So Um uh wanted to discuss digits good briefly but that Right won't take too long OK agenda items Uh we have digits What else we got New version of the presegmentation New version of presegmentation Um do we wanna say something why about you summarize an the update of the uh transcript Update on transcripts And I guess that includes some the filtering for the the ASI refs too Mmm Filtering for what For the references that we need to go from the the fancy transcripts to the sort of be basically be a of a meeting that we had jointly this morning With Don as well Got it Anything else more pressing than those things So So why don't we just do those You OK yours was brief so OK well the w uh as you can see from the numbers on the digits almost done The digits goes up to about four thousand Um and so uh we probably will be done with the in um another couple weeks um depending on how many we read each time So there were a bunch that we skipped You know someone fills out the form and then not at the meeting and so blank Um but those are almost all filled in as well And so once done it would be very nice to train up a recognizer and actually start working with this data So And have so a corpus the size of One particular test set of Test So set OK I I extracted Ther there was a file sitting around which people have used here as a test set It had been randomized and so on and Oh I used to generate the order of Great particular ones Um So impressed by what we could do Is take the standard training set for train up with whatever you know great features we think we have uh for instance and then test on uh this test set And presumably uh it should do reasonably well on that and then presumably Yeah should go to the distant mike and it should do poorly Right And then we should get really smart over the next year or two and it And get inc better increase it by Yeah or two percent yeah Yeah Um but in order to do that we need to extract out the actual digits Right Um so that the reason not just a transcript is that false starts and misreads and miscues and things like that And so I have a set of scripts and where you just select the portion hit R um it tells you what the next one should be and you just look for that You know so it put on the screen The next set is six nine nine two two And you find that and hit the key and it records it in a file in a particular format So And this the question is should we have the transcribers do that or should we just do it Well some of us been do done eight meetings something like that just by hand Just myself rather So it will not take long Um Uh what what do you My think feeling is that we discussed this right before coffee and I think a a fine idea partly because um not un unrelated to their present skill set but it will add for them an extra dimension it might be an interesting break for them And also it is contributing to the uh c composition of the transcript cuz we can incorporate those numbers directly and be a more complete transcript So I think fine that There part there So is you think fine to have the transcribers do it Yeah OK one other small bit which is just entering the information which at s which is at the top of this form onto the computer to go along with the Yeah digits are recorded automatically And so just you know typing in name times time date and so on Um which again either they can do but it is you know firing up an editor or again I can do Or someone else can do And that you know not that that one not so sure if into the the things that I wanted to use the hours for because the the time that be spending doing that they wouldn't be able to be putting more words on Mmm But really your choice So your these two separate tasks that can happen Or do they have to happen at the same time No before don't have this you have to enter the data before you do the second task but they don't have to happen at the same time So OK just I have a file whi which has this information on it and then when you start using my scripts for extracting the times it adds the times at the bottom of the file And so um I mean easy to create the files and leave them blank and so actually we could do it in either order Oh OK Um sort of nice to have the same person do it just as a to make sure entering for the right person But either way Yeah Yeah just by way of uh uh a uh order of magnitude uh um been working with this Aurora uh data set And uh the best score on the nicest part of the data that is where got training and test set that are basically the same kinds of noise and so forth uh is about uh I think the best score was something like five percent uh error per digit So Per Per that digit digit right So if you were doing ten digit uh recognition you would really be in trouble So So the The point there and this is uh car noise uh uh things but but real real situation well real Um the uh one microphone close that they have as as this sort of thing close versus distant Uh but in a car instead of instead of having a projector noise car noise Uh but it wasn't artificially added to get some some artificial ratio It was just people driving around in a car So an indication uh that was with many sites competing and this was the very best score and so forth so Although More models like weren't that good right I mean the models are pretty crappy right I think that we could have done better on the models but the thing is that we got this this is the kind of typical number for all of the uh uh things in this task all of the um languages And so I I think probably the models would be better in some than in others Hmm Um so uh Anyway just an indication once you get into this kind of realm even if looking at connected digits it can be pretty hard Hmm gonna be fun to see how we compare at this Yeah Very we exciting