Professor C: Oh , the short amount of time thing , right .
PhD D: So so we you know , we had spent a lot of time , um , writing up the HLT paper and we wanted to use that , uh , kind of analysis ,
Professor C: Yeah .
PhD D: but the HLT paper has , you know , it 's a very crude measure of overlap . It 's not really something you could scientifically say is overlap , it 's just whether or not the , um , the segments that were all synchronized , whether there was some overlap somewhere .
Grad E: c High correlation .
PhD D: And , you know , that pointed out some differences , so he thought well if we can do something quick and dirty because Dan said the cross - cancellation , it 's not straight - forward . If it were straight - forward then we would try it , but so , it 's sort of good to hear that it was not straight - forward , thinking if we can get decent forced alignments , then at least we can do sort of a overall report of what happens with actual overlap in time , but , um PhD B: I didn't think that his message said it wasn't straight - forward .
Grad E: Well if we 'd just Professor C: Well
PhD B: I thought he 's just saying you have to look over a longer time window when you do it .
Grad E: Um - hmm .
PhD D: and the but there are some issues of this timing , um , in the recordings
Professor C: Yeah .
PhD B: Right .
PhD D: and PhD B: So you just have to look over longer time when you 're trying to align the things , you can't you can't just look Grad E: Well . are you talking about the fact that the recording software doesn't do time - synchronous ? Is that what you 're referring to ?
Professor C :
Grad E: That seems to me you can do that over the entire file and get a very accurate PhD F: I don't thi I d I don't think that was the issue .
PhD D: I yeah , that was sort of a side issue .
Grad E: I didn't think so either .
PhD F: The issue was that you have to you have have you first have to have a pretty good speech detection on the individual channels .
PhD D: And it 's dynamic , so I guess it was more dynamic than some simple models would be able t to so so there are some things available , and I don't know too much about this area where if people aren't moving around much than you could apply them , and it should work pretty well if you took care of this recording time difference .
Grad E: Right , which should be pretty straight forward .
PhD D: Which a at least is well defined , and
Grad E: Yeah .
PhD D: um , but then if you add the dynamic aspect of adapting distances , then it wasn't I guess it just wasn't something that he could do quickly and not in time for us to be able to do something by two weeks from now , so . Well less than a week . So um , so I don't know what we can do if anything , that 's sort of worth , you know , a Eurospeech paper at this point .
PhD B: Well , Andreas , how well did it work on the non - lapel stuff ?
Grad E: Yeah . That 's what I was gonna say .
PhD F: I haven't checked those yet .
Grad E: C
PhD F: It 's very tedious to check these .
PhD B: Mmm .
PhD F: Um , we would really need , ideally , a transcriber to time mark the you know , the be at least the beginning and s ends {comment} of contiguous speech . Um ,  and , you know , then with the time marks , you can do an automatic comparison of your of your forced alignments .
PhD B: Because really the the at least in terms of how we were gonna use this in our system was to get an ideal an idea , uh , for each channel about the start and end boundaries .
Grad E: Oh , MNCM .
PhD F: Mm - hmm .
PhD B: We don't really care about like intermediate word boundaries , so PhD F: No , that 's how I 've been looking at it .
PhD B: Yeah .
PhD D: Right .
PhD F: I mean , I don't care that the individual words are aligned correctly ,
PhD B: Yeah .
PhD F: but you don't wanna , uh , infer from the alignment that someone spoke who didn't .
PhD B: Right , exactly . So that 's why I was wondering if it PhD F: so , so PhD B: I mean , maybe if it doesn't work for lapel stuff , we can just not use that
PhD F: Yeah .
PhD B: and PhD F: I haven't I ha just haven't had the time to , um , do the same procedure on one of the so I would need a k I would need a channel that has a speaker whose who has a lot of overlap but s you know , is a non - lapel mike . And , um ,  where preferably , also there 's someone sitting next to them who talks a lot .
Grad E: Hmm !
PhD F: So , I Grad E: So a meeting with me in it .
PhD F: maybe someone can help me find a good candidate and then I would be willing to
PhD B: We c you know what ? Maybe the best way to find that would be to look through these .
PhD F: you know , hand
PhD B: Cuz you can see the seat numbers , and then you can see what type of mike they were using . And so we just look for , you know , somebody sitting next to Adam at one of the meetings PhD D: Actually y we can tell from the data that we have ,
PhD F: From the insertions , maybe ?
PhD D: um , yeah , there 's a way to tell .
PhD F: fr fr from the PhD D: It might not be a single person who 's always overlapping that person but any number of people ,
PhD F: Right .
PhD D: and , um , if you align the two hypothesis files across the channels , you know , just word alignment , you 'd be able to find that . So so I guess that 's sort of a last ther there 're sort of a few things we could do . One is just do like non - lapels if we can get good enough alignments . Another one was to try to get somehow align Thilo 's energy segmentations with what we have . But then you have the problem of not knowing where the words are because these meetings were done before that segmentation . But maybe there 's something that could be done .
PhD B: What what is why do you need the , um , the forced alignment for the HLT I mean for the Eurospeech paper ?
PhD D: Well , I guess I I wanted to just do something not on recognition experiments because that 's ju way too early , but to be able to report , you know , actual numbers . Like if we if we had hand - transcribed pe good alignments or hand - checked alignments , then we could do this paper . It 's not that we need it to be automatic . But without knowing where the real words are , in time PhD B: So it was to get it was to get more data and better to to squeeze the boundaries in .
PhD D: To to know what an overlap really if it 's really an overlap , or if it 's just a a a segment correlated with an overlap ,
PhD B: Ah , OK . Yeah .
PhD D: and I guess that 's the difference to me between like a real paper and a sort of , promissory paper . So , um , if we d it might be possible to take Thilo 's output and like if you have , um , like right now these meetings are all ,
Grad E: Ugh ! I forgot the digital camera again .
PhD D: um ,
Grad E: Every meeting !
PhD D: you know , they 're time - aligned , so if these are two different channels and somebody 's talking here and somebody else is talking here , just that word , if Thilo can tell us that there 're boundaries here , we should be able to figure that out
Grad E: Mm - hmm .
PhD D: because the only thing transcribed in this channel is this word . But , um , you know , if there are things Grad E: Two words .
PhD D: Yeah , if you have two and they 're at the edges , it 's like here and here , and there 's speech here , then it doesn't really help you , so , um PhD B: Thilo 's won't put down two separate marks in that case PhD D: Well it w it would , but , um , we don't know exactly where the words are because the transcriber gave us two words in this time bin
Grad E: Thilo 's will . But .
PhD D: and we don't really know , I mean ,
Postdoc A: Well it 's a merging problem . If you had a if you had a s if you had a script which would PhD D: yeah it 's Postdoc A: I 've thought about this , um , and I 've discussed I 've discussed it with Thilo ,
PhD D: I mean , if you have any ideas . I would Postdoc A: um , the , I mean , I I in principle I could imagine writing a script which would approximate it to some degree , but there is this problem of slippage ,
Grad E: Well maybe Maybe that will get enough of the cases to be useful .
Postdoc A: yeah .
PhD D: Right . I mean , that that would be really helpful . That was sort of another possibility .
Grad E: You know s cuz it seemed like most of the cases are in fact the single word sorts , or at least a single phrase
Postdoc A: Well they they can be stretched .
Grad E: in most of the bins .
PhD F: Mmm .
Postdoc A: I wouldn't make that generalization cuz sometimes people will say , " And then I " and there 's a long pause
PhD D: Yeah .
Postdoc A: and finish the sentence and and sometimes it looks coherent and and the I mean it 's it 's not a simple problem . But it 's really And then it 's coupled with the problem that sometimes , you know , with with a fricative you might get the beginning of the word cut off and so it 's coupled with the problem that Thilo 's isn't perfect either . I mean , we 've i th it 's like you have a merging problem plus so merging plus this problem of , uh , not Grad E: Right . Hmm !
Postdoc A: y i i if the speech - nonspeech were perfect to begin with , the detector , that would already be an improvement , but that 's impossible , you know , i that 's too much to ask .
PhD D: Right .
Grad E: Yes .
Postdoc A: And so i and may you know , I mean , it 's I think that there always th there would have to be some hand - tweaking , but it 's possible that a script could be written to merge those two types of things . I 've I 've discussed it with Thilo and I mean in terms of not him doing it , but we we discussed some of the parameters of that and how hard it would be to in principle to write something that would do that .
PhD D: I mean , I guess in the future it won't be as much as an issue if transcribers are using the tightened boundaries to start with , then we have a good idea of where the forced alignment is constrained to .
Postdoc A: Well , it 's just , you know , a matter of we had the revolution we had the revolution of improved , uh , interface , um , one month too late ,
PhD D: So I 'm no I don't know if this
Grad E: Oh . Tools .
Postdoc A: but it 's like , you know , it 's wonderful to have the revolution ,
PhD D: Oh it 's it 's a Postdoc A: so it 's just a matter of of , you know , from now on we 'll be able to have things channelized to begin with .
PhD D: yeah .
Grad E: Right . And we 'll just have to see how hard that is .
Postdoc A: Yeah , that 's right .
Grad E: So so whether the corrections take too much time .
Postdoc A: That 's right .
Grad E: I was just thinking about the fact that if Thilo 's missed these short segments , that might be quite time - consuming for them to insert them .
PhD D: Yeah .
Postdoc A: Good point .
PhD D: But he he also can adjust this minimum time duration constraint and then what you get is noises mostly ,
Postdoc A: Yeah .
Grad E: Spurious .
PhD D: but that might be OK , an
Grad E: It might be easier to delete something that 's wrong than to insert something that 's missing .
PhD D: Right . And you can also see in the waveform exac
Grad E: What do you think , Jane ?
PhD D: yeah .
Professor C: If you can feel confident that what the yeah , that there 's actually something PhD D: Yeah .
Professor C: that you 're not gonna miss something ,
Grad E: Yeah . Cuz then then you just delete it , and you don't have to pick a time .
Professor C: yeah .
PhD D: I think it 's Postdoc A: Well the problem is I you know I I it 's a it 's a really good question , and I really find it a pain in the neck to delete things because you have to get the mouse up there on the t on the text line and i and otherwise you just use an arrow to get down I mean , i it depends on how lar th there 's so many extra things that would make it one of them harder than the other , or or vice versa . It 's not a simple question . But , you know , I mean , in principle , like , you know , if one of them is easier then to bias it towards whichever one 's easier .
Grad E: Yeah , I guess the semantics aren't clear when you delete a segment , right ? Because you would say You would have to determine what the surroundings were .
PhD D: You could just say it 's a noise , though , and write , you know , a post - processor will just all you have to do is just Grad E: If it 's really a noise .
PhD D: or just say it 's just put " X , " you know , like " not speech " or something ,
Postdoc A: I think it 's easier to add than delete , frankly ,
PhD D: and then you can get Yeah , or
Postdoc A: because you have to , uh , maneuver around on the on both windows then .
Grad E: To add or to delete ?
Postdoc A: To delete .
PhD D: Anyways , so I I guess Grad E: OK . That Maybe that 's an interface issue that might be addressable .
Postdoc A: It 's possible .
Grad E: But I think it 's the semantics that are that are questionable to me , that you delete something So let 's say someone is talking to here , and then you have a little segment here . Well , is that part of the speech ? Is it part of the nonspeech ? I mean , w what do you embed it in ?
PhD D: There 's something nice , though , about keeping , and this is probably another discussion , keeping the stuff that Thilo 's detector detected as possible speech and just marking it as not speech than deleting it . Because then when you align it , then the alignment can you can put a reject model or whatever ,
Grad E: Oh , I see . So then they could just like put Oh that 's what you meant by just put an " X " there .
PhD D: and you 're consistent with th the automatic system ,
Grad E: Uh , that 's an interesting idea .
PhD D: whereas if you delete it Grad E: So so all they So that all they would have to do is put like an " X " there .
PhD D: Yeah , or some , you know , dummy reject mod
Grad E: So blank for blank for silence , " S " " S " for speech , " X " " X " for something else .
PhD D: whatever , yeah . That 's actually a better way to do it cuz the a the forced alignment will probably be more consistent than Postdoc A: Well , like , I think there 's a complication which is that that you can have speech and noise in s
PhD D: I mean if it 's just as easy , but Postdoc A: uh , you know , on the same channel , the same speaker , so now sometimes you get a ni microphone pop and , uh , I mean , there 're these fuzzy hybrid cases , and then the problem with the boundaries that have to be shifted around . It 's not a simple not a simple problem .
PhD D: Anyway , quick question , though , at a high level do people think , let 's just say that we 're moving to this new era of like using the , um , pre - segmented t you know , non - synchronous conversations , does it make sense to try to take what we have now , which are the ones that , you know , we have recognition on which are synchronous and not time - tightened , and try to get something out of those for sort of purposes of illustrating the structure and the nature of the meetings , or is it better to just , you know , forget that and tr I mean , it 's Grad E: Well , I think we 'll have to , eventually . And my hope was that we would be able to use the forced alignment to get it .
PhD D: Right . That was everybody 's hope .
Grad E: But if we can't PhD D: And maybe we can for the non - lapel , but
Grad E: But if we can't , then maybe we just have to PhD D: is it worth if we can't then we can fake it even if we 're we report , you know , we 're wrong twenty percent of the time or ten percent of the time .
Grad E: Well , I 'm thinking are you talking about for a paper , or are talking about for the corpus .
PhD D: Uh uh , that 's a good question actually .
Grad E: I mean cuz for the corpus it would be nice if everything were PhD D: Actually that 's a good question because we 'd have to completely redo those meetings , and we have like ten of them now .
Grad E: We wouldn't have to re - do them , we would just have to edit them .
Postdoc A: Well , and also , I mean , I still haven't I still haven't given up on forced alignment .
PhD D: No , you 're right , actually Postdoc A: I think that when Brian comes , this 'll be uh an interesting aspect to ask him as well b
Grad E: When Postdoc A: when Brian Kingsbury comes .
Grad E: Oh , Brian . You s I thought you said Ryan . And it 's like , " Who 's Ryan ? "
Postdoc A: Yeah , good question .
Grad E: OK .
Postdoc A: Well , Ryan could come .
PhD D: Uh , no , that 's a good point , though , because for feature extraction like for prosody or something , I mean , the meetings we have now , it 's a good chunk of data Grad E: Yep .
PhD D: we need to get a decent f OK .
Postdoc A: That 's what my hope has been ,
PhD D: So we should at least try it even if we can't ,
Postdoc A: and that 's what that 's what you know , ever since the the February meeting that I transcribed from last year , forced alignment has been on the on the table as a way of cleaning them up later .
PhD D: right ?
Grad E: On the table , right ?
Postdoc A: And and so I 'm hopeful that that 's possible . I know that there 's complication in the overlap sections and with the lapel mikes ,
PhD F: There 's Postdoc A: but PhD F: Yeah .
PhD D: I mean , we might be able , at the very worst , we can get transcribers to correct the cases where I mean , you sort of have a good estimate where these places are because the recognition 's so poor . Right ?
PhD B: Yeah , we were never just gonna go with these as the final alignments .
PhD D: And so you 're Postdoc A: I agree . I agree .
PhD B: We were always gonna run them past somebody .
PhD D: Yeah .
Postdoc A: Absolutely .
PhD D: So we need some way to push these first chunk of meetings into a state where we get good alignments .
PhD F: I 'm probably going to spend another day or so trying to improve things by , um ,  by using , um , acoustic adaptation . Um , the  Right now I 'm using the unadapted models for the forced alignments , and it 's possible that you get considerably better results if you , uh , manage to adapt the , uh , phone models to the speaker and the reject model to the to to all the other speech . Um , so
PhD B: Could you could you at the same time adapt the reject model to the speech from all the other channels ?
Professor C: That 's what he just said .
Grad E: That 's what he was saying .
PhD D: Yeah .
PhD F: That 's what I just said .
PhD B: Oh , not just the speech from that of the other people from that channel ,
PhD F: Right .
PhD B: but the speech from the a actual other channels .
PhD D: Right .
PhD F: Oh , oh , I see . Um ,
Professor C: Oh .
Grad E: I don't think so . I don't think that would work ,
PhD F: No , it Grad E: right ? Because you 'd A lot of it 's dominated by channel properties .
PhD F: th Exactly .
PhD D: But what you do wanna do is take the , even if it 's klugey , take the segments the synchronous segments , the ones from the HLT paper , where only that speaker was talking .
PhD F: So you want to u
PhD D: Use those for adaptation , cuz if you if you use everything , then you get all the cross - talk in the adaptation , and it 's just sort of blurred .
PhD F: That 's a good point .
PhD B: If you PhD F: Yep .
PhD D: And that we know , I mean , we have that . And it 's about roughly two - thirds , I mean , very roughly averaged .
PhD F: Yeah .
PhD D: That 's not completely negligible . Like a third of it is bad for adaptation or so .
PhD F: Mm - hmm .
Grad E: Cool . I thought it was higher than that , that 's pr
PhD D: It really it depends a lot . This is just sort of an overall PhD F: So .
Professor C: Well I know what we 're not turning in to Eurospeech , a redo of the HLT paper .
Grad E: Right .
Professor C: That I don't wanna do that ,
Grad E: Yeah , I 'm doing that for AVIOS .
Professor C: but .
PhD D: Yeah . But I think we 're oh , Morgan 's talk went very well , I think .
Professor C: Bleep .
Grad E: Uh , " bleep " . Yeah , really .
PhD D: I think Morgan 's talk went very well it woke Postdoc A: Excellent .
PhD D: you know , it was really a well presented and got people laughing Postdoc A: Yeah .
PhD F: Some good jokes in it ?
Grad E: Especially the batteried meter popping up ,
PhD D: Yeah .
Grad E: that was hilarious . Right when you were talking about that .
Professor C: You know , that wa that was the battery meter saying that it was fully charged ,
Grad E: It 's full . Yeah .
Professor C: yeah .
Postdoc A: You said , " Speaking about energy " , or something .
Grad E: But that was funny .
Postdoc A: That was very nice .
Professor C: Yeah .
Grad E: He he he was onto the bullet points about talking about the you know the little hand - held , and trying to get lower power and so on ,
PhD F: Po - low power
Grad E: and Microsoft pops up a little window saying " Your batteries are now fully charged . "
Postdoc A: That 's great .
Professor C: Yeah , yeah , yeah .
Grad E: I 'm thinking about scripting that for my talk , you know , put put a little script in there to say " Your batteries are low " right when I 'm saying that .
Professor C: Yeah . Yeah . No I mean , i in in your case , I mean , you were joking about it , but , I mean , your case the fact that your talking about similar things at a couple of conferences , it 's not these are conferences that have d really different emphases . Whereas HLT and and Eurospeech , pretty pretty pretty similar , so I I I can't see really just putting in the same thing ,
Grad E: Are too close , yeah .
PhD D: No , I d I don't think that paper is really Professor C: but PhD D: the HLT paper is really more of a introduction - to - the - project paper , and , um Professor C: Yeah .
Grad E: Yeah , for Eurospeech we want some results if we can get them .
PhD D: Well , yeah , it it 's probably wouldn't make sense ,
Professor C: Or some or some I mean , I would see Eurospeech if we have some Eurospeech papers , these will be paper p p uh , submissions .
PhD D: but Professor C: These will be things that are particular things , aspects of it that we 're looking at , rather than , you know , attempt at a global paper about it .
PhD D: Right , right .
Grad E: Detail , yeah . Overall .
Postdoc A: I did go through one of these meetings . I had , uh , one of the transcribers go through and tighten up the bins on one of the , uh , NSA meetings , and then I went through afterwards and double - checked it so that one is really very very accurate .
PhD D: Oh .
Postdoc A: I men I mentioned the link . I sent You know that one ?
PhD D: Oh , so Grad G: The which one ? I 'm sorry .
Postdoc A: Um , I 'm trying to remember I don't remember the number off hand .
Grad E: Those are all Postdoc A: It 's one of the NSA 's . I sent email before the conference , before last week .
Grad G: Oh , OK .
Postdoc A: Bef - What I mean is Wednesday , Thursday .
PhD D: That might might have been the one one of the ones that we did .
Grad G: Mm - hmm .
Postdoc A: I 'm sure that that one 's accurate , I 've been through it myself .
PhD D: So that might actually be useful but they 're all non - native speakers .
PhD F: So we could compare before and after
Grad G: OK .
PhD F: and see Grad E: Yeah . Yeah , that 's what I was gonna say . The problem with those , they 're all German .
PhD F: oh , Darn !