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