this you know very limited training HTK system Uh but the other is that um the digits recorded here in this room with these close mikes i uh are actually a lot harder than the I think you know one reason for that uh might be that still even though there still is some noise and some room acoustics And another might be that uh I would presume that in the studio uh uh situation recording read speech that if somebody did something a little funny or n pronounced something a little funny or made a little didn't include that they didn't include it they made them do Whereas again I took out the ones that I noticed that were blatant that were Mmm correctable Yeah that if someone just read the wrong digit I corrected it And Yeah another one where Jose couldn't tell whether I couldn't tell whether he was saying zero or six And I asked him and he couldn't tell either So I just cut it out You know so Yeah just e edited out the first i uh word of the utterance Um so a little bit of correction but definitely not as clean as So my expectations is would especially I think is all American English Right So it would probably do even a little better still on the SRI system but we could give Well try But remember using a telephone bandwidth here uh on this uh on this SRI system so um I was I thought that maybe actually a good thing because it it gets rid of some of the uh the noises um you know in the the below and above the um the you know speech bandwidth and um I suspect that to get sort of the last bit out of these recordings you would have to in fact uh use models that uh were trained on data And of course we can't do that or Wha I thought t yeah It is in in fact OK we looked it up and it was actually twenty kilohertz sampling Oh right I I did look that up I couldn't remember whether Yeah that was or one Right other digit tasks But but I would Yeah easy enough to try just run it on Yeah So Morgan w breath noise one might wanna one mike issue little bit one issue with with that is that um the system has this uh notion of a speaker to which is used in adaptation variance norm uh you know both in uh mean and variance normalization and also in the VTL estimation So Yeah I noticed y the script that So extracted so th so does does um the database have speakers Yep that are known Yep And is there is there enough data or a comparable know comparable amount of data to I don't what we have in our recordings here I don't know how many speakers there are and OK Yeah and how many speakers per utterance Well the other thing would be to do it without the adaptation and compare to these numbers without the adaptation That Right Uh but not so much worried about the adaptation actually than than the um um the uh VTL estimation Right If you have only one utterance per speaker you might actually screw up on estimating the the warping uh factor So um I strongly suspect that they have more speakers than we do Right So not uh amount of speakers the num the amount of data per speaker Right So we we could Right probably do an extraction that was Right roughly equivalent So Um So although I I sort of know how to run it there are a little a f OK few details key and there that have to So th the system actually extracts the speaker ID from the waveform names Right I saw And that a a script and that is actually all in one script So this one script that parses waveform names and extracts things like the um speaker uh ID or something that can stand in as a speaker ID So we might have to modify that script to recognize the Right um speakers um in the in the uh um Right database And that uh Or you can fake you can fake names for these waveforms that resemble the names that we use here for the Right for the meetings That would be the sort of probably the I might safest do way that to anyway to to do because we may have to do an extract to get the amount of data per speaker about right The other thing is isn't isolated digits Or is that another one I looked through a bunch of the digits t corp corpora and now all blurring Cuz one of them was literally people reading a single digit And then others were connected digits Yeah Most of is connected digits I think The I mean we had a Bellcore corpus that we were using Maybe was Bell Gram that was isolated digits Digits Alright By Um I think we can improve these numbers if we care to compr improve them by um not starting with the Switchboard models but by taking the Switchboard models and doing supervised adaptation on a small amount of digit data collected Yep in this setting Because that would adapt your models to the room acoustics and f for the microphones you know to the noise And that should really improve things um further And then you use those adapted models which are not speaker adapted but sort of acous Channel you adapted channel adapted use that as the starting models for your speaker adaptation Yeah But the thing is uh I mean w when you it depends whether ju were just using this as a a starter task for you know to get things going for conversational or if really interested i in connected digits And I I think the answer is both Well And for for I connected digits over the telephone you don't actually want to put a whole lot of effort into adaptation because somebody gets on the phone and says a number and then you just want it You don't This don't Right better Um but you know I uh my impression was that you were actually interested in the microphone uh problem I mean So you want to you want to the obvious thing to try Oh Right Then eh Right because you you don't have any where the most m acoustic mismatch is between the currently used models and the Right the r the set up here So Yeah So be anoth another interesting data point I mean I I guess saying I don't know if want to do that way as Other the way Liz as Now all It watching f me it clips over Alright ears This There way you go If you have a strong watching if you have a This strong terrible preference you could use this just we we think it has some spikes So uh we we didn't use that one