mike performance using HTK ? And so , uh , with some input from , uh , Andreas , I have a theory in two parts. Um , first of all HTK  sorry , SR - the SRI system is doing channel adaptation , and so HTK wasn't. Um , so this , um  This mean subtraction approach will do a kind of channel  normalization and so that might have given the HTK use of it a boost that wouldn't have been applied in the SRI case. And also , um , the  Andreas pointed out the SRI system is using more parameters. It 's got finer - grained acoustic models. So those finer - grained acoustic models could be more sensitive to the artifacts  in the re - synthesized audio. Um. And me and Barry were listening to the re - synthesized audio and sometimes it seems like you get of a bit of an echo of speech in the background. And so that seems like it could be difficult for training , cuz you could have  different phones  lined up with a different foreground phone ,  um ,  depending on  the timing of the echo. So , um , I 'm gonna try training on a larger data set , and then , eh , the system will have seen more examples o of these artifacts and hopefully will be more robust to them. So I 'm planning to use the Macrophone set of , um , read speech , and , um  Hmm.