A: OK .
C: OK . So the one   one thing I knew I wanted to talk about was about , uh , sort of last minute stuff to to , uh , try to get some recognition results .
F: Recognition results
C: Yeah .
F: for C: So , uh , on on on meeting data .
C: And so , I 'm I 'm not sure exactly what you 're doing already ,
C: and and there 's some stuff I 've talked to Dave F: Well , we I just started recognition on the on Thilo 's segments ,
F: which was F: but using the far far
F: the close - talking microphone .
C: OK . So Um F: And you wanted I know you wanted the far - field data .
C: Right . So so , we have some stuff with no overlap , uh , for which there would be near near - field results .
F: Uh - huh .
C: We wanted to get the far - field results for that .
C: And then this this real , uh , long shot thing would be , that we 'd apply Dave 's processing to , uh , potentially training and test data
F: Mm - hmm .
C: and do the look at the same thing .
C: And in talking this morning with , uh , Chuck and with Dave one thought was to use C: We couldn't remember how different the numbers were ,
C: but if you just worked with males only and used the short training there 're there 're C: uh , I think Chuck 's recollection was that when he was doing the feature stuff , it took maybe a day and a half
C: to do the training .
F: To retrain ?
C: Yeah .
F: Uh
F: Yeah .
F: Um .
F: That 's about right .
F: Actually , it should probably be F: It depends on who else is using machines ,
F: but we have more machines now .
F: So .
A: That 's true .
F: It 's more like a day , probably .
C: Um , how much worse is the short training set than the large one , in terms of the ultimate performance ?
F: Mmm , it 's like Something like three three percent F: three or four percent absolute .
C: Yep .
C: So , it 's that should be fine for this , I would think .
C: So we an and you have the short you have short training results for the close case ?
F: Um , not for meetings .
F: Because we didn't train we didn't re ever recognize with the with the small models on meeting data .
F: But I I have the models ,
F: so I could run reco
C: So , how do you know it 's ?
C: Oh , it 's three percent on on , uh , on Hub - five .
F: On A: Hub - five .
C: I see .
C: Yeah . But we have the models so we could get that number ,
C: and C: So the question is , what ? w
F: I mean , the the mmm
F: The recognition also takes non - negligible amount of time .
F: So we might wanna restrict it to , maybe , a few meetings ,
F: if you want to do a full comparison .
C: It has to be enough so that F: Uh .
C: I mean , it 's the non - overlap only .
C: Um <comment> And C: it has to be enough to be sort of comparable to what you folks were seeing and what you reported already .
F: Hmm .
C: I mean F: Well , do we have the do we have the processed data ?
F: That that 's also C: No . He has to create that .
C: But but s but so F: Right .
C: Um C: We have a whole parallel set of things over here which are all with digits .
F: Mmm .
F: I see .
C: And and and Dave has been working with that and there 's all of those issues .
C: But  I know that if I go in with something that 's not just digits it would be good .
C: And , um , so C: We already have these results that you C: I mean , on uh , a l a lot tha tha a particular test set , that you that we reported at HLT .
F: Right .
C: Um , it 'd be nice to have something more than that .
C: And w we had talked about was having distant C: Um , and then ,
C: uh , if we could on top of that C: I mean , so this is gonna be a lot worse .
C: Right ?
C: Whatever comparison we w w one would presume .
F: Mm - hmm .
C: But we don't know how much worse ,
F: Right .
C: w w uh , which is certainly one interesting thing .
C: And then , um ,
C: Dave C: I think we figured that it 'd probably take a day or two to compute the the ,
C: uh uh C: Well How many hours of training ?
F: Actually , I did retrain F: I recently retrained , um , for another reason , on the full training set .
F: And that took only I think it took only two days .
C: Yeah .
F: So , it 's actually conceivable to do use the full training set .
C: Yeah , but we also have to do this other processing , so having a smaller training set ,
C: if it 's only a few percent difference , it might be be worth doing it .
F: Oh , I see .
C: But - m How big is the small training set ?
C: Do you remember ?
F: Hmm . <comment> <comment> Uh .
F: I don't know ,
F: something something like something between <comment> thirty and fifty hours , maybe .
F: I f I forget the exac
C: It 's around there .
C: And ha and and male is roughly half of that ,
F: Right .
C: or ?
A: Well , that 's only male .
C: Or or or was that only male ?
F: Uh . Actually , I don't know .
F: I 'd  I I can look it up .
F: It 's it 's it 's just ,
F: uh , I don't know the remember the the number .
A: We could only jus just do the male only
C: OK .
A: right ?
A: Or will we run into trouble when we g ?
F: Well , the males account for most of this meeting data anyhow .
A: Yeah .
F: So F: Yeah . I would say we you do only males .
C: Yeah .
C: So C: Yeah . So that 's certainly part of the issue , is that right now he 's he hasn't written his stuff for efficiency .
C: Yeah . It 's it 's in Matlab
C: and so on ,
C: and  And , uh , it 's not an impossible amount of time .
F: Mm - hmm .
C: We we were guestimating it was like one and a half times faster than real time , or something ?
C: So , if there 's thirty hours of data , you can calculate that he can do , uh , the enhancement in a day
F: u
C: and something .
C: So C: But if we were dealing with two hundred hours or something , I think it 'd be prohibitive .
F: No . It 's definitely It 's less than a hundred hours , for sure .
F: It 's C: Yeah .
F: It 's probably actually , uh It 's uh , I think it 's around thirty hours
F: just for for one gender .
A: That 's what I was thinking .
C: Yeah .
F: Yeah .
A: Yeah .
C: Yeah . So , I mean , it 's a bit of a push ,
C: but it seems like ,
C: OK , we 've got some models ,
C: we 've got some training data , we have software that works , he 's got a method that helps with , you know , other ta another task .
C: Um C: It , you know , appears to be , you know , debugged .
A: S
F: Mm - hmm .
C: Um A: So We - Yo - So , one thing I was wondering is , did you already do that middle one or should we re - do that one , too ?
F: No . I didn't do that ,
F: because we haven't even cut the waveforms for that .
A: You didn't do that . OK .
A: Yeah . That 's what I was gonna say next .
F: So A: We hafta cut the , uh F: Right .
F: So .
A: W s so is Morgan , is the plan to just pick one of the far - field mikes ?
E: Yeah .     Yeah . We did .
A: Uh F: And there 's a bit of a question whether you want to use um , what segmentations you want to use .
C: Yes .
F: Uh F: Uh , David just F: Um , I 'm sorry .
F: Don just , uh , created a new version of the first meetings that we had previously recognized ,
F: but with different segmentation .
F: And so   Um F: It would be nice F: I mean , if the results are comparable to what we had before F: to use those segmentations ,
F: because th then we could claim that everything 's automatic .
A: Do you know when he 'll have the the comparison ?
F: Right .
F: Well , I 'm as I said , I just started the recognizer , um F: It will  uh , it will probably be a couple hours before before I have some results .
C: Oh . Well , that 's not bad .
A: Oh , OK .
F: So A: So cuz I don't think the new data will be ready , uh , for a couple days may probably .
C: You mean the training .
A: So ,
A: the training .
F: But the segmentations matter for the filtering .
C: Yeah .
F: Right ?
F: Because C: For the test .
F: For the test set . Yeah .
F: So ,
C: Yeah , fo
A: Yeah .
F: need to be F: But , first first , of course , you would wanna process the training data ,
F: because we wanna get that started .
C: Right .
F: Yeah .
C: Yeah .
C: I mean , it it 'd be really great if it was all automatic , but I think that , you know , given the pressure of time , if i i
C: I mean , since you 're gonna find out in a short amount of time , that 's great .
F: Mm - hmm .
C: But i if if it doesn't work out , I think we would rather charge ahead with the older segmentations
F: Mm - hmm .
F: Right .
C: and Um ,
C: and we were gonna use one of the P Z
C: I don I don't know what C: Probably whatever one you 've been using for for for the digits .
A: I think it 's that one .
A: Right ? F .
C: Is it this one ?
B: It 's e F .
A: F .
A: Yeah , that 's it .
C: I I 'd which ?
C: That 's F ?
C: How do you know ?
A: That 's F .
A: It 's the second nearest the machine room .
C: Oh .
C: Bien sur
C: OK . Alright , so C: Bet they 'll have fun with that one .
C: OK , so .
C: Uh A: OK . So , uh , I just want to make sure I understand what we need to run .
A: Um A: Let 's see .
A: So , it 's A: OK , so if we 're talking about A: Let 's let 's assume that we 're gonna use the new segmentations .
A: We need to , um , run recognition o just looking at the no overlap column .
A: Basically , we have to d r do recognitions for all three of those cases .
A: Right ?
C: Mm - hmm .
C: Right .
A: Um , because we 're gonna be using the just the male , uh , model A: short training set for the male .
F: Mm - hmm .
A: So we need to have results for all three of those ,
A: even though we have F: Ma - maybe you should limit ourselves to the Meeting Recorder meetings ?
A: OK .
F: Um ,
F: if you were gonna cut down on the test set , I would suggest that .
C: Well Maybe .
C: But , I I mean , how long does it take for the test ?
F: Actually , the longer the Robustness meetings take longer ,
F: because there 's this one speaker who talks a lot .
F: And so
F: the  Um .
F: No . It 's because for all the for the adaptation and normalization steps , you cannot F: you have to d you have to , uh , um ,
F: you cannot chop it up into small pieces .
F: So , you 're sort of limited by how long the longest speaker , uh , is s speaking .
F: So how much data there is from the the speaker who talks the most .
F: So , um , you parallelize across different speakers ,
A: Mm - hmm .
F: but you know , if you have a bunch of speakers who speak very little and then one wh who who speaks a lot , then effectively , everybody waits for the longest one to process .
C: Right .
A: Right .
A: Yeah .
C: Bu - But what what was your result for uh , that we had at the HLT ?
F: So .
C: Was that a combination of me ?
F: That was both types of meetings ,
F: but most but there were only two Robustness meetings , and four or five , uh , Meeting Recorders .
A: And if we 're re - doing the baseline anyways , it it it would be OK .
F: Right .
A: Right ? I mean To to just limit ourselves to a smaller A: How long would it take to run recognition , if we did that ?
F: Oh .
F: Uh , I I F: I don't have I don't have a good gue
A: I mean , is is it like a day or is it a few hours or A: roughly ?
F: For everything ?
F: For all the meetings ?
A: For A: yeah , let 's say we just did the Meeting Recorder meetings for our test set .
F: Uh .
F: Um .
F: It 's probably more than a day ,
F: but probably less than two .
A: Oh , really .
A: I didn't realize each test took that long .
F: Well , i No . I mean for all the meetings .
F: Because it 's  Again , it 's ,
F: um C: So you were doing like F: So each meeting each meetings takes , uh , something like F: Again , we we I ran when we ran these , we were sort of short on machines ,
F: and , um ,
F: I don't know ,
F: I I would estimate maybe four hours per meeting .
F: Something like that .
A: Four hours per meeting .
F: Right .
A: Wow .
C: Yeah . But if you So , if you do half a dozen meetings , that 's that 's about a day .
C: We also have more machines now .
F: Right . Right . So that 's why I 'm saying I 'm not sure how they would scale with more machines .
C: well
C: Yeah .
C: Yeah .
C: I mean , if we had about six A six hour test set 's not bad .
C: Right ?
F: Right .
A: S six hour test set .
A: Six meetings . OK .
C: You know ?
C: Right ?
C: I mean , a lot of the evaluations have been F: We have MR F: two , th We have two , three , four , fi
F: I think there are four Meeting Recorder meetings that we worked with .
C: Four that you worked with ?
E: I think it 's E: Is it the same set as the alignments ?
F: Yeah .
E: I think it 's five Meeting Recorder meetings .
F: Five ?
F: OK .
C: That would be OK , too .
C: I mean , I 'm i
C: So , if they have a set that they worked with ,
C: and you you got C: Did you do similarly in performance between them and the other meetings ,
C: or was it ?
F: Uh , with the Robust - compared to Robustness ?
F: Yeah .
F: The big variation is by whether it 's a native speaker or not .
C: Yeah .
F: And whether it 's , um F: Uh , I think that 's the o actually F: and and of course what , um , you know , whether it 's lapel or , uh , headset microphone .
C: And overlap or not .
F: Yeah .
C: Yeah .
C: So maybe just with the the the Meeting Recorder set of the De - th that you did before .
F: And we can exclude we don't need to recognize the non - natives ,
F: because we know that F: I mean , in fact , we excluded them previously from C: Yeah . So we want to do the same same thing .
F: Right .
A: OK . So Alright . So if we got a list of the , uh , segmentations for these five Meeting Recorder meetings , we could start , uh , the first two experiments going right away , using the short male models .
F: Mm - hmm .
A: So , you could get those going while Dave is , um , creating waveforms for the r retraining the short male models .
C: Yeah . Once we know which segmentations we 're using .
C: Yeah .
A: Right . OK . OK .
A: And then A: OK . So , do we w also want to run that bottom experiment without retraining the short male models on his thing ?
A: Did you want that ?
A: Or ?
C: Um , I I agree that that would be an interesting thing to do ,
C: but I sort of regard it as secondary .
A: OK . So , we 'll save that .
C: So if there 's sort of machines sitting around and people sitting around and they 're waiting for other things to finish , then sure .
C: But C: Uh , Chuck had been asking about that earlier as kind of a control to know , um C: Cuz , I mean , you could imagine a fantasy in which you said that Dave 's processing made the , uh , far microphone like the near microphone .
C: In which case you shouldn't have to actually retrain .
F: Mm - hmm .
C: But it 's it 's not really true .
C: It 's it 's sort of fantasy .
C: It does it does muck up the data in in some funny ways .
F: Mm - hmm .
C: And so ,
C: I 'm I 'm kind of questioning that .
A: Well , i it C: But But A: Well , on a more basic level , also , it means that that third experiment , there are actually two differences between the other experiments ,
A: not one .
C: Right .
A: So , it 's hard to know C: It involves retraining and it involves a A: Right .
C: Uh , that 's right .
C: I mean the other thing which which it might come in to is if there was some problem in the retraining .
C: I mean , maybe you 'd just have some mechanical thing we do wrong .
A: Mm - hmm .
C: Uh , that , uh since Dave 's experience was that it didn't help as much if you didn't retrain , but it does help some , that we would hopefully see that .
A: Right .
A: Mm - hmm .
C: So , that that 's that 's true .
F: Wait , did ?
C: I i
F: So when you used original the original models , and you just process the test set in this way , d do you get any do you get decent performance or not ?
B: u I I I think , um , for the far - mike HTK system I was using , it did help somewhat .
F: Mm - hmm .
B: I could re - check that .
B: But it was such a bad baseline that I don't know what that means .
F: Mmm . Right .
F: Right .
F: OK .
B: Cuz the baseline word error rate was around forty percent on digits .
F: Mm - hmm .
A: On the far - field ?
B: Right .
C: Right . So F: OK . Well , I 'll I can get started on the F: well , the first the one that already has a
F: cross there .
F: We need to re - do that with small models .
F: Right .
C: Yeah .
F: And then , have to ask , um , I guess , Don to , uh , cut the , um , cut the segments for the sh for the tis distant mike .
F: Uh , uh that 's  So we would be using the same channel for each fo for everything ?
C: Yeah .
A: Mm - hmm .
F: OK .
F: So .
C: I mean , do you ha do you have to rely on his segmentations at all to do the top one ?
F: No , no . We would use the same segmentations ,
F: but he needs to extract extract the wavef form segments from a different channel .
C: Oh . OK .
C: Got it .
F: Right .
A: So when you said you were gonna start that top one , were you gonna use the new segmentations ?
F: Mm - hmm .
F: Um Yeah .
A: OK .
F: If assuming that the performance turns out to be comparable with with the old experiments and the old segmentations .
A: Right . OK .
F: Now there 's the issue of F: Oh , OK . So there 's the issue of speaker normalization .
F: So , with the distant microphone you wouldn't know which speaker is talking .
F: Right ?
A: Can we ?
C: We we talked about this before .
C: I think what we were saying was that , um , the very fact that in both cases we 're ignoring the overlap section means that , um , uh , we 're to some extent finessing that .
C: So , um ,
C: I think , for the purposes of just determining whether a far - field microphone uh , what the effect of the far - field microphone is , we should do the same to both .
F: Mm - hmm .
F: I see . So you want to cheat ?
C: I mean C: We want to i incorporate <comment> certain data that would not be available during final tests , <comment> uh , under a a full fair test of it , much as we are in the all the numbers that we have so far .
F: OK . So we assume we assume knowledge of the speakers as as , um  in a way that 's compatible with the close - talking test set .
E: Oops .
C: Yeah .
C: We we simply wanna determine what 's the difference in performance due to being distant versus close .
F: OK .
F: Oh , OK .
A: So , does that mean you turn off speaker normalization when you run it ? Or you just let it do what it would do , an ?
F: No . It means No . It just means
F: you you group together the segments that by magic you know belong to one speaker ,
F: and and treat C: I mean to a lesser extent you had that same magic the other way , too ,
C: because you have leakage into other microphones .
C: Right ?
C: But , it 's just you 're using the fact that this is where this person is .
F: Right .
F: Right .
C: Right ?
C: So .
F: But , um C: It 's just easier to do .
F: Well , in the new test , actually , that 's not true .
F: So  Again , if this if these new segmentations work OK , then we then it 's a fair it 's a completely fair test .
C: Yeah .
C: So , how do you determine what you use to group together to be a a ?
F: You group together all the data coming in through one channel
F: and where Thilo 's speech detector has has determined that there is speech .
F: And that speech is is deemed to come from that speaker , whether that 's true or not .
F: So if you get some cross - talk from another microphone , then you just process this it as if it were from that speaker .
C: The only other alternative would be to turn off speaker adaptation in both .
F: Well , that 's more of a problem .
F: I mean , because it 's You can just pretend it 's some kind of gene
F: I mean you can pretend it 's all from one speaker
F: and do all this processing the same ,
F: but then you 're gonna get results that are worse on account of not doing proper speaker normalization
C: Mm - hmm .
F: and you 're gonna have F: So , you could certainly do better than that by doing , for instance uh , cluster the segments ,
F: which is what we do , say , in a Broadcast News system , where you don't have speaker labels .
F: But that would be another processing step that I 'm I would have to debug first , and so forth ,
F: and so we wanna avoid that .
F: So I agree with you . We should  we should , uh , do the you know , this sort of cheating experiment .
C: OK .
C: Yeah . An - and e so that will tell us what the difference it between the mikes ,
C: and then , uh , in order to C: The the other difference that we 'd have to take care of is that ,
C: uh yeah , we we don't have a mike that , uh , is particular to a person .
C: And so we 'll have to do some clustering ,
C: and that 'll be another  another , uh , issue , too .
F: Mm - hmm .
C: But , it it I could be wrong ,
C: but it seems to me that that the speaker C: the the level of degradation that you get from having the distant mike in a normal acoustic is much greater than what you get from , say , not applying speaker adaptation or applying speaker adaptation .
C: I think that the C: I mean , we 'll see .
C: But but , I think that the kind of gains that we 've seen from speaker adaptation on Hub - five sort of things are like a few percent .
C: Right ?
C: And F: It 's not just speaker adaptation .
F: It 's the whole norm feature normalization process .
F: I it 's spea uh , all that is speaker - based .
F: You know , so we F: So , in that I 'm ,
F: um F: Y you know , d d b the most important , of course , is the cepstral mean subtraction .
C: Yeah .
F: And that F: I don't know if we F: we never really F: I don't remember ,
F: because it 's so far  s so long ago that we didn't do that on a per speaker basis ,
C: It doesn't make that much difference , I think .
F: but C: I would doubt that it would be a huge amount of difference for that .
C: So , I mean , I I think that that difference would definitely be marginal .
C: I think the main thing is to do something C: to do some cepstral mean subtraction on some level .
C: And , uh , so what 's different about this processing is just that we 're doing it at a much longer time scale .
C: Right ?
C: But Um F: A and by the way ,
B: What wo
F: it 's actually we 're we 're already If we use the same segmentations that we use for the close - talking microphone , then the segmentations assume that we have access to all channels
F: and cross correlate them ,
C: That 's right .
F: so there 's no point in not using that knowledge for speaker identification .
C: Yeah .
A: Mm - hmm .
B: Uh , I I think also for the log spectral mean subtraction , uh , we wanna know which speaker 's talking when ,
C: Yeah .
B: cuz we wanna chain together the audio from one particular speaker to calculate the mean
F: Yeah .
B: and subtract it ,
B: and we don't F: Right .
C: Right .
F: OK .
C: Um . Yeah , I guess .
C: But , um ,
C: I also think that a again once we got into it , that , um , using some kind of clustering would probably work reasonably well there , too .
C: Certainly for the the two microphone case ,
C: which we 're not gonna mess with because it 's another whole deal with the low - quality microphones ,
C: um ,
C: we ought to be able to at least tell that it appears that things are coming from a particular direction .
C: So we ought to be able to use that information , um , as well .
C: But , I think we might be able to do not too bad a job of separating out sp uh , segments that appear to come from a single speaker
C: both in terms of s acoustic similarity and in terms of direction .
C: So , I mean But that 's another research thing to do
C: and probably won't get done the next week .
F: Right . So what what is the schedule here ?
C: Well , I mean , I 'm I 'm leaving for for the , uh , the New Orleans meeting , uh , next Saturday ,
C: and and , um ,
F: Mm - hmm .
C: it 'd be kinda nice to have some results at least a day or two before that , so that I could figure out what I wanted to say about it .
F: Mm - hmm .
F: Oh , we 'll call you when you get there .
A: You 'll have email .
A: Right ?
C: Yeah .
C: Uh , not to mention that that , uh , Mari 's putting together this report next week , too , you know .
C: So Uh ,
C: what we were hoping was that over the weekend we could do , uh , the , um , calculation on the training set
C: and , uh C: uh , maybe , you know , we could by the end of the weekend , we could have the top one ,
C: and and then early next week , do these .
C: If we had enough machines , maybe do them in parallel .
F: Mm - hmm .
C: So that by the middle of the week we had s had some kind of result .
C: I mean , it 's it 's one of these Hail Mary kinds of things .
C: I mean , it C: it , uh might might not work out .
C: But , uh , f figured I may as well ask for it .
F: OK .
F: I 'll ask A: So ?
F: The other thing is , um F: and I 'll ask Don which is easier to process in terms of creating these F: the the test data for the far far microphone .
F: If if it turns out that for some reason it 's easier for him to use the old um , the the the old ,
F: uh ,
A: Segmentation ?
F: segmentations , then we 'll just use that , I figure .
C: Right .
F: Um A: S So , um .
A: I don't want you to have to be burdened with doing a lot of stuff .
A: What what can I do to ?
A: Y y you said it would be easy for you to do that top one there ,
A: and I guess Don can do the segmentations of the , uh , channel F ?
F: Mm - hmm .
F: Um A: I mean , I can certainly help with , uh , retraining the short male models ,
F: Right .
A: uh , once we have the new data .
C: You have models of short males ?
F: Yeah .
F: Um Right . Uh , let 's see . The the
F: You you can I mean you could you could run the ,
F: um ,
F: you c Basically , once the , um , whe the top the top one 's done , you could easily re - run the whole set of experiments .
A: The top one is done . I c
A: I can re - do the next one .
A: Yeah .
A: Mm - hmm .
F: Uh , I mean , manage the jobs and so forth ,
A: Yeah . Sure .
F: uh  Um ,
A: Yeah , the the bottom one would b just be a matter of pointing it at a new set of files and kicking it off ,
F: that 's all A: so that would be re
A: I mean , th not the bottom one ,
A: but the middle one
F: Mm - hmm .
A: would be really easy once you 've got the top one going .
F: Yeah .
A: I could do that .
F: Right .
A: OK . So I guess I just need to get Don to , uh F: Right . So , somehow the F: Assuming he uses the new naming scheme , then he should call the waveforms
F: the so , the waveform names have the , you know , meeting meeting ID ,
F: and the microphone ,
F: and the , um F: I guess , the channel and the microphone and the speaker , um , speaker F: some something that identifies the speaker .
A: Mm - hmm .
F: So A: To keep it the same but j just change them all to channel F ?
F: Exactly . So , uh well , you still need to be able to distinguish the different speakers .
F: That 's the key point .
A: Right . Right .
F: Because , if we wanna do what we just discussed F: So  Uh , uh , the the best the easiest way to do that would be to just take You know , you make the channel be channel F ,
A: Yeah .
F: but then keep the speaker names the same as they would be in the old in the close - talking , uh , version .
A: Yeah .
A: OK .
A: OK .
A: OK .
A: And so that 's something that Don would do A: right ? A: when he creates these .
F: Right . Exactly .
A: OK . OK .
A: So will you talk to him about that ,
A: or do you want me to talk to him ?
F: I , uh , I I can talk to him .
A: OK .
A: And then the bottom one in terms of the test will be ?
A: Uh A: That will just be a copy of the one above it , except for different models from training .
B: Well , we also have to mean subtract the test data .
A: Ah . OK .
A: So , we need to run ?
A: OK .
A: Well , once we have the new , uh A: Well , once I do that , uh , second experiment , we 'll have the , um , files ,
A: and I can give you those to to process .
B: OK .
B: And there 's , um ,
B: S so the way this means subtraction expects to work , is it expects to have , um , this continuous stream of audio data from a particular speaker to operate on .
B: And it goes along it with this sliding window , calculating the mean using the data in the window ,
B: and then subtracting that .
A: So , do you create this continuous stream from the individual utterance files ?
B: I mean , uh B: That 's that 's how I 've been doing it ,
B: just by concatenating files together .
B: Um , and if these files and the
B: since they 're individual utterance files , um , s long silence periods are removed ,
B: which is a good thing .
B: Because this method might estimate the mean badly , if it had to face long silence periods .
B: But that does mean that I need as much I need twice as much disk space as the original set
B: cuz I need while I 'm running it cuz I need to create this intermediate set , um , of these big files ,
A: Yeah .
B: and then create the finally , the mean subtracted , um , little files .
B: And then I can get rid of the big files .
B: But st while I 'm doing the processing , I 'll nee I need twice as much disk space .
A: OK . I 'm gonna I 'll check with , um , Markham ,
A: and see what happened with the the disks .
A: He went to put those on a couple of weeks ago
A: and something C: I think Markham 's out on vacation .
C: I think , che check with Dave .
A: Oh , OK . I 'll ask David , then .
E: Yeah .
C: Yeah .
A: You haven't seen new disks pop up ,
A: have you ?
E: Nope .
E: I was wondering if they were in .
A: OK .
A: Yeah .
F: Well , they grow them on trees now .
C: I thought they were like mushrooms .
A: Well , he went to put them on F: Just , you shake them and they fall down .
C: They 're popping up .
A: Yeah .
A: He went to put them on and then something happened .
A: And he sent around a note saying " Oh , uh , it didn't work , and we 'll have to schedule another time . "
E: Yeah .
E: Yeah .
E: Something w went wrong .
A: And then ,
A: didn't see E: Nothing happened .
A: nothing happened .
E: No ? Yeah .
A: So A: I 'll check with David about that .
A: OK .
F: OK .
C: Yeah .
C: Cuz we still all have tha the that other one going ,
C: which is the , uh the Macrophone training .
B: Right ,
B: an and B: So so , Andreas , um ,
F: Mm - hmm ?
B: in U doctor speech data SRI Hub - five , there 's this , uh , Hub - five training set .
F: Mmm . Mm - hmm .
B: Now , is that the long training set there ?
F: Mm - hmm .
F: That 's everything . Yeah ,
B: OK .
F: so F: So , I can give you a list of the short version .
B: OK . I th I think you already did , actually .
F: So you can F: Oh , OK .
B: OK . And so , say the Macrophone files that are included in this short training , are just a subset of the Macrophone files .
B: Right ?
F: That 's right .
B: OK .
B: So so , um when you You did some TI - digits t t experiments training on Macrophone .
F: Yeah .
B: Um .
B: But that 's not necessarily any less data than the SRI Hub - five
B: set . It 's not a it 's not a subset of the short SRI Hu - Hub - five set .
B: Right ?
F: Um F: Wel - No , it is .
F: Th - the F: Sorry .
F: Um . Can you repeat the question ?
B: Uh , whe when you trained on Macrophone , um , to do those digits experiments , did you use the entire Macrophone corpus ?
F: There wa it is a subse
F: Yeah .
F: No . Only the portion that was in the Hub - five training set .
B: Oh . OK .
C: That was in the Hub - five small training set ?
F: Well , the Hub - five small training set contains as much Macrophone as the large training set , for historical reasons .
C: Yes .
F: Yeah .
B: OK - OK .
B: So Um F: So , do you have that processed there , then F: right ?
F: Because you already did did y didn't you already do that experiment ?
B: I <comment> I I got confused ,
B: cuz I thought I thought you were using the whole Macrophone set .
F: Mm - hmm .
F: No .
B: Um . OK .
B: Well , if if if I just need to use that subset , I I can get it processed .
B: I actually got I think I got f into it before ,
B: and then I thought I was doing the wrong thing
B: and I stopped .
B: And it shouldn it shouldn't take that long to do .
F: Mmm . OK .
F: Right .
C: OK .
F: And you need only the males .
F: So .
B: Oh , OK .
A: So , basically , Dave , so y for you to get your processing going , you need the list of the , uh , wave
A: Well , I guess it 'll dep
A: you 'll you 'll we 'll need to get the segmentations .
B: Yeah .
A: Figure out whether we 're using the new or the old from Don .
A: And then ,
A: uh ,
A: from that you need the from the segmentations you 'll have the list of wavefiles that the short , uh , set is trained on .
A: And then you 'll need disk space .
A: And once you 've got those things , then you can start your processing .
A: Right ?
B: Yeah .
A: OK .
F: Does this th this ?
F: It - it 's sort of f f not very nice to use the small training set for another reason ,
F: which is that the you also are losing on Again , because you don't use all the data you have for one speaker .
F: So , the normalizations you compute for your training speakers will be , uh , crummier than they would in the large training set .
F: So , um , I have to So , to make it really a matching experiment , I have to find F: uh , I have to use short models that were trained on normalizations that were also only estimated on the short set .
F: Which is , uh A: Do you have this , uh ?
F: I think so .
F: I 've I I have to check .
F: In any case , I could retrain short models within a few hours actually at if I use machines at SRI .
C: I wonder about that , though .
C: I mean , because all we 're doing The only reason we 're using the short training set is is for speed .
C: And there we 're not really making any claims about using a smaller training set .
F: Yeah .
C: So as long as we 're not using any testing data from F: No . But the thing is , if if we used if we used the whole training set for normalizations , then David would have to process much more data ,
C: Yeah .
F: which That 's a that 's one bottleneck , for us
F: right ,
F: in terms of get
C: Oh , you mean for for his normalizations .
F: Yeah .
C: Oh , oh , oh . I 'm sorry .
F: Right .
F: So , you wanna do the exact same thing ,
C: Right .
F: or else you 'll have apples and oranges .
C: Yeah .
F: So .
F: It doesn't make I don't think it makes that much of a difference .
F: It 's just this little detail that if you can take care of that , then you should .
C: Mm - hmm .
F: I I think I have I have the models ,
F: I have <comment> I have ,
F: um F: let 's see , um F: Yeah . And if not I can retrain those models very quickly .
A: Oh , there 's there 's one other issue ,
A: uh , and that is that Dave throws out speakers that have less than twelve seconds of training data .
A: And he said there were a few in the Macrophone set like that .
A: So , do we need to wait to find out who he 's gonna throw out ,
A: so that we create a new set of short models that don't include those speakers ?
F: Uh C: Say this again ?
C: Sorry I missed it .
A: So i in  Th - the problem is that if we proceed like we just described , um , when he goes to m um , create the new s training data with his processing , he throws out some speakers .
A: So the t two training sets won't be identical .
F: Yeah .
C: He throws out some speakers that are that are very small .
A: Yeah . Have just a little bit .
F: Yeah . I don't think it 'll make a matter .
C: Yeah . I think there was only a few
A: OK .
C: we thought to be the case .
F: In fact , I thought about throwing those out too ,
C: Righ - ?
F: because when I heard how little speech there was for some of them , I thought they can only hurt your models ,
F: because they 're again their normalizations will be all all all over the map ,
A: Yeah .
C: Right .
F: and you won't get very very clean models from them , anyhow .
F: So .
A: So you think it 's OK , then ?
F: Yeah .
F: In fact , if if you wanna do this , uh , to speed things up , um , you we can leave out the Macrophone data altogether .
F: That hurt F: Actually .
F: Oh , no . Sorry .
F: Not in the short .
F: Then you have too little data .
F: OK .
F: Sorry . Forget that .
F: Um ,
F: When you use when you go to the large training set , then leaving out Macrophone actually sometimes helps you ,
F: because it 's it it 's just not relevant to the to the meeting and or to conversational speech anyway .
A: It 's read .
F: OK .
F: Yeah . Leave it out ,
F: and  Um , in the event that I retrain the short models ,
F: um , why don't you give me a list of the files that you throw out ,
F: and I I 'll throw them out , too .
B: Right .
F: And then we have complete  completely identical training conditions .
A: We sh M
A: Right .
A: A actually you should be able to figure out , Dave ,
A: right ,
A: once you know the segmentations , uh , who you 're going to which speakers will get left out , even before you run your processing .
B: Mm - hmm .
A: Right ?
B: The segmentations ?
A: Yeah . The segmentations from Don ?
F: But th the segmentations are only they only affect the test set .
A: Once we ?
B: Oh .
F: We 're talking about the training speakers .
A: Ah . Right , right , right , right , right .
C: No . The training set he could go through right now , and see how how long the F: Right .
A: Right . But I 'm just wondering how long it will take to get that information .
B: Um F: He already has the in you already have the information .
B: I I have i I have it for for Macrophone , um , already , I think ,
F: Right ?
B: and , um , I think by tomorrow I 'll have it for th for the rest .
F: Mm - hmm .
F: OK . Alright .
A: OK .
F: OK .
C: At that one ? Maybe .
C: Uh C: Been looking at synthesizers ?
E: Synthesizers ?
C: You were looking at Festival .
E: Yeah , yeah . I was doing something for the SmartKom data collectioners .
C: Yeah .
E: Robert has taken his laptop t back to Germany so we needed a new synthesis machine .
E: And we have now a SUN workstation in the library , which does the synthesis
E: and the Festival speech s s system is running on
A: Sorry , Robert did what ?
A: Robert took what ?
E: His laptop where , uh which we used for the SmartKom data collection , for the synthesis .
A: Uh - huh .
E: And so he took it to Germany
E: and so we couldn't do any data collection .
A: Oh .
A: Is he gone now ?
E: Uh E: No . He 's just gone to a SmartKom workshop .
A: Oh . Oh , oh . Oh , OK .
E: And so , we have now the SUN in the library which can do that .
E: And I looked into the ts F - zero thing and talked to to Liz ,
E: and it seems that it 's quite s what she wants ,
E: but we 'll have to think about the the energy thing E: uh , what we wanna do .
C: Right .
C: This was a business about , uh , um , coming up with something that that was purely prosodic .
F: Mm - hmm .
C: And so , uh , we 're just gonna use a pitch detector ,
C: drive a synthesizer ,
C: and since it doesn't have a hook in it for , uh , modifying energy , we 'll have a little box at the output that 'll modify the energy .
C: So F: OK .
C: So . Rrrrr - rrrrm
C: Something like that .
A: Are you are you interfacing to that thing with the C - plus - plus
A: Or are you is there another interface that you use ?
E: For w for Festival ?
A: Yeah .
E: Uh , you can just use it from the E: Yeah . Basically from the command line ,
E: and the defining the phones , whatever you want to have synthesized ,
A: Oh .
E: and give the F - zero targets ,
A: And it saves it in a E: and then ts get gives out a a waveform ,
E: and I I want to manipulate the the waveform , then .
A: I see .
A: Oh , that 's neat .
C: OK ?
C: Digits ?
A: Ah . OK .
C: That 's all folks .