F: Mm - hmm .
B: I think , you know , one reason for that , uh , might be that there 's still even though it 's close - talking , there still is some noise and some room acoustics .
F: Mm - hmm .
B: And another might be that , uh , I 'd I would presume that in the studio , uh , uh , situation recording read speech that if somebody
B: did something a little funny or n pronounced something a little funny or made a little that they didn't include it ,
E: They didn't include it .
B: they made them do it again .
E: Whereas , I took out the ones that I noticed that were blatant that were correctable .
B: Mmm .
B: Yeah .
E: So that , if someone just read the wrong digit , I corrected it .
E: And then there was another one where Jose couldn't tell whether I couldn't tell whether he was saying zero or six .
B: Yeah .
E: And I asked him and he couldn't tell either .
I: Hmm .
E: So I just cut it out .
E: You know , so I just e edited out the first , i uh , word of the utterance .
B: Yeah .
E: Um , so there 's a little bit of correction but it 's definitely not as clean as TI - digits .
E: So my expectations is TI - digits would , especially E: I think TI - digits is all American English .
E: Right ?
E: So it would probably do even a little better still
B: Mm - hmm .
E: on the SRI system ,
E: but we could give it a try .
F: Well .
F: But remember , we 're using a telephone bandwidth front - end here , uh , on this , uh on this SRI system ,
F: so , um , I was I thought that maybe that 's actually a good thing
F: because it it gets rid of some of the uh , the noises ,
F: um , you know , in the the below and above the um , the , you know , speech bandwidth
B: Mm - hmm .
B: Mm - hmm .
F: and , um ,
F: I suspect that to get sort of the last bit out of these higher - quality recordings you would have to in fact , uh , use models that , uh , were trained on wider - band data .
F: And of course we can't do that or E: Wha - what 's TI - digits ?
E: I thought t
B: It 's wide - band , yeah .
B: It 's in in fact , we looked it up
E: It is wide - band .
E: OK .
B: and it was actually twenty kilohertz sampling .
E: Oh , that 's right .
E: I I did look that up .
F: Mm - hmm .
E: I couldn't remember whether that was TI - digits or one of the other digit tasks .
B: Yeah .
F: Right .
F: But but , I would F: Yeah .
F: It 's it 's easy enough to try ,
F: just run it on E: Mm - hmm .
B: Yeah .
E: So , Morgan , you 're getting a little breath noise .
B: See w
F: Now , eh , does F: one one issue E: You might wanna move the mike down a little bit .
F: 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
F: and also in the VTL estimation .
B: Mm - hmm .
F: So E: Yeah , I noticed the script that extracted it .
F: Do y ?
F: Is ?
F: So does so th so does does , um , the TI - digits database have speakers that are known ?
E: Yep .
E: Yep .
F: And is there is there enough data or a comparable comparable amount of data to to what we have in our recordings here ?
E: That I don't know .
E: I don't know .
E: I don't know how many speakers there are ,
E: and and how many speakers per utterance .
F: OK .
B: Yeah .
B: Well , the other thing would be to do it without the adaptation and compare to these numbers without the adaptation .
B: That would F: Right . Uh , but I 'm not so much worried about the adaptation , actually , than than the , um , um the , uh , VTL estimation .
E: Right .
F: If you have only one utterance per speaker you might actually screw up on estimating the the warping , uh , factor .
F: So , um E: I strongly suspect that they have more speakers than we do .
F: Right . But it 's not the amount of speakers ,
E: So , uh F: it 's the num it 's the amount of data per speaker .
E: Right . So we we could probably do an extraction that was roughly equivalent .
F: Right .
F: Right .
E: Um .
F: So E: So , although I I sort of know how to run it , there are a little a f few details here and there that I 'll have to dig out .
F: OK .
F: The key F: So th the system actually extracts the speaker ID from the waveform names .
E: Right . I saw that .
F: And there 's a there 's a script and that is actually all in one script .
F: So there 's this one script that parses waveform names
F: and extracts things like the , um , speaker , uh , ID
F: or something that can stand in as a speaker ID .
F: So , we might have to modify that script to recognize the , um , speakers , um , in the in the , uh , um , TI - digits database .
E: Right .
E: Right .
E: And that , uh F: Or you can fake you can fake names for these waveforms that resemble the names that we use here for the for the meetings .
E: Right .
F: That would be the , sort of probably the safest way to do E: I might have to do that anyway to to do E: because we may have to do an extract to get the amount of data per speaker about right .
F: Uh - huh .
E: The other thing is , isn't TI - digits isolated digits ?
F: Right .
E: Or is that another one ?
E: I 'm I looked through a bunch of the digits t corp corpora ,
E: and now they 're all blurring .
B: Mm - hmm .
E: Cuz one of them was literally people reading a single digit .
E: And then others were connected digits .
B: Yeah . Most of TI - digits is connected digits , I think .
E: OK .
B: The I mean , we had a Bellcore corpus that we were using .
E: Maybe it 's the Bell Gram .
B: It was  that 's that was isolated digits .
E: Bell Digits .
E: Alright .
F: By the way , 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 in this setting .
B: Um .
E: Yep .
F: Because that would adapt your models to the room acoustics
F: and f for the far - field microphones , you know , to the noise .
F: And that should really improve things , um , further .
F: And then you use those adapted models , which are not speaker adapted but sort of acous you know , channel adapted E: Channel adapted .
F: use that as the starting models for your speaker adaptation .
B: Yeah . But the thing is , uh I mean , w when you it depends whether you 're ju were just using this as a  a starter task for you know , to get things going for conversational or if we 're really interested i in connected digits .
B: And I I think the answer is both .
F: Well , I don't know .
B: And for for connected digits over the telephone you don't actually want to put a whole lot of effort into adaptation
B: because somebody gets on the phone and says a number
B: and then you just want it .
B: You don't don't , uh C: This is this that one 's better .
F: Right .
C: Mm - hmm .
F: Um , but , you know , I uh , my impression was that you were actually interested in the far - field microphone , uh , problem ,
F: I mean .
F: So ,
F: you want to you want to That 's the obvious thing to try .
C: Oh .
C: Oh .
F: Right ?
F: Then , eh because you you don't have any B: Right .
C: Yeah .
F: That 's where the most m acoustic mismatch is between the currently used models and the the r the set up here .
B: Right .
F: So .
B: Yeah . So that 'd be anoth another interesting data point .
F: Mm - hmm .
B: I mean , I I guess I 'm saying I don't know if we 'd want to do that as the as E: Other way .
D: Other way .
E: Liz A: Now you 're all watching me .
E: It f it clips over your ears .
A: Alright .
A: This way .
E: There you go .
C: If you have a strong fe if you have a strong preference , you could use this .
A: You 're all watching .
A: This is terrible .
C: It 's just we we think it has some spikes .
C: So , uh , we we didn't use that one .
A: I 'll get it .
C: But you could if you want .
B: Yeah . At any rate , I don't know if w
C: I don't know .
C: And Andre - Andreas , your your microphone 's a little bit low .
F: It is ?
B: Yeah .
C: Yeah .
B: I don't know if we wanna use that as the F: Uh .
E: Uh , it pivots .
C: So if you see the picture
F: I I E: It it like this .
C: and then you have to scr
F: I I already adjusted this a number of times .
E: Eh .
F: I I
E: Yeah , I think these mikes are not working as well as I would like .
F: can't quite seem to F: Yeah , I think this contraption around your head is not working so well .
B: Too many adju too many adjustments . Yeah .
B: Anyway , what I was saying is that I I think I probably wouldn't want to see that as sort of like the norm , that we compared all things to .
C: That looks good .
C: Yeah .
B: To , uh , the to have have all this ad all this , uh , adaptation .
B: But I think it 's an important data point , if you 're if F: Right .
B: Yeah .
B: Um .
B: The other thing that that , uh of course , what Barry was looking at was was just that ,
B: the near versus far .
B: And , yeah , the adaptation would get th some of that .
F: Mm - hmm .
B: But , I think even even if there was , uh , only a factor of two or something , like I was saying in the email , I think that 's  that 's a big factor .
F: Mm - hmm .
B: So E: Liz , you could also just use the other mike if you 're having problems with that one .
B: N
C: Well .
A: OK .
C: Yeah . This would be OK .
C: We we we think that this has spikes on it ,
A: It 's this thing 's This is too big for my head .
C: so it 's not as good acoustically ,
F: Yeah , basically your ears are too big .
C: but F: I mean , mine are too .
F: E th everybody 's ears are too big for these things .
A: No , my my But this is too big for my head .
F: Uh A: So , I mean , <comment> <comment> it doesn't you know , it 's sit
C: Well , if you 'd rather have this one then it 's A: OK .
B: Yeah .
E: Oh , well .
B: It 's great .
E: So the To get that , uh , pivoted this way , it pivots like this .
A: No this way .
E: Yeah .
A: Yeah .
E: There you go .
C: And there 's a screw that you can tighten .
E: And then it E: Right .
A: Right .
A: I already tried to get it close .
C: Good .
E: So if it doesn't bounce around too much , that 's actually good placement .
C: That looks good .
A: OK .
E: But it looks like it 's gonna bounce a lot .
B: So , where were we ?
B: Uh  Yeah .
C: Yeah .
E: Digits .
E: Adaptation .
B: Uh , adaptation , non - adaptation ,
B: um , factor of two ,
B: um F: What k u By the way , wh what factor of two did you ?
B: Oh , yeah . I know what I was go w
F: I mean B: Oh , no , no .
B: It 's tha that that we were saying , you know , well is how much worse is far than near , you know .
F: Oh , th OK .
B: And I mean it depends on which one you 're looking at ,
F: That factor of two .
F: Mm - hmm .
B: but for the everybody , it 's little under a factor or two .
B: Yeah . I I know what I was thinking was that maybe , uh , i i we could actually t t try at least looking at , uh , some of the the large vocabulary speech from a far microphone ,
B: at least from the good one .
F: Mm - hmm .
B: I mean , before I thought we 'd get , you know , a hundred and fifty percent error or something ,
F: Mm - hmm .
B: but if  if , uh if we 're getting thirty - five , forty percent or something , u um A: Actually if you run , though , on a close - talking mike over the whole meeting , during all those silences , you get , like , four hundred percent word error .
B: Mm - hmm .
B: Right . I understand .
B: But doing the same kind of limited thing A: Or or some high number .
B: Yeah , sure .
B: Get all these insertions .
B: But I 'm saying if you do the same kind of limited thing as people have done in Switchboard evaluations or as a
A: Yeah .
A: Where you know who the speaker is and there 's no overlap ?
B: Yeah .
A: And you do just the far - field for those regions ?
B: Yeah . The same sort of numbers that we got those graphs from .
E: Could we do exactly the same thing that we 're doing now , but do it with a far - field mike ?
B: Right ?
B: Yeah , do it with one of on
E: Cuz we extract the times from the near - field mike ,
E: but you use the acoustics from the far - field mike .
A: Right . I understand that .
A: I just meant that so you have three choices .
A: There 's , um You can use times where that person is talking only from the transcripts but the segmentations were were synchronized .
A: Or you can do a forced alignment on the close - talking to determine that , the you know , within this segment , these really were the times that this person was talking
A: and elsewhere in the segment other people are overlapping
A: and just front - end those pieces .
A: Or you can run it on the whole data ,
A: which is which is , you know , a B: But but but how did we get the how did we determine the links , uh , that we 're testing on in the stuff we reported ?
A: In the H L T paper we took segments that are channel time - aligned ,
A: which is now h being changed in the transcription process ,
A: which is good ,
A: and we took cases where the transcribers said there was only one person talking here ,
A: because no one else had time any words in that segment
A: and called that " non - overlap " .
B: And tha
B: And that 's what we were getting those numbers from .
A: Yes .
B: Right .
A: Tho - good the good numbers .
A: The bad numbers were from the segments where there was overlap .
B: Well , we could start with the good ones .
B: But anyway so I think that we should try it once with the same conditions that were used to create those ,
A: Yeah .
B: and in those same segments just use one of the P Z
A: Right . So we we can do that .
B: And then , you know , I mean , the thing is if we were getting , uh what , thirty - five , forty percent , something like that on on that particular set ,
A: Yeah .
B: uh , does it go to seventy or eighty ?
B: Or , does it use up so much memory we can't decode it ?
A: Right .
A: It might also depend on which speaker th it is and how close they are to the PZM ?
B: Uh A: I don't know how different they are from each other .
F: You want to probably choose the PZM channel that is closest to the speaker .
A: To be best E: For this particular digit ones , I just picked that one .
D: Yeah .
A: f
B: Well A: OK .
F: Oh , OK .
A: So we would then use that one , too ,
E: So B: This is kind of central .
A: or ?
B: You know , it 's so i but I would I 'd pick that one .
B: It 'll be less good for some people than for other ,
B: but I I 'd like to see it on the same exact same data set that that we did the other thing on .
E: Actually E: I sh actually should 've picked a different one ,
B: Right ?
E: because that could be why the PDA is worse .
E: Because it 's further away from most of the people reading digits .
D: It 's further away . Yeah .
D: Yeah .
B: That 's probably one of the reasons .
C: Hmm .
C: Mm - hmm .
A: Well , yeah . You could look at , I guess , that PZM or something .
E: Yep .
B: But the other is , it 's very , uh I mean , even though there 's I 'm sure the f f the the SRI , uh , front - end has some kind of pre - emphasis , it 's it 's , uh  still , th it 's picking up lots of low - frequency energy .
F: Mm - hmm .
B: So , even discriminating against it , I 'm sure some of it 's getting through .
B: Um .
B: But , yeah , you 're right .
B: Prob - a part of it is just the distance .
A: And aren't these pretty bad microphones ?
E: Yep .
A: I mean B: Well , they 're bad .
B: But , I mean , if you listen to it , it sounds OK . You know ?
E: Yeah . When you listen to it , uh , the PZM and the PDA Yeah , th the PDA has higher sound floor
B: u Yeah .
E: but not by a lot .
E: It 's really pretty uh , pretty much the same .
A: I just remember you saying you got them to be cheap on purpose .
A: Cheap in terms of their quality .
A: So .
B: Well , they 're twenty - five cents or so .
E: Th - we wanted them to be to be typical of what would be in a PDA .
B: Yeah .
E: So they are they 're not the PZM three hundred dollar type .
A: Mm - hmm .
E: They 're the twenty - five cent ,
B: Yeah .
E: buy them in packs of thousand type .
B: But , I mean , the thing is people use those little mikes for everything
A: I see .
E: Everything .
B: because they 're really not bad .
B: I mean , if you 're not doing something ridiculous like feeding it to a speech recognizer , they they  they you know , you can hear the sou hear the sounds just fine .
A: Mm - hmm .
A: Right .
B: You know , it 's B: They I mean , i it 's more or less the same principles as these other mikes are built under ,
B: it 's just that there 's less quality control .
B: They just , you know , churn them out and don't check them .
B: Um . So .
B: So that was Yeah . So that was i interesting result .
B: So like I said , the front - end guys are very much interested in in this is as as well
B: and
F: So so , but where is this now ?
F: I mean , what 's where do we go from here ?
F: I mean ,
E: Yeah . That was gonna be my question .
F: we so we have a we have a a system that works pretty well
F: but it 's not , you know , the system that people here are used to using to working with .
F: So what what do we do now ?
B: Well , I think what we wanna do is we want to eh ,
B: and we 've talked about this in other contexts B: we want to have the ability to feed it different features .
F: Mm - hmm .
B: And then , um , from the point of view of the front - end research , it would be s uh , substituting for HTK .
F: OK .
F: OK .
B: I think that 's the key thing .
B: And then if we can feed it different features , then we can try all the different things that we 're trying there .
F: OK . Alright .
B: And then , um , uh , also Dave is is thinking about using the data in different ways , uh , to um , uh , explicitly work on reverberation
F: Mm - hmm .
B: starting with some techniques that some other people have found somewhat useful , and Yeah .
F: OK .
F: So so the key thing that 's missing here is basically the ability to feed , you know , other features i into the recognizer
F: and also then to train the system .
B: Right .
B: Right .
F: OK .
F: And , uh , es I don't know when Chuck will be back
F: but that 's exactly what he he 's gonna