Um un unsurprisingly S Adam shouldn't the affect golden anything you see this here yeah y bah OK no Oh What and happens is it actually affects the script Huh if we know about it then I guess Well always acoustic checked one for shouldn't it do but anything they Yeah agree know exactly agree what affected it but I talk have about But Yeah Yeah show you the Yeah effect point transcription you know I mean I Mmm remember hearing it and and having time bins but I do remember seeing a once the transcriber produce an incorrect XML file where one Oh synch numbers was incorrect what happened time Oh monotonic digits yeah than mean should right things that are l in smaller increments than a frame Oh interesting Oh OK so Hmm then I mean you look at that and got you know more than three significant digits in Oh be right yeah Yeah Mmm bug anyway just why we only have twenty minutes but a significant amount of Um there are like more The other cuz one I lot saw zeros that I it tacked on just because of the way the script ran I mean yeah there were there was a point Yeah that was fine The other That I that saw OK non OK synch times and that definitely Uh indicra indicates would bug really be a problem yeah So anyway these are just the ones that are Yeah the prebug for one meeting um and So very encouraging really encouraging Cool this is free recognition no Mmm I mean the language model for Switchboard is totally different so you can see some like Trent this Lott Trent Lott which um I mean these are get sort those funny ones a lot of perfect ones and good ones and all the references I mean you can read them and when we get more results I can look through and see but and as I said um like to pretty look good the lattices because it sounded like even the ones it got wrong it sort of got it right Well Sounds so likes guess we can generate There are a fair number um are you know where got the plural S wrong or the Yeah inflection cares on and there the were verb lots of of course so if on No those are actually a lot of the errors I think are out of vocabulary so is it like PZM is three words PZM I mean nothing Right Ri ri language right model for PZM or um say no language for PZM No language model I mean those Do you mean so every time someone says PZM an error Maybe we shouldn't say PZM in these meetings Well well all kinds of other stuff like Jimlet and Yeah I right mean Jimlet um anyway there Well we don't even know what that means Yeah I right but this is really encouraging because so I mean the bottom line is even though not a huge amount of data um it should be uh reasonable to actually run recognition and be like within the scope of of r reasonable s you know Switchboard this is like h about how well we do on data with the trained Right trained recognizer and is Excellent got sort of a different population of speakers and a different topic and talking about things in the news that happened after so there was Yeah so great Yeah so in better Um than we were say when we did had the workshop and we were all getting like seventy percent error on Switchboard Oh yeah I mean know is really Mmm and thanks to Andreas Mmm this is a Yeah especially Oh for um first run I mean Yeah you the first yeah ran of Switchboard I got a hundred twenty percent word error but So and what al also this Right means is Not that Switchboard um uh Well I Broadcast bunch of things in News this note to various people especially I guess um with Jane that that would help for since we have this new data now uh in order to go from the transcripts more easily to um just the words that the recognizer would use for scoring I had to deal with some of it by hand but I think a lot of it can be automated Oh s by thing I guess I didn't get so you know the language model was straight from from bigram from Switchboard the acoustic models were also from Switchboard Yeah or or Yeah amazing anything from great this No acoustic data in yet And actually amazing we actually um used Switchboard telephone bandwidth models Yeah yeah the so we ones on there are Yeah only acoustic training data that Right have a lot of and I guess Ramana so a guy at SRI said that um not a huge amount of difference going from Right probably lose a huge amount but we won't know because we don't have any full band models for s conversational speech probably not as bad as going So f using full band models on telephone band speech Oh right yeah Right Right so Yeah but for Broadcast so News when we we played around between the two there wasn't a huge Right it Yeah not so I I deal so I loss Although say combining em language well model is not just Switchboard also I mean uh actually more data is from Broadcast News but with a little less weight Like Trent Lott because have been from I guess Switchboard was before uh By the way just for Good fun we also ran I mean our complete system starts by doing ge a gender detection so just for the heck of it a I hundred ran percent that male um and it might be reassuring for everybody to know that it got all the genders j so Oh It glad all two Yeah genders you know Jane and Adam Yes have you kn about equal performance and uh and interesting cuz I think the their language models are quite different so and I pretty sure from listening to Eric that you know given the words he was saying and given his pronunciation that the reason that so much worse is the lapel Right That Yeah lot of sense So yeah nice now if we can just sort of eliminate Very lapel possible one when when we get new Yeah microphones that would I be worth would it bet on that um cuz he certainly in that when as a as a burp user he was he was a pretty uh strong one Sheep Yeah he he he sounded Yeah me just from he sounded like a it a sheep or a goat A sheep Baah Sounded good good Yeah so um so I guess the good news is that and and again this is without a lot of the sort of bells and whistles that we c can do with the SRI system and have more data and we can also start to maybe