D: Well , you know , um I mean if we 're talking about , having the , annotators annotate these kinds of features , it seems like ,
D: You know , you D: The the question is , do they do that on , meeting data ?
D: Or do they do that on , Switchboard ?
F: That 's what I was saying ,
F: maybe meeting data isn't the right corpus .
B: W Well it seems like you could do both .
B: I mean , I was thinking that it would be interesting , to do it with respect to , parts of Switchboard anyway , in terms of ,
C: Mm - hmm .
B: uh partly to see , if you could , generate first guesses at what the articulatory feature would be , based on the phone representation at that lower level .
C: Mm - hmm .
B: It might be a time gain .
B: But also in terms of comparability of , um ,
C: Mm - hmm .
D: Well cuz the yeah , and then also , if you did it on Switchboard , you would have , the full continuum of transcriptions .
B: what you gain
B: Yep .
D: You 'd have it , from the lowest level , the ac acoustic features , then you 'd have the , you know , the phonetic level that Steve did ,
B: Mm - hmm .
G: Yeah that that 's all I was thinking about .
D: and ,
G: it is telephone band , so , the bandwidth might be D: yeah .
B: And you could tell that D: It 'd be a complete , set then .
B: And you get the relative gain up ahead .
C: It 's so it 's a little different .
G: Yeah .
C: So I mean i we 'll see wha how much we can , uh , get the people to do , and how much money we 'll have and all this sort of thing ,
B: Mm - hmm .
B: Mm - hmm .
D: But it it might be good to do what Jane was saying uh , you know , seed it , with , guesses about what we think the features are , based on , you know , the phone or Steve 's transcriptions or something . to make it quicker .
C: but ,
C: Might be do both .
F: Alright , so based on the phone transcripts they would all be synchronous , but then you could imagine , nudging them here and there .
D: Adjusting ?
D: Yeah , exactly .
D: Scoot the voicing over a little , because B: Yeah .
F: Right .
C: Well I think what I mean I 'm I 'm a l little behind in what they 're doing , now , and , uh , the stuff they 're doing on Switchboard now .
C: But I think that , Steve and the gang are doing , something with an automatic system first and then doing some adjustment .
C: As I re as I recall .
C: So I mean that 's probably the right way to go anyway , is to is to start off with an automatic system with a pretty rich pronunciation dictionary that , that , um , you know , tries , to label it all .
C: And then , people go through and fix it .
B: So in in our case you 'd think about us s starting with maybe the regular dictionary entry ,
B: and then ?
C: Well , regular dictionary , I mean , this is a pretty rich dictionary .
B: Or would we C: It 's got , got a fair number of pronunciations in it
D: Or you could start from the if we were gonna , do the same set , of sentences that Steve had , done , we could start with those transcriptions .
B: But B: Mm - hmm .
C: Yeah .
G: That 's actually what I was thinking , is tha B: So I was thinking F: Right .
D: Yeah .
G: the problem is when you run , uh , if you run a regular dictionary , um , even if you have variants , in there , which most people don't , you don't always get , out , the actual pronunciations ,
D: Yeah .
G: so that 's why the human transcriber 's giving you the that pronunciation ,
B: Yeah .
C: Actually maybe they 're using phone recognizers .
B: Oh .
G: and so y
G: they they I thought that they were C: Is that what they 're doing ?
F: They are .
G: we should catch up on what Steve is ,
C: Oh , OK .
G: uh I think that would be a good i good idea .
C: Yeah .
C: Yeah , so I think that i i we also don't have , I mean ,
C: we 've got a good start on it ,
C: but we don't have a really good , meeting , recorder or recognizer or transcriber or anything yet ,
C: so .
G: Yeah .
C: So , I mean another way to look at this is to , is to , uh , do some stuff on Switchboard which has all this other , stuff to it .
C: And then , um , As we get , further down the road and we can do more things ahead of time , we can , do some of the same things to the meeting data .
D: Mm - hmm .
B: OK .
D: Yeah .
B: And I 'm and these people might they they are , s most of them are trained with IPA .
C: Yeah
B: They 'd be able to do phonetic - level coding , or articulatory .
D: Are they busy for the next couple years , or ?
B: Well , you know , I mean they , they they 're interested in continuing working with us ,
B: so I mean I , and this would be up their alley , so , we could when the when you d meet with , with John Ohala and find , you know what taxonomy you want to apply , then , they 'd be , good to train onto it .
C: Yeah .
C: Yeah .
C: Anyway , this is , not an urgent thing at all ,
B: Yeah .
C: just it came up .
D: It 'd be very interesting though , to have that data .
G: Yeah .
F: I wonder , how would you do a forced alignment ?
B: I think so , too .
G: Might B: Interesting idea .
F: To to I mean , you 'd wanna iterate , somehow .
F: Yeah .
F: It 's interesting thing to think about .
G: It might be D: Hmm .
G: I was thinking it might be n
F: I mean you 'd you 'd want models for spreading .
D: Of the f acoustic features ?
F: Yeah .
D: Mm - hmm .
C: Mm - hmm .
C: Yeah .
G: Well it might be neat to do some , phonetic , features on these , nonword words .
G: Are are these kinds of words that people never the " huh "s and the " hmm "s and the " huh " and the uh G: These k No , I 'm serious .
G: There are all these kinds of functional , uh , elements .
G: I don't know what you call them .
G: But not just fill pauses but all kinds of ways of interrupting <comment> and so forth .
F: Uh - huh .
G: And some of them are , yeah , " uh - huh "s , and " hmm "s , and , " hmm ! "
G: " hmm " <comment> " OK " , " uh " <comment> Grunts ,
G: uh ,
G: that might be interesting .
B: He 's got lip  lipsmacks .
G: In the meetings .
C: We should move on .
B: Yeah .
C: Uh , new version of , uh , presegmentation ?
A: Uh , oh yeah , um , I worked a little bit on the on the presegmentation to to get another version which does channel - specific , uh , speech - nonspeech detection .
A: And , what I did is I used some normalized features which , uh , look in into the which is normalized energy , uh , energy normalized by the mean over the channels and by the , minimum over the , other .
A: within each channel .
A: And to to , mm , to , yeah , to normalize also loudness and and modified loudness and things and that those special features actually are in my feature vector .
F: Oh .
A: And , and , therefore to be able to , uh , somewhat distinguish between foreground and background speech in in the different in each channel .
A: And , eh , I tested it on on three or four meetings and it seems to work , well yeah , fairly well , I I would say .
A: There are some problems with the lapel mike .
F: Of course .
A: Yeah .
F: Wow that 's great .
A: Uh , yeah .
A: And .
F: So I I understand that 's what you were saying about your problem with , minimum .
A: Yeah .
A: And .
A: Yeah , and and I had I had , uh , specific problems with .
F: I get it .
F: So new use ninetieth quartile , rather than , minimum .
A: Yeah .
A: Yeah .
B: Wow .
A: Yeah yeah , then I I did some some some things like that ,
B: Interesting .
A: as there there are some some problems in , when , in the channel , there they the the speaker doesn't doesn't talk much or doesn't talk at all .
A: Then , the , yeah , there are there are some problems with with with n with normalization , and , then , uh , there the system doesn't work at all .
A: So , I 'm I 'm glad that there is the the digit part , where everybody is forced to say something ,
C: Right .
A: so , that 's that 's great for for my purpose .
A: And , the thing is I I , then the evaluation of of the system is a little bit hard , as I don't have any references .
F: Well we did the hand F: the one by hand .
A: Yeah , that 's the one one wh where I do the training on so I can't do the evaluation on
A: So the thing is , can the transcribers perhaps do some , some some meetings in in terms of speech - nonspeech in in the specific channels ?
F: Uh .
D: Well won't you have that from their transcriptions ?
B: Well , I have B: Well , OK , so , now we need F: No , cuz we need is really tight .
A: Yeah .
B: so , um ,
B: I think I might have done what you 're requesting , though I did it in the service of a different thing .
A: Oh , great .
B: I have thirty minutes that I 've more tightly transcribed with reference to individual channels .
A: OK .
A: OK , that 's great .
A: That 's great
B: And I could And A: for me . Yeah , so .
F: Hopefully that 's not the same meeting that we did .
B: And B: No , actually it 's a different meeting .
F: Good .
B: So , um , e so the , you know , we have the , th they transcribe as if it 's one channel with these with the slashes to separate the overlapping parts .
A: OK .
B: And then we run it through then it then I 'm gonna edit it and I 'm gonna run it through channelize which takes it into Dave Gelbart 's form format .
A: Yeah .
A: Yeah .
B: And then you have , all these things split across according to channel ,
B: and then that means that , if a person contributed more than once in a given , overlap during that time bend that that two parts of the utterance end up together , it 's the same channel ,
B: and then I took his tool , and last night for the first thirty minutes of one of these transcripts , I , tightened up the , um , boundaries on individual speakers ' channels ,
A: OK .
A: OK .
B: cuz his his interface allows me to have total flexibility in the time tags across the channels .
A: Yeah .
A: Yeah .
B: And um , so .
D: So , current This week .
A: so , yeah yeah , that that that 's great , but what would be nice to have some more meetings , not just one meeting to to be sure that that , there is a system ,
B: Yes .
B: Might not be what you need .
F: Yeah , so if we could get a couple meetings done with that level of precision I think that would be a good idea .
A: OK .
B: Oh , OK .
A: Yeah .
B: Uh , how how m much
B: time so the meetings vary in length ,
B: what are we talking about in terms of the number of minutes you 'd like to have as your as your training set ?
A: It seems to me that it would be good to have , a few minutes from from different meetings ,
A: so .
A: But I 'm not sure about how much .
B: OK , now you 're saying different meetings because of different speakers or because of different audio quality or both or ?
A: Both A: both .
B: OK .
A: Different different number of speakers , different speakers , different conditions .
C: Yeah , we don't have that much variety in meetings yet ,
C: uh , I mean we have this meeting and the feature meeting and we have a couple others that we have uh , couple examples of . But but , uh ,
A: Yeah , m
A: Yeah .
B: Mm - hmm .
E: Even probably with the gains differently will affect it , you mean A: Uh , not really as C: Poten - potentially .
E: Oh , cuz you use the normalization ?
A: uh , because of the normalization , yeah .
E: OK .
C: Oh , OK .
A: Yeah .
G: We can try running G: we haven't done this yet because , um , uh , Andreas an is is gonna move over the SRI recognizer .
G: i basically I ran out of machines at SRI ,
G: cuz we 're running the evals
A: OK .
G: and I just don't have machine time there .
G: But , once that 's moved over , uh , hopefully in a a couple days , then , we can take , um , what Jane just told us about as , the presegmented , <nonvocalsound> the the segmentations that you did , at level eight or som <comment> at some , threshold that Jane , tha right , and try doing , forced alignment . um , on the word strings .
F: Oh , shoot !
A: Yeah .
A: Yeah .
B: The pre presegment
A: Yeah .
B: yeah .
A: With the recognizer ?
A: Yeah .
G: And if it 's good , then that will that may give you a good boundary .
G: Of course if it 's good , we don't then we 're we 're fine ,
A: Yeah .
A: M
G: but , I don't know yet whether these , segments that contain a lot of pauses around the words , will work or not .
A: I I would quite like to have some manually transcribed references for for the system , as I 'm not sure if if it 's really good to compare with with some other automatic , found boundaries .
G: Yeah .
G: Right .
B: Well , no , if we were to start with this and then tweak it h manually , would that that would be OK ?
A: Yeah .
G: Right .
A: Yeah sure .
G: They might be OK .
B: OK .
G: It you know it really depends on a lot of things ,
A: Yeah .
G: but , I would have maybe a transciber , uh , look at the result of a forced alignment and then adjust those .
A: Yeah .
A: To a adjust them , or ,
A: yeah .
A: Yeah , yeah .
G: That might save some time .
G: If they 're horrible it won't help at all ,
A: Yeah , great .
G: but they might not be horrible .
A: Yeah .
G: So but I 'll let you know when we , uh , have that .
A: OK , great .
B: How many minutes would you want from B: I mean , we could easily , get a section , you know , like say a minute or so , from every meeting that we have so f from the newer ones that we 're working on , everyone that we have .
B: And then ,
B: should provide this .
A: If it 's not the first minute of of the meeting , that that 's OK with me ,
A: but , in in the first minute , uh , Often there are some some strange things going on which which aren't really , well , for , which which aren't re re really good .
A: So . What what I 'd quite like , perhaps , is , to have , some five minutes of of of different meetings ,
A: so .
B: Somewhere not in the very beginning , five minutes , OK .
A: Yeah .
B: And , then I wanted to ask you just for my inter information ,
B: then , would you , be trai
B: cuz I don't quite unders
B: so , would you be training then , um , the segmenter so that , it could , on the basis of that , segment the rest of the meeting ?
B: So , if I give you like five minutes is the idea that this would then be applied to , uh , to , providing tighter time bands ?
A: I I could do a a retraining with that , yeah .
B: Wow , interesting .
A: That 's but but I hope that I I don't need to do it .
B: OK .
B: Uh - huh .
A: So , uh it c can be do in an unsupervised way .
B: Excellent .
A: So .
B: Excellent , OK .
A: I 'm I 'm not sure ,
A: but , for for for those three meetings whi which I which I did , it seems to be , quite well ,
A: but , there are some some as I said some problems with the lapel mike ,
A: but , perhaps we can do something with with cross - correlations to , to get rid of the of those .
A: And .
A: Yeah .
A: That 's that 's what I that 's my future work .
A: Well well what I want to do is to to look into cross - correlations for for removing those , false overlaps .
B: Wonderful .
G: Are the , um , wireless , different than the wired , mikes , at all ?
G: I mean , have you noticed any difference ?
A: I 'm I 'm not sure , um , if if there are any wired mikes in those meetings , or , uh , I have have to loo have a look at them
A: but , I 'm I 'm I think there 's no difference between ,
G: So it 's just the lapel versus everything else ?
A: Yeah .
A: Yeah .
B: OK , so then , if that 's five minutes per meeting we 've got like twelve minutes , twelve meetings , roughly , that I 'm that I 've been working with , then C: Of of of the meetings that you 're working with , how many of them are different , tha
A: No .
C: are there any of them that are different than , these two meetings ?
B: Well oh wa in terms of the speakers or the conditions or the ?
C: Yeah , speakers .
C: Sorry .
C: So .
B: Um , we have different combinations of speakers .
A: Yeah , that B: I mean , just from what I 've seen , uh , there are some where , um , you 're present or not present ,
B: and , then then you have the difference between the networks group and this group
A: Yeah , I know , some of the NSA meetings , yeah .
C: Yeah .
C: So I didn't know in the group you had if you had A: Yeah .
C: so you have the networks meeting ?
B: Yep , we do .
A: Yeah .
C: Do you have any of Jerry 's meetings in your , pack , er ,
B: Um , no .
C: No ?
B: We could ,
B: I mean you you recorded one last week or so .
B: I could get that new one in this week I get that new one in .
F: Yep .
G: We 're gonna be recording them every Monday ,
F: u
C: Yeah .
C: Cuz I think he really needs variety ,
G: so C: and and having as much variety for speaker certainly would be a big part of that I think .
B: Great .
A: Yeah .
A: Yeah .
B: OK , so if I ,
B: OK , included include ,
B: OK , then , uh , if I were to include all together samples from twelve meetings that would only take an hour
B: and I could get the transcribers to do that
B: right B: I mean , what I mean is , that would be an hour sampled , and then they 'd transcribe those that hour ,
B: right ?
B: That 's what I should do ?
C: Yeah .
C: And .
C: Right .
C: Ye - But you 're y
A: That 's that 's .
B: I don't mean transcribe
B: I mean I mean adjust .
B: So they get it into the multi - channel format and then adjust the timebands so it 's precise .
C: So that should be faster than the ten times kind of thing ,
B: Absolutely .
C: yeah .
B: I did I did , um , uh , so , last night I did , uh ,
B: Oh gosh ,
B: well , last night , I did about half an hour in , three hours , which is not , terrific ,
C: Yeah .
B: but , um ,