A: I 'll <comment> probably gonna hafta look at the paper ,
A: but which I 'm not gonna have time to do in the next few days ,
A: but  but I 'm I 'm curious about it .
C: Yeah .
A: Um , uh ,
A: OK .
F: I I did i it did occur to me that this is uh , the return to the transcription ,
F: that there 's one third thing I wanted to to ex raise as a to as an issue
F: which is , um , how to handle breaths .
F: So , I wanted to raise the question of whether people in speech recognition want to know where the breaths are .
F: And the reason I ask the question is ,
F: um , aside from the fact that they 're obviously very time - consuming to encode ,
F: uh , the fact that there was some F: I had the indication from Dan Ellis in the email that I sent to you ,
F: and you know about ,
E: Yeah .
F: that in principle we might be able to , um , handle breaths by accessi by using cross - talk from the other things ,
F: be able that F: in principle , maybe we could get rid of them ,
F: so maybe F: And I was F: I I don't know ,
F: I mean we had this an
F: and I didn't couldn't get back to you ,
E: Yeah .
F: but the question of whether it 'd be possible to eliminate them from the audio signal ,
F: which would be the ideal situation ,
F: cuz A: I don't know think it 'd be ideal .
G: Uh - uh .
A: We - See , we 're we 're dealing with real speech
A: and we 're trying to have it be as real as possible
E: Yeah .
A: and breaths are part of real speech .
F: Well , except that these are really truly F: I mean , ther there 's a segment in o the one I did F: n the first one that I did for i for this ,
E: Yeah .
F: where truly w we 're hearing you breathing like as if we 're you 're in our ear ,
F: you know , and it 's like it 's like A: Yeah .
F: I y i
F: I mean , breath is natural ,
F: but not
A: It is but it is if you record it .
C: Yeah .
F: Except that we 're we 're trying to mimic F: Oh , I see what you 're saying .
F: You 're saying that the PDA application would have uh , have to cope with breath .
A: Yeah .
B: Well
F: But G: An - any application may have to .
E: No .
B: The P D A might not have to ,
E: No i
B: but more people than just PDA users are interested in this corpus .
E: Yeah .
F: OK , then the then I have two questions .
B: So so mean you 're right
B: we could remove it ,
F: Yeah ?
B: but I I think we don't wanna w remove it from the corpus , in terms of delivering it
B: because the people will want it in there .
F: OK , so maybe the question is notating it .
A: Yeah . If it gets F: Yeah ?
A: Yeah i Right .
A: If if it gets in the way of what somebody is doing with it then you might wanna have some method which will allow you to block it ,
A: but you it 's real data .
A: You don't wanna b but you don't F: OK , well A: If s you know , if there 's a little bit of noise out there , and somebody is is talking about something they 're doing , that 's part of what we accept as part of a real meeting ,
A: even And
A: we have the f uh the uh the the fan and the in the projector up there ,
A: and , uh , this is it 's this is actual stuff that we we wanna work with .
F: Well this is in very interesting
A: So .
F: because i it basically has a i it shows very clearly the contrast between , uh , speech recognition research and discourse research
F: because in in discourse and linguistic research , what counts is what 's communit communicative .
G: Mm - hmm .
F: And breath ,
F: you know , everyone breathes ,
F: they breathe all the time .
F: And once in a while breath is communicative ,
F: but r very rarely .
F: OK , so now , I had a discussion with Chuck about the data structure
A: Mm - hmm .
F: and the idea is that the transcripts will that get stored as a master
F: there 'll be a master transcript
F: which has in it everything that 's needed for both of these uses .
A: Mm - hmm .
F: And the one that 's used for speech recognition will be processed via scripts .
F: You know , like , Don 's been writing scripts
F: and and ,
A: Mm - hmm .
F: uh , to process it for the speech recognition side .
F: Discourse side will have
F: this this side over he the we we 'll have a s ch
F: Sorry , not being very fluent here .
F: But , um ,
F: this the discourse side will have a script which will stri strip away the things which are non - communicative .
F: OK . So then the then let 's let 's think about the practicalities of how we get to that master copy with reference to breaths .
F: So what I would r r what I would wonder is would it be possible to encode those automatically ?
F: Could we get a breath detector ?
B: Oh , just to save the transcribers time .
F: Well , I mean , you just have no idea .
F: I mean , if you 're getting a breath several times every minute ,
B: Mm - hmm .
F: and just simply the keystrokes it takes to negotiate , to put the boundaries in , to to type it in , i it 's just a huge amount of time .
B: Mm - hmm .
E: Oops .
C: Yeah .
A: Wh - what F: And you wanna be sure it 's used ,
F: and you wanna be sure it 's done as efficiently as possible ,
F: and if it can be done automatically , that would be ideal .
A: what if you put it in but didn't put the boundaries ?
F: Well , but A: So you just know it 's between these other things ,
F: Well , OK . So now there 's there 's another another possibility
A: right ?
F: which is , um , the time boundaries could mark off words <comment> from nonwords .
F: And that would be extremely time - effective , if that 's sufficient .
A: Yeah I mean I 'm think if it 's too if it 's too hard for us to annotate the breaths per se , we are gonna be building up models for these things
A: and these things are somewhat self - aligning ,
A: so if so , we i i if we say there is some kind of a thing which we call a " breath " or a " breath - in " or " breath - out " , the models will learn that sort of thing .
A: Uh , so but you but you do want them to point them at some region where where the breaths really are .
F: OK . But that would maybe include a pause as well ,
A: So G: Well , there 's a there 's F: and that wouldn't be a problem to have it , uh , pause plus breath plus laugh plus sneeze ?
A: Yeah , i
A: You know there is there 's this dynamic tension between between marking absolutely everything , as you know ,
A: and and and
A: marking just a little bit and counting on the statistical methods .
A: Basically the more we can mark the better .
A: But if there seems to be a lot of effort for a small amount of reward in some area ,
A: and this might be one like this A: Although I I I 'd be interested to h get get input from Liz and Andreas on this to see if they A: Cuz they 've - they 've got lots of experience with the breaths in in , uh , uh , their transcripts .
G: I B: They have lots of experience with breathing ?
A: Actually A: Well , yes they do ,
A: but we  we can handle that without them here .
A: But but but , uh ,
A: you were gonna say something about G: Yeah , I I think , um , one possible way that we could handle it is that ,
G: um ,
G: you know , as the transcribers are going through , and if they get a hunk of speech that they 're gonna transcribe ,
G: u th they 're gonna transcribe it because there 's words in there or whatnot .
G: If there 's a breath in there , they could transcribe that .
E: Yeah .
F: That 's what they 've been doing .
E: Yeah .
G: Right .
F: So , within an overlap segment , they they do this .
G: But G: Right . But if there 's a big hunk of speech , let 's say on Morgan 's mike where he 's not talking at all , um , don't don't worry about that .
E: Yeah .
G: So what we 're saying is , there 's no guarantee that , um G: So for the chunks that are transcribed , everything 's transcribed .
G: But outside of those boundaries , there could have been stuff that wasn't transcribed .
G: So you just somebody can't rely on that data and say " that 's perfectly clean data " .
G: Uh do you see what I 'm saying ?
F: Yeah , you 're saying it 's uncharted territory .
G: So I would say don't tell them to transcribe anything that 's outside of a grouping of words .
A: That sounds like a reasonable reasonable compromise .
E: Yeah , and that 's that that quite co corresponds to the way I I try to train the speech - nonspeech detector ,
E: as I really try to not to detect those breaths
E: which are not within a speech chunk but with which are just in in a silence region .
A: Yeah .
E: And they so they hopefully won't be marked in in those channel - specific files .
G: Yeah , so E: But A: u I I wanted to comment a little more just for clarification about this business about the different purposes .
F: Mm - hmm .
A: See , in a in a way this is a really key point ,
A: that for speech recognition , uh , research ,
A: uh , um ,
A: e a it 's not just a minor part .
A: In fact , the I think I would say the core thing that we 're trying to do is to recognize the actual , meaningful components in the midst of other things that are not meaningful .
A: So it 's critical A: it 's not just incidental
A: it 's critical for us to get these other components that are not meaningful .
A: Because that 's what we 're trying to pull the other out of .
A: That 's our problem .
A: If we had nothing F: Yeah .
A: if we had only linguistically - relevant things A: if if we only had changes in the spectrum that were associated with words , with different spectral components ,
A: and , uh , we we didn't have noise ,
A: we didn't have convolutional errors ,
A: we didn't have extraneous , uh , behaviors , and so forth ,
A: and moving your head
A: and all these sorts of things ,
A: then , actually speech recognition i i isn't that bad right now .
A: I mean you can
C: Yeah .
A: you know it 's  it 's the technology 's come along pretty well .
A: The the the reason we still complain about it is because is when when you have more realistic conditions then then things fall apart .
F: OK , fair enough .
F: I guess , um , I uh , what I was wondering is what what at what level does the breathing aspect enter into the problem ?
F: Because if it were likely that a PDA would be able to be built which would get rid of the breathing , so it wouldn't even have to be processed at thi at this computational le
F: well , let me see , it 'd have to be computationally processed to get rid of it ,
F: but if there were , uh , like likely on the frontier , a good breath extractor
F: then , um , and then you 'd have to A: But that 's a research question ,
A: you know ?
A: And so F: Yeah , well , see and that 's what I wouldn't know .
A: that A: And we don't either .
A: I mean so so the thing is it 's it A: right now it 's just raw d it 's just data that we 're collecting ,
A: and so we don't wanna presuppose that people will be able to get rid of particular degradations
A: because that 's actually the research that we 're trying to feed .
F: OK .
A: So , you know , an and maybe maybe in five years it 'll work really well ,
A: and and it 'll only mess - up ten percent of the time ,
A: but then we would still want to account for that ten percent ,
F: I guess there 's another aspect
A: so .
F: which is that as we 've improved our microphone technique , we have a lot less breath in the in the more recent , uh , recordings ,
F: so it 's in a way it 's an artifact that there 's so much on the on the earlier ones .
A: Uh - huh .
A: I see .
G: One of the G: um , just to add to this G: one of the ways that we will be able to get rid of breath is by having models for them .
G: I mean , that 's what a lot of people do nowadays .
B: Right .
A: Right .
G: And so in order to build the model you need to have some amount of it marked ,
C: Yeah .
G: so that you know where the boundaries are .
C: Hmm .
A: Yeah .
G: So G: I mean , I don't think we need to worry a lot about breaths that are happening outside of a , you know , conversation .
G: We don't have to go and search for them to to mark them at all ,
G: but , I mean , if they 're there while they 're transcribing some hunk of words , I 'd say put them in if possible .
F: OK , and it 's also the fact that they differ a lot from one channel to the other
F: because of the way the microphone 's adjusted .
C: Yeah .
G: Mm - hmm .
F: OK .
A: Should we do the digits ?
B: Yep .
B: OK .
C: OK .
D: Mmm . Alright .