Grad E: OK , we 're on .
Professor B: OK .
Grad E: So , I mean , everyone who 's on the wireless check that they 're on .
PhD F: C we Grad G: Alright .
Postdoc C: I see . Yeah .
PhD F: Yeah .
Grad E: OK , our agenda was quite short .
Professor B: Oh , could you close the door , maybe ? Yeah .
Grad E: Sure . Two items , which was , uh , digits and possibly stuff on on , uh , forced alignment , which Jane said that Liz and Andreas had in information on ,
Professor B :
Grad E: but they didn't ,
PhD F: Mm - hmm .
Professor B: I guess the only other thing , uh , for which I Grad E: so .
PhD F: We should do that second , because Liz might join us in time for that .
Grad E: OK .
Professor B: Um . OK , so there 's digits , alignments , and , um , I guess the other thing , which I came unprepared for , uh , is , uh , to dis s s see if there 's anything anybody wants to discuss about the Saturday meeting .
Grad E: Right .
Professor B: So . Any I mean , maybe not .
Grad E: Digits and alignments . But Professor B: Uh .
PhD F: Talk about aligning people 's schedules .
Professor B: Yeah .
Grad E: Yeah .
Postdoc C: Mm - hmm .
Professor B: Yeah . I mean Right . Yeah , I mean , it was Grad E: Yeah , it 's forced alignment of people 's schedules .
PhD F: Yeah .
PhD D: Forced align .
PhD F: If we 're very Professor B: Yeah .
PhD F: Yeah .
Professor B: With with whatever it was , a month and a half or something ahead of time , the only time we could find in common roughly in common , was on a Saturday .
PhD D: Yeah .
Professor B: Ugh .
Grad E: Yep .
PhD F: It 's pretty sad .
Professor B: Yeah .
PhD F: Yeah .
Postdoc C: Have Have we thought about having a conference call to include him in more of  in more of the meeting ? I I mean , I don't know , if we had the if we had the telephone on the table Professor B: No . But , h I mean , he probably has to go do something .
PhD F: No , actually I I have to I have to shuttle kids from various places to various other places .
Professor B: Right ?
Postdoc C: I see . OK .
Professor B: Yeah .
PhD F: So . And I don't have and I don't , um , have a cell phone
PhD D: A cell phone ?
PhD F: so I can't be having a conference call while driving .
Professor B: R r right .
Postdoc C: No . {comment} It 's not good .
Professor B: So we have to we Postdoc C: That 's not good .
PhD F: Plus , it would make for interesting noise background noise .
Professor B :
Grad E: Yep .
PhD F: Uh Professor B: So we have to equip him with a with a  with a head - mounted , uh , cell phone
Grad E: Ye - we and we 'd have to force you to read lots and lots of digits ,
Professor B: and Grad E: so it could get real  real car noise .
PhD F: Oh , yeah .
PhD D: Yeah .
PhD F: Oh , yeah .
Grad G: Take advantage .
PhD D: And with the kids in the background .
PhD F: I 'll let I 'd let PhD D: Yeah .
PhD F: I let , uh , my five - year - old have a try at the digits , eh .
Professor B: Yeah .
Grad E: So , anyway , I can talk about digits . Um , did everyone get the results or shall I go over them again ? I mean that it was basically the only thing that was even slightly surprising was that the lapel did so well . Um , and in retrospect that 's not as surprising as maybe i it shouldn't have been as surprising as I as as I felt it was . The lapel mike is a very high - quality microphone . And as Morgan pointed out , that there are actually some advantages to it in terms of breath noises and clothes rustling if no one else is talking .
PhD D: Yeah .
PhD F: Exactly .
Grad E: Um , so , uh Grad G: Mm - hmm .
Professor B: Well , it 's Yeah , sort of the bre the breath noises and the mouth clicks and so forth like that , the lapel 's gonna be better on .
Grad G: It 's g it PhD D: Or the cross - talk . Yeah .
Professor B: The lapel is typically worse on the on clothes rustling , but if no one 's rustling their clothes ,
Grad E: Right . I mean , a lot of people are just sort of leaning over and reading the digits ,
Professor B: it 's it 's Grad E: so it 's it 's a very different task than sort of the natural .
PhD D: Yeah . You don't move much during reading digits , I think .
Professor B: Yeah .
Grad E: So .
Professor B: Yeah .
Grad E: Right .
Grad G: Probably the fact that it picks up other people 's speakers other people 's talking is an indication of that it the fact it is a good microphone .
PhD D: Yeah .
Professor B: Right . So in the digits , in most most cases , there weren't other people talking .
Grad E: Right . Right .
Grad G: So .
Professor B: So .
PhD F: D do the lapel mikes have any directionality to them ?
Professor B: There typically don't , no .
PhD F: Because I I suppose you could make some that have sort of that you have to orient towards your mouth ,
Grad E: They have a little bit ,
PhD F: and then it would Grad E: but they 're not noise - cancelling . So , uh Professor B: They 're they 're intended to be omni - directional .
Grad E: Right .
Professor B: And th it 's and because you don't know how people are gonna put them on , you know .
PhD F: Mm - hmm .
Grad E: Right . So , also , Andreas , on that one the the back part of it should be right against your head . And that will he keep it from flopping aro up and down as much .
PhD F: It is against my head .
Grad E: OK .
Professor B: Yeah . Um . Yeah , we actually talked about this in the , uh , front - end meeting this morning , too . Much the same thing ,
Grad E: Uh - huh .
Professor B: and and it was uh , I mean , there the point of interest to the group was primarily that , um , the , uh the system that we had that was based on H T K , that 's used by , you know , all the participants in Aurora , was so much worse than the than the S R
Grad E: Everybody .
Professor B: And the interesting thing is that even though , yes , it 's a digits task and that 's a relatively small number of words and there 's a bunch of digits that you train on , it 's just not as good as having a a l very large amount of data and training up a a a nice good big HMM . Um , also you had the adaptation in the SRI system , which we didn't have in this . Um . So . Um .
PhD F: And we know Di - did I send you some results without adaptation ?
Grad E: No .
Professor B: I s I think Stephane , uh , had seen them .
Grad E: Or if you did , I didn't include them , cuz it was Professor B: So PhD F: Yeah , I think I did , actually . So there was a significant loss from not doing the adaptation .
Professor B: Yeah .
PhD F: Um . A a a couple percent or some I mean Well , I don't know it Overall Uh , I I don't remember , but there was {nonvocalsound} there was a significant , um , loss or win {comment} from adaptation with with adaptation . And , um , that was the phone - loop adaptation . And then there was a very small like point one percent on the natives uh , win from doing , um , you know , adaptation to the recognition hypotheses . And I tried both means adaptation and means and variances , and the variances added another or subtracted another point one percent . So , it 's , um that 's the number there . Point six , I believe , is what you get with both , uh , means and variance adaptation .
Grad E: Right .
Professor B: But I think one thing is that , uh , I would presume Hav - Have you ever t Have you ever tried this exact same recognizer out on the actual TI - digits test set ?
PhD F: This exact same recognizer ? No .
Professor B: It might be interesting to do that . Cuz my my cuz my sense , um PhD F: But but , I have I mean , people people at SRI are actually working on digits .
Grad E: I bet it would do even slightly better .
PhD F: I could and they are using a system that 's , um you know , h is actually trained on digits , um , but h h otherwise uses the same , you know , decoder , the same , uh , training methods , and so forth ,
Professor B: Mm - hmm .
PhD F: and I could ask them what they get on TI - digits .
Professor B: Yeah , bu although I 'd be I think it 'd be interesting to just take this exact actual system so that these numbers were comparable
PhD F: Mm - hmm .
Professor B: and try it out on TI - digits .
PhD F: Well , Adam knows how to run it ,
Professor B: Yeah .
Grad E: Yeah . No problem .
PhD F: so you just make a f
Professor B: Yeah . Yeah . Cuz our sense from the other from the Aurora , uh , task is that Grad E: And try it with TI - digits ?
PhD F: Mm - hmm .
Professor B: I mean , cuz we were getting sub one percent numbers on TI - digits also with the tandem thing .
PhD F: Mm - hmm .
Professor B: So , one so there were a number of things we noted from this .
PhD F: Mmm .
Professor B: One is , yeah , the SRI system is a lot better than the HTK PhD F: Hmm .
Professor B: this , you know , very limited training HTK system .
PhD F: Mm - hmm .
Professor B: 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 studio - recording TI - digits . 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 .
PhD F: Mm - hmm . Mm - hmm .
Professor 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 did something a little funny or n pronounced something a little funny or made a little that they didn't include it ,
Grad E: They didn't include it .
Professor B: they made them do it again .
Grad E: Whereas , I took out the ones that I noticed that were blatant that were correctable .
Professor B: Mmm . Yeah .
Grad E: So that , if someone just read the wrong digit , I corrected it .
Professor B: Yeah .
Grad E: And then there was 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 .
Grad I: Hmm .
Grad E: So I just cut it out .
Professor B: Yeah .
Grad E: You know , so I just e edited out the first , i uh , word of the utterance . Um , so there 's a little bit of correction but it 's definitely not as clean as TI - digits . So my expectations is TI - digits would , especially I think TI - digits is all American English .
Professor B: Mm - hmm .
Grad E: Right ? So it would probably do even a little better still on the SRI system , but we could give it a try .
PhD F: Well . But remember , we 're using a telephone bandwidth front - end here , uh , on this , uh on this SRI system , so , um , I was I thought that maybe that 's 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
Professor B: Mm - hmm . Mm - hmm .
PhD F: and , um , 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 . And of course we can't do that or Grad E: Wha - what 's TI - digits ? I thought t
Professor B: It 's wide - band , yeah . It 's in in fact , we looked it up
Grad E: It is wide - band . OK .
Professor B: and it was actually twenty kilohertz sampling .
Grad E: Oh , that 's right . I I did look that up .
PhD F: Mm - hmm .
Grad E: I couldn't remember whether that was TI - digits or one of the other digit tasks .
Professor B: Yeah .
PhD F: Right . But but , I would Yeah . It 's it 's easy enough to try , just run it on Professor B: Yeah .
Grad E: Mm - hmm .
Professor B: See w
Grad E: So , Morgan , you 're getting a little breath noise .
PhD F: Now , eh , does Grad E: You might wanna move the mike down a little bit .
PhD F: one one issue 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 .
Professor B: Mm - hmm .
PhD F: So Grad E: Yeah , I noticed the script that extracted it .
PhD F: Do y ? Is ? So does so th so does does , um , the TI - digits database have speakers that are known ?
Grad E: Yep . Yep .
PhD F: And is there is there enough data or a comparable comparable amount of data to to what we have in our recordings here ?
Grad E: That I don't know . I don't know . I don't know how many speakers there are ,
Professor B: Yeah .
Grad E: and and how many speakers per utterance .
PhD F: OK .
Professor B: Well , the other thing would be to do it without the adaptation and compare to these numbers without the adaptation . That would PhD F: Right . Uh , but I 'm not so much worried about the adaptation , actually , than than the , um , um the , uh , VTL estimation .
Grad E: Right .
PhD F: If you have only one utterance per speaker you might actually screw up on estimating the the warping , uh , factor . So , um Grad E: I strongly suspect that they have more speakers than we do . So , uh PhD F: Right . But it 's not the amount of speakers , it 's the num it 's the amount of data per speaker .
Grad E: Right . So we we could probably do an extraction that was roughly equivalent .
PhD F: Right . Right .
Grad E: Um .
PhD F: So Grad 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 .
PhD F: OK . The key So th the system actually extracts the speaker ID from the waveform names .
Grad E: Right . I saw that .
PhD F: And there 's a there 's a script and that is actually all in one script . So there 's 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 , um , speakers , um , in the in the , uh , um , TI - digits database .
Grad E: Right . Right . And that , uh PhD 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 .
Grad E: Right .
PhD F: That would be the , sort of probably the safest way to do Grad E: I might have to do that anyway to to do because we may have to do an extract to get the amount of data per speaker about right .
PhD F: Uh - huh .
Grad E: The other thing is , isn't TI - digits isolated digits ?
PhD F: Right .
Grad E: Or is that another one ? I 'm I looked through a bunch of the digits t corp corpora , and now they 're all blurring .
Professor B: Mm - hmm .
Grad E: Cuz one of them was literally people reading a single digit . And then others were connected digits .
Professor B: Yeah . Most of TI - digits is connected digits , I think .
Grad E: OK .
Professor B: The I mean , we had a Bellcore corpus that we were using . It was  that 's that was isolated digits .
Grad E: Maybe it 's the Bell Gram . Bell Digits . Alright .
Professor B: Um .
PhD 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 .
Grad E: Yep .
PhD F: Because that would adapt your models to the room acoustics and f for the far - field 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 you know , channel adapted Grad E: Channel adapted .
PhD F: use that as the starting models for your speaker adaptation .
Professor 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 . And I I think the answer is both . And for for connected digits over the telephone you don't actually want to put a whole lot of effort into adaptation
PhD F: Well , I don't know .
Professor B: because somebody gets on the phone and says a number and then you just want it . You don't don't , uh Postdoc C: This is this that one 's better .
PhD F: Right .
Postdoc C: Mm - hmm .
PhD F: Um , but , you know , I uh , my impression was that you were actually interested in the far - field microphone , uh , problem , I mean . So , you want to you want to That 's the obvious thing to try .
Postdoc C: Oh . Oh .
Professor B: Right .
PhD F: Right ? Then , eh because you you don't have any Postdoc C: Yeah .
PhD F: That 's where the most m acoustic mismatch is between the currently used models and the the r the set up here .
Professor B: Right .
PhD F: So .
Professor B: Yeah . So that 'd be anoth another interesting data point .
PhD F: Mm - hmm .
Professor B: I mean , I I guess I 'm saying I don't know if we 'd want to do that as the as PhD D: Other way .
Grad E: Other way . Liz PhD A: Now you 're all watching me .
Grad E: It f it clips over your ears .
PhD A: Alright . This way .
Grad E: There you go .
Postdoc C: If you have a strong fe if you have a strong preference , you could use this .
PhD A: You 're all watching . This is terrible .
Postdoc C: It 's just we we think it has some spikes . So , uh , we we didn't use that one .
PhD A: I 'll get it .
Postdoc C: But you could if you want .
Professor B: Yeah . At any rate , I don't know if w
Postdoc C: I don't know . And Andre - Andreas , your your microphone 's a little bit low .
Professor B: Yeah .
PhD F: It is ?
Professor B: I don't know if we wanna use that as the Postdoc C: Yeah .
Grad E: Uh , it pivots .
PhD F: Uh .
Postdoc C: So if you see the picture
Grad E: It it like this .
PhD F: I I Postdoc C: and then you have to scr
PhD F: I I already adjusted this a number of times .
Grad E: Eh .
PhD F: I I
Grad E: Yeah , I think these mikes are not working as well as I would like .
PhD F: can't quite seem to Yeah , I think this contraption around your head is not working so well .
Professor B: Too many adju too many adjustments . Yeah . 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 .
Postdoc C: That looks good . Yeah .
Professor B: To , uh , the to have have all this ad all this , uh , adaptation . But I think it 's an important data point , if you 're if Yeah .
PhD F: Right .
Professor B: Um . The other thing that that , uh of course , what Barry was looking at was was just that , the near versus far . And , yeah , the adaptation would get th some of that .
PhD F: Mm - hmm .
Professor 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 . So PhD F: Mm - hmm .
Professor B: N
Grad E: Liz , you could also just use the other mike if you 're having problems with that one .
Postdoc C: Well .
PhD A: OK .
Postdoc C: Yeah . This would be OK . We we we think that this has spikes on it ,
PhD A: It 's this thing 's This is too big for my head .
Postdoc C: so it 's not as good acoustically ,
PhD F: Yeah , basically your ears are too big .
Postdoc C: but PhD F: I mean , mine are too . E th everybody 's ears are too big for these things .
PhD A: No , my my But this is too big for my head . So , I mean , {comment} {comment} it doesn't you know , it 's sit
PhD F: Uh Postdoc C: Well , if you 'd rather have this one then it 's PhD A: OK .
Professor B: Yeah .
Grad E: Oh , well .
Professor B: It 's great .
Grad E: So the To get that , uh , pivoted this way , it pivots like this .
PhD A: No this way . Yeah .
Grad E: Yeah . There you go .
Postdoc C: And there 's a screw that you can tighten .
Grad E: And then it PhD A: Right .
Grad E: Right .
PhD A: I already tried to get it close .
Postdoc C: Good .
Grad E: So if it doesn't bounce around too much , that 's actually good placement .
PhD A: OK .
Postdoc C: That looks good .
Grad E: But it looks like it 's gonna bounce a lot .
Professor B: So , where were we ? Uh  Yeah .
Postdoc C: Yeah .
Grad E: Digits . Adaptation .
Professor B: Uh , adaptation , non - adaptation , um , factor of two , um Oh , yeah . I know what I was go w
PhD F: What k u By the way , wh what factor of two did you ?
Professor B: Oh , no , no .
PhD F: I mean Professor B: It 's tha that that we were saying , you know , well is how much worse is far than near , you know .
PhD F: Oh , th OK .
Professor B: And I mean it depends on which one you 're looking at ,
PhD F: That factor of two .
Professor B: but for the everybody , it 's little under a factor or two .
PhD F: Mm - hmm .
Professor 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 , at least from the good one .
PhD F: Mm - hmm .
Professor B: I mean , before I thought we 'd get , you know , a hundred and fifty percent error or something , but if  if , uh if we 're getting thirty - five , forty percent or something , u um PhD F: Mm - hmm .
PhD 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 .
Professor B: Mm - hmm . Right . I understand . But doing the same kind of limited thing PhD A: Or or some high number .
Professor B: Yeah , sure . Get all these insertions . But I 'm saying if you do the same kind of limited thing as people have done in Switchboard evaluations or as a
PhD A: Yeah . Where you know who the speaker is and there 's no overlap ? And you do just the far - field for those regions ?