grad b: We didn't crash we 're not crashing anymore phd c: One , two , three , four , f grad b: and it really bothers me . phd c: Yeah ? professor a: Oh ! Well maybe it 's just , you know , how many t u u u u how many times you crash in a day . phd g: Or maybe it 's once you 've done enough meetings it won't crash on you anymore . professor a: Do we have an agenda ? Liz Liz and Andreas can't sh can't  , can't come . phd g: Did they send , the messages to you about the meeting today ? grad b: I have no idea but I just got it a few minutes ago . grad b: So , does anyone have any a agenda items other than me ? I actually have one more also which is to talk about the digits . professor a: right , so so I I was just gonna talk briefly about the NSF ITR . professor a: and then , you have postdoc f: Can w professor a: I won't say much , but  , but then , you said wanna talk about digits ? grad b: I have a short thing about digits and then  I wanna talk a little bit about naming conventions , although it 's unclear whether this is the right place to talk about it . So maybe just talk about it very briefly and take the details to the people who for whom it 's relevant . I 've been but but  , well professor a: Well if we Yeah , we shouldn't add things in just to add things in . grad b: So the only thing I wanna say about digits is , we are pretty much done with the first test set . There are probably forms here and there that are marked as having been read that weren't really read . So I won't really know until I go through all the transcriber forms and extract out pieces that are in error . The first is what should we do about digits that were misread ? My opinion is , we should just throw them out completely , and have them read again by someone else . grad b: so it it 's perfectly fine to put a a group together again of errors and have them re - read , just to finish out the test set . postdoc f: Oh ! By throw them out completely ? grad b: the other thing you could do is change the transcript to match what they really said . I know I 've done it , where I say say a grad b: What the transcribers did with that is if they did a correction , and they eventually did read the right string , you extract the right string . phd g: Oh , you 're talking about where they completely read the wrong string and didn't correct it ? phd e: Yeah . grad b: So so postdoc f: Well , and s and you 're talking string - wise , you 're not talking about the entire page ? grad b: Correct . grad b: And so the the two options are change the transcript to match what they really said , but then but then the transcript isn't the Aurora test set anymore . phd g: Well how many are how how often does that happen ? grad b: Mmm , five or six times . professor a: Yeah , it 's five or six times out of thousands ? phd c: Yeah . phd g: Yeah , it 's professor a: Yeah , I would , tak do the easy way , phd g: Yeah . professor a: It it 's kinda nice  , wh who knows what studies people will be doing on on speaker - dependent things phd c: Mmm . phd g: So you  , how many digits have been transcribed now ? grad b: Four thousand lines . phd g: Four thousand lines ? grad b: I didn't I didn't compute the average . grad b: And , Jane , I do have a set of forms which I think you have copies of somewhere . grad b: Yeah , I was just wond I thought I had had all of them back from you . And then the other thing is that , the forms in front of us here that we 're gonna read later , were suggested by Liz postdoc f: No , not yet . And so , I just wanted people to , take a quick look at the instructions phd c:   grad b: and the way it wa worked and see if it makes sense and if anyone has any comments on it . Although we could switch it back and tell them always to say " zero " or always to say " O " . professor a: that 's the only thought I have because if you t start talking about these , you know u tr She 's trying to get at natural groupings , but it there 's there 's nothing natural about reading numbers this way . grad b: But , the other problem we were thinking about is if you just put the numerals , they might say forty - three instead of four three .  , you can With when you space them out they don't look like , forty - three anymore . grad b: and she felt that it 's very , very natural to do that sort of chunking .  it 's a it 's a it 's an interesting problem  , we 've done stuff with numbers before , and yeah sometimes people If you say s " three nine eight one " sometimes people will say " thirty - nine eighty - one " or " three hundred three hundred eighty - nine one " , or I don't think they 'd say that , phd c: Yeah . professor a: but but th grad b: Not very frequently professor a: no grad b: but , they certainly could . grad b: and , since this was something that Liz asked for specifically , I think we need to defer to her . Well , we 're probably gonna be collecting meetings for a while and if we decide we still wanna do some digits later we might be able to do some different ver different versions , grad b: Do something different , professor a: but this is the next suggestion , grad b: yeah . OK , so  e l I guess , let me , get my my short thing out about the NSF .  , I I sent to what I thought we had , in some previous mail , as the right joint thing to send to , which was " M MTG RCDR hyphen joint " . professor a: But then I got some sort of funny mail saying that the moderator was going to grad b: It 's That 's because they set the one up at UW postdoc f:  grad b: that 's not on our side , that 's on the U - dub side . grad b: And , I have no idea whether it actually ever goes to anyone so you might just wanna mail to Mari professor a: No no , th I got I got , little excited notes from Mari and Jeff and so on , grad b: and professor a: so it 's grad b: OK , good . grad b: Cuz I had sent one earlier Actually the same thing happened to me I had sent one earlier . The message says , " You 'll be informed " and then I was never informed but I got replies from people indicating that they had gotten it , so . Well , anyway , I guess everybody here Are y are you are on that list , right ? So you got the note ? phd g:   professor a: so this was , a , proposal that we put in before on on more more higher level , issues in meetings , from I guess higher level from my point of view .  , and , meeting mappings , and ,  so is i for it was a proposal for the ITR program , Information Technology Research program 's part of National Science Foundation . They 're they 're a lot of them are some of them anyway , are larger larger grants than the usual , small NSF grants , and . So , they 're very competitive , and they have a first phase where you put in pre - proposals , and we we , got through that . grad b: When 's when 's the full proposal due ? professor a: I think April ninth , or something . And they said end of business day you could check on the reviewer forms , phd g: u grad b: is that phd g: Tomorrow . grad b: I guess that 's a good thing cuz that way I got my papers done early . phd g: It would be interesting professor a: So that 's amazing you showed up at this meeting ! grad b: It is . My favorite is was when when when one reviewer says , " you know , this should be far more detailed " , and the nex the next reviewer says , " you know , there 's way too much detail " . Or " this is way too general " , and the other reviewer says , " this is way too specific " . Is that right ? That they didn't reject a lot of the pre - proposals ? professor a: Do you know anything about the numbers ? grad b: No . phd g: Gary Strong 's professor a: I phd g: there was a sentence at the end of one of his paragraphs phd e: Yeah . He said the next phase 'll be very , competitive phd e: Very very , phd g: because we didn't want to weed out much in the first phase . professor a: So , you know , maybe they didn't r weed out as much as usual , but it 's it 's usually a pretty But it Yeah . It 's it 's certainly not I 'm sure that it 's not down to one in two or something of what 's left . professor a: I 'm sure it 's , you know grad b: How how many awards are there , do you know ? professor a: Well there 's different numbers of w awards for different size They have three size grants . This one there 's ,  See the small ones are less than five hundred thousand total over three years and that they have a fair number of them .  , and the large ones are , boy , I forget , I think , more than , more than a million and a half , more than two million or something like that . But , I don't remember , but it 's pr probably along the li I I could be wrong on this yeah , but probably along the lines of fifteen or that they 'll fund , or twenty .  when they Do you do you know how many they funded when they f in in Chuck 's , that he got last year ? phd g: I don't I don't know . grad b: I thought it was smaller , that it was like four or five , wasn't it ? professor a: Well they fund phd g: I I 'm professor a: they phd g: I don't remember . professor a: and this time they came up with a middle one , so it 'll there 'll be more of them that they fund than of the big . phd g: If we end up getting this , what will it mean to ICSI in terms of , w wh where will the money go to , what would we be doing with it ? professor a:  . professor a: You know , it i None of it will go for those yachts that we 've talking about . phd g:  Dang ! professor a: well , no ,  it 's u It phd g: It 's just for the research to continue the research on the Meeting Recorder stuff ? professor a: It 's extending the research , right ? Because the other phd g: Yeah . grad b: Yeah it 's go higher level stuff than we 've been talking about for Meeting Recorder . Yeah the other things that we have , been working on with , the c with Communicator  , especially with the newer things with the more acoustically - oriented things are are are are lower level . And , this is dealing with , mapping on the level of of , the conversation of mapping the conversations phd g:   So it 's all it 's all stuff that none none of us are doing right now , or none of us are funded for , so it 's so it 's it would be new . phd g: So assuming everybody 's completely busy now , it means we 're gonna hafta , hire more students , or , something ? professor a: Well there 's evenings , and there 's weekends , and  . Yeah , there there would be there would be new hires , and and there there would be expansion , but , also , there 's always for everybody there 's there 's always things that are dropping off , grants that are ending , or other things that are ending , so , phd g: Right . professor a: But but there definitely would be new new new , students , phd g: I see . grad b: Are there any students in your class who are expressing interest ? professor a: not clear yet . professor a:  we got we have yeah , two of them are two in the c There 're two in the class already here , and then and and , then there 's a third who 's doing a project here , who ,  But he he he won't be in the country that long , grad b:   professor a: Actually there is one other guy who 's looking that that 's that guy , Jeremy ? I think .  professor a: Anyway , yeah that 's that 's all I was gonna say is that that that 's you know , that 's nice and we 're sorta preceding to the next step , and , it 'll mean some more work , you know , in in March in getting the proposal out , and then , it 's , you know We 'll see what happens . It just ,  we 've been cutting up sound files , in for ba both digits and for , doing recognition . And Liz had some suggestions on naming and it just brought up the whole issue that hasn't really been resolved about naming . So , one thing she would like to have is for all the names to be the same length so that sorting is easier . grad b: same number of characters so that when you 're sorting filenames you can easily extract out bits and pieces that you want . And I don't think we have so many meetings that that 's a big deal just to change the names . So that means , instead of calling it " MR one " , " MR two " , you 'd call it " MRM zero zero one " , " MRM zero zero two " , things like that . postdoc f: But , you know , when you , do things like that you can always as long as you have  , you can always search from the beginning or the end of the string . postdoc f: You know , so " zero zero two " grad b: Alright , postdoc f: Yeah . grad b: so we we have th we 're gonna have the speaker ID , the session , information on the microphones , postdoc f: Yeah , well , your example was really grad b: information on the speak on the channels and all that . postdoc f: i grad b: And so if each one of those is a fixed length , the sorting becomes a lot easier . So like , the NSA meeting lengths , all filenames are gonna be the same length as the Meeting Recorder meeting names ? grad b: Yep . And as I said , the it 's we just don't have that many that that 's a big deal . grad b: And so , at some point we have to sort of take a few days off , let the transcribers have a few days off , make sure no one 's touching the data and reorganize the file structures . postdoc f: I I would think though that the transcribe the transcripts themselves wouldn't need to have such lengthy names . postdoc f: So , you 're dealing with a different domain there , and with start and end times and all that , and channels and stuff , grad b: Right . So the only thing that would change with that is just the directory names , postdoc f: so , it 's a different set . grad b: So for for m the meetings we were thinking about three letters and three numbers for meeting I Ds .  , for speakers , M or F and then three numbers , For ,  and , that also brings up the point that we have to start assembling a speaker database so that we get those links back and forth and keep it consistent . We want some way of specifying , more than looking in the " key " file , what channel and what mike . grad b: that 's gonna become especially important once we start changing the microphone set - up . We have some new microphones that I 'd like to start trying out , once I test them . professor a: since we have such a short agenda list I guess I wi I will ask how how are the transcriptions going ? Yeah . postdoc f: The the news is that I 've I  s So in s  So I 've switched to Start my new sentence . I I switched to doing the channel - by - channel transcriptions to provide , the  , tighter time bins for partly for use in Thilo 's work and also it 's of relevance to other people in the project . And , I discovered in the process a couple of of interesting things , which , one of them is that , it seems that there are time lags involved in doing this , using an interface that has so much more complexity to it . And I and I wanted to maybe ask , Chuck to help me with some of the questions of efficiency . Maybe I was thinking maybe the best way to do this in the long run may be to give them single channel parts and then piece them together later .  , so it 's like , I I know that I can take them apart and put them together and I 'll end up with the representation which is where the real power of that interface is . postdoc f: And it may be that it 's faster to transcribe a channel at a time with only one , sound file and one , set of of , utterances to check through . I thought that that one of the reason we thought we were so much faster than than , the the other transcription , thing was that that we were using the mixed file . But , with the mixed , when you have an overlap , you only have a a choice of one start and end time for that entire overlap , which means that you 're not tightly , tuning the individual parts th of that overlap by different speakers . postdoc f: So someone may have only said two words in that entire big chunk of overlap . postdoc f: And for purposes of of , things like well , so things like training the speech - nonspeech segmentation thing . postdoc f: And w and w and , you know , is a It would be wonderful if , it 's possible then to use that algorithm to more tightly tie in all the channels after that but , you know , I 've th the So , I I don't know exactly where that 's going at this point . But m I was experimenting with doing this by hand and I really do think that it 's wise that we 've had them start the way we have with , m y working off the mixed signal , having the interface that doesn't require them to do the ti  , the time bins for every single channel at a t  , through the entire interaction . postdoc f: I did discover a couple other things by doing this though , and one of them is that , once in a while a backchannel will be overlooked by the transcriber . postdoc f: because when it 's a b backchannel could well happen in a very densely populated overlap . And if we 're gonna study types of overlaps , which is what I wanna do , an analysis of that , then that really does require listening to every single channel all the way through the entire length for all the different speakers . Now , for only four speakers , that 's not gonna be too much time , but if it 's nine speakers , then that i that is more time . So it 's li you know , kind of wondering And I think again it 's like this it 's really valuable that Thilo 's working on the speech - nonspeech segmentation because maybe , we can close in on that wi without having to actually go to the time that it would take to listen to every single channel from start to finish through every single meeting .  especially if they 're really short and they 're not very loud and so it it can it it will always happen that also the automatic s detection system will miss some of them , so . Well so then then , maybe the answer is to , listen especially densely in places of overlap , phd e: Yeah . postdoc f: just so that they 're they 're not being overlooked because of that , and count on accuracy during the sparser phases . postdoc f: Cuz there are large s spaces of the That 's a good point . I I think it 's really interesting data to work with , I have to say , it 's very enjoyable .  , you 're you 're you 're still in the midst of what you 're doing from what you described last time , I assume , phd c: Is true . postdoc f:  professor a: and phd c: I haven't results , eh , yet professor a: Yeah . phd c: but , eh , I I 'm continue working with the mixed signal now , after the the last experience . phd c: And and I 'm tried to to , adjust the to to improve , eh , an harmonicity , eh , detector that , eh , I I implement . phd c: But I have problem because , eh , I get , eh , eh , very much harmonics now . phd c: harmonic possi possible harmonics , eh , and now I 'm I 'm I 'm trying to to find , eh , some kind of a ,  of h of help , eh , using the energy to to distinguish between possible harmonics , and and other fre frequency peaks , that , eh , corres not harmonics . And , eh , I have to to talk with y with you , with the group , eh , about the instantaneous frequency , because I have , eh , an algorithm , and , I get , mmm , eh , t t results similar results , like , eh , the paper , eh , that I I am following . But , eh , the the rules , eh , that , eh , people used in the paper to to distinguish the harmonics , is doesn't work well . phd c: And I I I I not sure that i eh , the the way o to ob the way to obtain the the instantaneous frequency is right , or it 's it 's not right . If if if ,  If I don't have enough time and y you wanna discuss with someone else some someone else besides us that you might want to talk to , might be Stephane . phd c: and , professor a: Yeah , but phd c: they nnn they they they didn't phd e: I 'm not too experienced with harmonics professor a: I see . phd c: they think that the experience is not enough to phd e: and phd g: Is is this the algorithm where you hypothesize a fundamental , and then get the energy for all the harmonics of that fundamental ? phd c: No , no it 's No No . phd g: And then hypothesize a new fundamental and get the energy professor a: Yeah , that 's wh phd c: No . phd c: And The algorithm said that , eh , if you if you change the the the , eh , nnn the X - the frequency " X " , eh , using the in the instantaneous frequency , you can find , eh , how , eh , in several frequencies that proba probably the the harmonics , eh , professor a:  - huh . phd c: the errors of peaks the frequency peaks , eh , eh , move around these , eh eh frequency harmonic the frequency of the harmonic . And , eh , if you if you compare the the instantaneous frequency , eh , of the of the , eh , continuous , eh , eh , filters , that , eh that , eh , they used eh , to to to get , eh , the the instantaneous frequency , professor a:   phd c: it probably too , you can find , eh , that the instantaneous frequency for the continuous , eh , eh the output of the continuous filters are very near . phd c: And professor a: It 's it 's it 's I haven't worked with that either so I 'm not sure The way the simple - minded way I suggested was what Chuck was just saying , is that you could make a a sieve . professor a: Let 's let 's hypothesize that it 's this frequency or that frequency , and and , maybe you maybe you could use some other cute methods to , short cut it by by  , making some guesses , phd c: Yeah . professor a: but but    , I would ,   you could make some guesses from ,  from the auto - correlation or something but but then , given those guesses , try , only looking at the energy at multiples of the of that frequency , and and see how much of the take the one that 's maximum . phd g: Do you hafta do some kind of , low - pass filter before you do that ? phd c: I don't use . phd g: Or phd c: But , I I know many people use , eh , low - pass filter to to to get , eh , the pitch . phd g: But i But the harmonics are gonna be , I don't know what the right word is .  , they 're gonna be dampened by the  , vocal tract , right ? The response of the vocal tract . professor a: Yeah ? phd c: Yeah ? phd g: And so just looking at the energy on those at the harmonics , is that gonna ? professor a: Well so the thing is that the This is for , a ,  phd g: I m what you 'd like to do is get rid of the effect of the vocal tract . professor a: But ,  but I but but I don't know that you need to grad b: Open wide ! professor a: but I don't need you know if you need to get rid of it .  that 'd that 'd be nice but I don't know if it 's ess if it 's essential .  ,  cuz I think the main thing is that , you 're trying phd g:  - huh . professor a: wha what are you doing this for ? You 're trying distinguish between the case where there is ,  where where there are more than  , where there 's more than one speaker and the case where there 's only one speaker . professor a: So if there 's more than one speaker ,  yeah I guess you could I guess yeah you 're so you 're not distinguished between voiced and unvoiced , so so , i if you don't if you don't care about that phd c: Yeah . professor a: See , if you also wanna just determine if you also wanna determine whether it 's unvoiced , then I think you want to look look at high frequencies also , because the f the fact that there 's more energy in the high frequencies is gonna be an ob sort of obvious cue that it 's unvoiced . professor a: But , i i   i i but , other than that I guess as far as the one person versus two persons , it would be primarily a low frequency phenomenon . And if you looked at the low frequencies , yes the higher frequencies are gonna there 's gonna be a spectral slope .  that 's that 's w phd c: I will prepare for the next week eh , all my results about the harmonicity and will will try to come in and to discuss here , because , eh , I haven't enough feeling to to u many time to to understand what happened with the with , eh , so many peaks , eh , eh , and I I see the harmonics there many time but , eh , there are a lot of peaks , eh , that , eh , they are not harmonics . phd c: I have to discover what what is the the w the best way to to to c to use them professor a: Well , but yeah I don't think you can  you 're not gonna be able to look at every frame , so  I  I I really I I really thought that the best way to do it , and I 'm speaking with no experience on this particular point , but , my impression was that the best way to do it was however you You 've used instantaneous frequency , whatever . However you 've come up you with your candidates , you wanna see how much of the energy is in that phd c: Yeah . And , if it 's voiced , I guess so so y I think maybe you do need a voiced - unvoiced determination too . professor a: and the ,  e the fraction of the energy that 's in the harmonic sequence that you 're looking at is relatively low , then it should be then it 's more likely to be an overlap . This this is the idea the idea I I I had to to compare the the ratio of the the energy of the harmonics with the eh , with the , eh , total energy in the spectrum and try to get a ratio to to distinguish between overlapping and speech . professor a: But you 're looking a y you 're looking at Let 's take a second with this .  , you 're looking at f at the phase derivative , in in , what domain ?  this is this is in in in in bands ? Or or phd c: No , no , no . professor a: Just just overall phd c: It 's a it 's a o i w the band the band is , eh , from zero to to four kilohertz . And I I ot I professor a: And you just take the instantaneous frequency ? phd c: Yeah . to FFT to to obtain the or to study the harmonics from from the spectrum directly , professor a: Yeah . And another another algorithm I have is the in the instantaneous frequency , based on on on the FFT to to to calculate the the phase derivate in the time . phd c: But , eh , in m i in my opinion the the the instantaneous frequency , the the the behavior , eh , was th it was very interesting . Because I I saw eh , how the spectrum concentrate , eh , professor a: Oh ! phd c: around the the harmonic . But then when I apply the the rule , eh , of the in the the instantaneous frequency of the ne of the continuous filter in the the near filter , the the rule that , eh , people propose in the paper doesn't work . professor a: But the instantaneous frequency , wouldn't that give you something more like the central frequency of the you know , of the where most of the energy is ?  , I think if you Does i does it Why would it correspond to pitch ? phd c: Yeah . phd c: When first I I calculate , eh , using the FFT , postdoc f: Di - digital camera . professor a: Oh , so you scale you s you do a a scaling along that axis according to instantaneous phd c: I use Yeah . phd c: eh , when i I I use these these frequency , eh , the range is different , and the resolution is different . phd c: But , eh , they used , eh , a rule , eh , based in the in the because to to calculate the instantaneous frequency , they use a Hanning window . 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 . I 'll 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 . postdoc f: I I did i it did occur to me that this is  , 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 , 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 , aside from the fact that they 're obviously very time - consuming to encode , 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 , 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 ,  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 . 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 . postdoc f: Well , except that these are really truly  , 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  , breath is natural , but not professor a: It is but it is if you record it . postdoc f: Except that we 're we 're trying to mimic Oh , I see what you 're saying . 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 . 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 . 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  the  the the fan and the in the projector up there , and , this is it 's this is actual stuff that we we wanna work with . postdoc f: because i it basically has a i it shows very clearly the contrast between , speech recognition research and discourse research because in in discourse and linguistic research , what counts is what 's communit communicative . postdoc f: And breath , you know , everyone breathes , they breathe all the time . 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 . postdoc f: And the one that 's used for speech recognition will be processed via scripts . Discourse side will have this this side over he the we we 'll have a s ch Sorry , not being very fluent here . But , this the discourse side will have a script which will stri strip away the things which are non - communicative . 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: 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 . 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 , the time boundaries could mark off words from nonwords . professor a: Yeah  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 .  , so but you but you do want them to point them at some region where where the breaths really are . 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 , 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 . 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 , 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 , you were gonna say something about phd g: Yeah , I I think , one possible way that we could handle it is that , 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 . But if there 's a big hunk of speech , let 's say on Morgan 's mike where he 's not talking at all , don't don't worry about that . phd g: So what we 're saying is , there 's no guarantee that ,  So for the chunks that are transcribed , everything 's transcribed . So you just somebody can't rely on that data and say " that 's perfectly clean data " .  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 . 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 . 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 . professor a: See , in a in a way this is a really key point , that for speech recognition , research , 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 . 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 , we we didn't have noise , we didn't have convolutional errors , we didn't have extraneous , 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 . 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 . I guess , I  , 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 , like likely on the frontier , a good breath extractor then , 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 .  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 , recordings , so it 's in a way it 's an artifact that there 's so much on the on the earlier ones . phd g: One of the  , 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 . 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 g: So  , 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 , 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 <doc-sep>But all all I know is that it seems like every time I am up here after a meeting , and I start it , it works fine . And if I 'm up here and I start it and we 're all sitting here waiting to have a meeting , it gives me that error message and I have not yet sat down with been able to get that error message in a point where I can sit down and find out where it 's occurring in the code . professor d: So  so the  , the new procedural change that just got suggested , which I think is a good idea is that  , we do the digit recordings at the end . And that way , if we 're recording somebody else 's  meeting , and a number of the participants have to run off to some other meeting and don't have the time , then they can run off . It 'll mean we 'll get somewhat fewer  , sets of digits , but  , I think that way we 'll cut into people 's time , if someone 's on strict time  , less .  , so , let 's see , we were having a discussion the other day , maybe we should bring that up , about  , the nature of the data that we are collecting .  @ @ that  , we should have a fair amount of data that is  , collected for the same meeting , so that we can ,  I don't know . Wh - what what were some of the points again about that ? Is it phd f: well , OK , I 'll back up . phd f: at the previous at last week 's meeting , this meeting I was griping about wanting to get more data and I I talked about this with Jane and Adam , and was thinking of this mostly just so that we could do research on this data  , since we 'll have a new this new student di does wanna work with us , phd a: Well , great . phd f: And he 's already funded part - time , so we 'll only be paying him for sort of for half of the normal part - time , phd a: What a deal . grad b: And what 's he interested in , specifically ? phd f: So he 's comes from a signal - processing background , but I liked him a lot cuz he 's very interested in higher level things , like language , and disfluencies and all kinds of eb maybe prosody , grad b:   Anyway , I thought OK , maybe we should have enough data so that if he starts he 'd be starting in January , next semester that we 'd have , you know , enough data to work with . phd f: But , Jane and Adam brought up a lot of good points that just posting a note to Berkeley people to have them come down here has some problems in that you m you need to make sure that the speakers are who you want and that the meeting type is what you want , and so forth . So , I thought about that and I think it 's still possible , but I 'd rather try to get more regular meetings of types that we know about , and hear , then sort of a mish - mosh of a bunch of one one - time grad b: One offs ? phd f: Yeah , just because it would be very hard to process the data in all senses , both to get the ,  to figure out what type of meeting it is and to do any kind of higher level work on it , like well , I was talking to Morgan about things like summarization , or what 's this meeting about .  it 's very different if you have a group that 's just giving a report on what they did that week , versus coming to a decision and so forth . Then I was  , talking to Morgan about some new proposed work in this area , sort of a separate issue from what the student would be working on where I was thinking of doing some kind of summarization of meetings or trying to find cues in both the utterances and in the utterance patterns , like in numbers of overlaps and amount of speech , sort of raw cues from the interaction that can be measured from the signals and from the diff different microphones that point to sort of hot spots in the meeting , or things where stuff is going on that might be important for someone who didn't attend to listen to . And in that  , regard , I thought we definitely w will need it 'd b it 'd be nice for us to have a bunch of data from a few different domains , or a few different kinds of meetings . So this this meeting is one of them , although I 'm not sure I can participate if I You know , I would feel very strange being part of a meeting that you were then analysing later for things like summarization . phd f: and then there are some others that menti that Morgan mentioned , like the front - end meeting and maybe a networking group meeting . phd f: But basically , for anything where you 're trying to get a summarization of some kind of meeting meaning out of the meeting , it would be too hard to have fifty different kinds of meetings where we didn't really have a good grasp on what does it mean to summarize , grad b: Yeah . phd f: but rather we should have different meetings by the same group but hopefully that have different summaries . And then we need a couple that of We don't wanna just have one group because that might be specific to that particular group , but @ @ three or four different kinds . So , in general , I was thinking more data but also data where we hold some parameters constant or fairly similar , grad b:   phd f: like a meeting about of people doing a certain kind of work where at least half the participants each time are the same . grad b:  professor d: Now , let l l let me just give you the other side to that cuz I ca because I I don't disagree with that , but I think there is a complimentary piece to it too . professor d: As many people here a a and talking about the kind of thing that you were just talking about it would have  too few people from my point of view . So ,  I think I would also very much like us to have a fair amount of really random scattered meetings , of somebody coming down from campus , and and  , phd c:   professor d: sure , if we can get more from them , fine , postdoc e:   professor d: but if we only get one or two from each group , that still could be useful acoustically just because we 'd have close and distant microphones with different people . postdoc e: Can I can I say about that that the the issues that I think Adam and I raised were more a matter of advertising so that you get more native speakers . Because I think if you just say an And in particular , my suggestion was to advertise to linguistics grad students because there you 'd have so people who 'd have proficiency enough in English that that  , it would be useful for for purposes You know . postdoc e: But you know , I think I 've been I 've I I 've gathered data from undergrads at on campus and if you just post randomly to undergrads I think you 'd get such a mixed bag that it would be hard to know how much conversation you 'd have at all . And and the English you 'd have The language models would be really hard to build professor d: Well , you want to i postdoc e: because it would not really be it would be an interlanguage rather than than a professor d: Well , OK , first place , I I I don't think we 'd just want to have random people come down and talk to one another , I think there should be a meeting that has some goal and point cuz I I think that 's what we 're investigating , postdoc e: OK . phd f: It has to be a a pre - existing meeting , like a meeting that would otherwise happen anyway . professor d: So I was I was thinking more in terms of talking to professors  , and and and  , senior  , d and  , doctoral students who are leading projects and offering to them that they have their hold their meeting down here . The second point is  I think that for some time now , going back through BeRP I think that we have had speakers that we 've worked with who had non - native accents and I th I think that postdoc e: Oh , oh . postdoc e: No , it 's more a matter of  , proficiency , e e just simply fluency . postdoc e: I deal with people on on campus who I think sometimes people , undergraduates  in computer science  , have language skills that make , you know that their their fluency and writing skills are not so strong . professor d: You 're just talking about postdoc e: Well , e I just think , grad b: We all had the same thought . postdoc e: but you know , it 's like when you get into the graduate level , no problem . professor d: But grad b: Well , I think that ,  I think that the only thing we should say in the advertisement is that the meeting should be held in English . And and I think if it 's a pre - existing meeting and it 's held in English , I I think it 's probably OK if a few of the people don't have  , g particularly good English skills . postdoc e: OK , now can I can I say the other aspect of this from my perspective which is that  , there 's there 's this this issue , you have a corpus out there , it should be used for for multiple things cuz it 's so expensive to put together . postdoc e: And if people want to approach  , i so I know e e You know this The idea of computational linguistics and probabilistic grammars and all may not be the focus of this group , professor d:  - huh . postdoc e: but the idea of language models , which are fund you know generally speaking  , you know , t t terms of like the amount of benefit per dollar spent or an hour invested in preparing the data , professor d:   postdoc e: if you have a choice between people who are pr more proficient in  , i more fluent , more more close to being academic English , then it would seem to me to be a good thing . I postdoc e: Because otherwise y you don't have the ability to have  , so if if you have a bunch of idiolects that 's the worst possible case . If you have people who are using English as a as an interlanguage because they they don't  , they can't speak in their native languages and but their interlanguage isn't really a match to any existing , language model , professor d:  - huh . professor d: Well , that 's pretty much what you 're going to have in the networking group . But the thing is , I think that these people are of high enough level in their in their language proficiency that professor d: I see . postdoc e: I I 'm I 'm just thinking that we have to think at a at a higher level view , could we have a language model , a a grammar a grammar , basically , that  , wo would be a a possibility . postdoc e: So y so if you wanted to bring in a model like Dan Jurafsky 's model , an and do some top - down stuff , it to help th the bottom - up and merge the things or whatever , it seems like  , I don't see that there 's an argument professor d:   postdoc e: I 'm I what I think is that why not have the corpus , since it 's so expensive to put together , useful for the widest range of of central corp things that people generally use corpora for and which are , you know , used in computational linguistics . professor d: OK , well , i i let 's let 's see what we can get .  , it it I think that if we 're aiming at at  , groups of graduate students and professors and so forth who are talking about things together , and it 's from the Berkeley campus , probably most of it will be OK , postdoc e: Yes , that 's fine . And my point in m in my note to Liz was I think that undergrads are an iff iffy population . grad b: Well , not to mention the fact that I would be hesitant certainly to take anyone under eighteen , probably even an anyone under twenty - one . professor d: Oh , you age - ist ! grad b: What 's that ? Well , age - ist . Well , Morgan , you were mentioning that Mari may not use the k equipment from IBM if they found something else , cuz there 's a professor d: They 're they 're yeah , they 're d they 're  assessing whether they should do that or y do something else , hopefully over the next few weeks . phd f: Cuz  , one remote possibility is that if we st if we inherited that equipment , if she weren't using it , could we set up a room in the linguistics department ? And and  , there there may be a lot more or or in psych , or in comp wherever , in another building where we could  , record people there . I think we 'd have a better chance grad b: I think we 'd need a real motivated partner to do that . But if there were such a  it 's a remote possibility , then  , you know , one of us could you know , go up there and record the meeting or something rather than bring all of them down here . phd f: So it 's just a just a thought if they end up not using the the hardware . professor d: Well , the other thing Yeah ,  the other thing that I was hoping to do in the first place was to turn it into some kind of portable thing so you could wheel it around .  , and grad b: Well , I know that space is really scarce on at least in CS . phd f: But you may not need a separate room , you know , grad b: That 's true . phd f: the idea is , if they have a meeting room and they can guarantee that the equipment will be safe and so forth , and if one of us is up there once a week to record the meeting or something grad b: True . professor d: Well , maybe John would let us put it into the phonology lab or something . grad b: Yeah , I think it would be interesting because then we could regularly get another meeting . phd c: But I I I think you need , another portable thing a another portable equipment to to do , eh , more e easier the recording process , eh , out from ICSI . phd c: Eh , if you you want to to record , eh , a seminar or a class , eh , in the university , you you need It - it would be eh eh very difficult to to put , eh , a lot of , eh , head phones eh in different people when you have to to record only with , eh , this kind of , eh , d device . grad b: Yeah , but I think if we if we wanna just record with the tabletop microphones , that 's easy . grad b: Right ? That 's very easy , phd c: Ye - Yeah , yeah . professor d: Actually , that 's a int that raises an interesting point that came up in our discussion that 's maybe worth repeating . We realized that , when we were talking about this that , OK , there 's these different things that we want to do with it . So , it 's true that we wanna be selective in some ways , the way that you were speaking about with , not having an interlingua and  , these other issues . But on the other hand , it 's not necessarily true that we need all of the corpus to satisfy all of it . So , a a as per the example that we wanna have a fair amount that 's done with a small n recorded with a small , typ number of types of meetings But we can also have another part that 's , just one or two meetings of each of a of a range of them and that 's OK too .  , i We realized in discussion that the other thing is , what about this business of distant and close microphones ?  , we really wanna have a substantial amount recorded this way , that 's why we did it . But what about For th for these issues of summarization , a lot of these higher level things you don't really need the distant microphone . phd f: Right , I c I think there 's grad b: And you don't really need the close microphone , you mean . phd f: Yea - yeah yeah , you actually don't really even need any fancy microphone . postdoc e: Which one did you mean ? professor d: You d You don't ne it doesn't you just need some microphone , somewhere . professor d: but  phd f: use  , but I think that any data that we spend a lot of effort to collect , professor d: Yeah . phd f: you know , each person who 's interested in  , we have a cou we have a bunch of different , slants and perspectives on what it 's useful for , they need to be taking charge of making sure they 're getting enough of the kind of data that they want . phd f: And So in my case , I think there w there is enough data for some kinds of projects and not enough for others . phd f: And so I 'm looking and thinking , " Well I 'd be glad to walk over and record people and so forth if it 's to help th in my interest . phd f: And other people need to do that for themselves , h or at least discuss it so that we can find some optimal professor d: Right . professor d: But I think that I 'm raising that cuz I think it 's relevant exactly for this idea up there that if you think about , " Well , gee , we have this really complicated setup to do , " well maybe you don't . professor d: Maybe if if If really all you want is to have a a a recording that 's good enough to get a  , a transcription from later , you just need to grab a tape recorder and go up and make a recording . professor d: we we could have a fairly We could just get a DAT machine and phd f: Well , I agree with Jane , though , on the other hand that phd c: Yeah . phd f: So that might be true , you may say for instance , summarization , or something that sounds very language oriented . You may say well , " Oh yeah , you just do that from transcripts of a radio show . phd f: But what you what I was thinking is long term what would be neat is to be able to pick up on  Suppose you just had a distant microphone there and you really wanted to be able to determine this . phd f: So I do think that long term you should always try to satisfy the greatest number of of interests and have this parallel information , which is really what makes this corpus powerful . phd f: Otherwise , you know , lots of other sites can propose individual studies , so professor d:  but I I think that the  i We can't really underestimate the difficulty shouldn't really u underestimate the difficulty of getting a setup like this up . professor d: And so ,  it took quite a while to get that together and to say , " Oh , we 'll just do it up there , " phd f: OK . professor d: If you 're talking about something simple , where you throw away a lot of these dimensions , then you can do that right away . Talking about something that has all of these different facets that we have here , it won't happen quickly , it won't be easy , and there 's all sorts of issues about th you know keeping the equipment safe , or else hauling it around , and all sorts of o phd f: So then maybe we should try to bring people here . professor d: I think the first priority should be to pry to get try to get people to come here . phd f: that 's that 's OK , so professor d: We 're set up for it . phd f: Yeah , I And I think we can get people to come here , that But the issue is you definitely wanna make sure that the kind of group you 're getting is the right group so that you don't waste a lot of your time and the overhead in bringing people down . professor d: Yeah , they have to do their digits or they don't get they don't get their food . professor d: Yeah grad b: Should I pursue that ? phd f: Oh , definitely , yeah . grad b: So I 'm not sure whether they 'll still be so willing to volunteer , but I 'll send an email and ask . phd f: I 'd love to get people that are not linguists or engineers , cuz these are both weird grad b: Right . professor d: The the The oth the other h phd f: well , I know , I shouldn't say that . The o the o the other The other thing is , that we we talked about is give to them  , burn an extra CD - ROM . professor d: and give them So if they want a basically and audio record of their phd f: Well , I thought that was I thought he meant , " Give them a music CD , " like they g Then he said a CD of the of their speech professor d: Oh . phd f: and I guess it depends of what kind of audience you 're talking to , but You know , I personally would not want a CD of my meeting , grad b: Mmm . Of the meeting ? phd f: but maybe yeah , maybe you 're professor d: If you 're having some planning meeting of some sort and  you 'd like phd f: right . professor d: We 're saying , " Look , you know , you 're gonna get this . It 's actually p It 's probably gonna be pretty useless to you , grad b: Yep . professor d: but you 'll ge appreciate , you know , where it 's useful and where it 's useless , phd f: Right . professor d: and then , we 're gonna move this technology , so it 'll become useful . phd a: What if you could tell them that you 'll give them the the transcripts when they come back ? postdoc e: Alth phd f: But we might need a little more to incentivize them , that 's all . postdoc e: I hav I have to  raise a little eensy - weensy concern about doing th giving them the CD immediately , because of these issues of , you know , this kind of stuff , where maybe You know ? professor d: Good point . professor d: So we can so we can postdoc e: We could burn it after it 's been cleared with the transcript stage . phd f: If It should be the same CD - ROM that we distribute publically , grad b: That 's a good point . phd f: right ? professor d: Although it 's phd f: Otherwise they 're not allowed to play it for anyone . postdoc e: Yeah , that 's right , say " Yeah , well , I got this CD , and , Your Honor , I " grad b: Yeah . So that was that topic , and then  , I guess another topic would be where are we in the whole disk resources question for grad b: We are slowly slowly getting to the point where we have  enough sp room to record meetings . So I  did a bunch of archiving , and still doing a bunch of archiving , I I 'm in the midst of doing the P - files from  , Broadcast News . phd c: Eleven ? grad b: And it 'll take another eleven to do the clone . phd a: Where did you copy it to ? grad b: Well , it 's Abbott . professor d: Sk - It 's copying from one place on Abbott to another place on Abbott ? grad b: Tape . phd a: Ah ! grad b: So I 'm archiving it , and then I 'm gonna delete the files . postdoc e: One thing The good news about that that is that once once it 's archived , it 's pretty quick to get back . professor d: Is it ? postdoc e: it it it The other direction is fast , but this direction is really slow . phd a: Generating a clone ? postdoc e: Yeah , that 's a good point . phd a: Oh ! Hunh ! professor d: S postdoc e: Now , what will  Is the plan to g to So stuff will be saved , it 's just that you 're relocating it ?  , so we 're gonna get more disk space ? Or did I ? grad b: No , the the these are the P - files from Broadcast News , which are regeneratable regeneratable postdoc e: OK . grad b: And so they they were two gigabytes per file and we had six of them or something . professor d: Or maybe six ? grad b: The SUN , ha  , takes more disks than the Andatico one did . The SUN rack takes Th - One took four and one took six , or maybe it was eight and twelve . professor d: How many How much phd a: Is there a difference in price or something ? grad b: Well , what happened is that we we bought all our racks and disks from Andatico for years , according to Dave , and Andatico got bought by another company and doubled their prices . I 've been looking at the , Aurora data and , first first look at it , there were basically three directories on there that could be moved . One was called Aurora , one was Spanish , which was Carmen 's Spanish stuff , and the other one was , SPINE . phd a: And so , I wrote to Dan and he was very concerned that the SPINE stuff was moving to a non - backed - up disk . So , I realized that well , probably not all of that should be moved , just the CD - ROM type data , the the static data . So I moved that , and then  , I asked him to check out and see if it was OK . I told him he could delete it if he wanted to , I haven't checked today to see if he 's deleted it or not . And then Carmen 's stuff , I realized that when I had copied all of her stuff to XA , I had copied stuff there that was dynamic data . professor d: So , but ,  y you 're figuring you can record another five meetings or something with the space that you 're clearing up from the Broadcast News , but , we have some other disks , some of which you 're using for Aurora , but are we g do we have some other other space now ? grad b: Yep . So , so , we have space on the current disk right now , where Meeting Recorder is , and that 's probably enough for about four meetings . phd a: OK , I but the stuff I 'm moving from Aurora is on the DC disk that we grad b: I don't remember . Yeah ,  we were at a hundred percent and then we dropped down to eighty - six for reasons I don't understand . And then with Broadcast News , that 's five or six more meetings , so , you know , we have a couple weeks .  , so , yeah , I think I think we 're OK , until we get the new disk . phd a: So should ,  One question I had for you was , we need we sh probably should move the Aurora an and all that other stuff off of the Meeting Recorder disk . Is there another backed - up disk that you know of that would ? grad b: We should put it onto the Broadcast News one . grad b: But , so we could ' jus just do that at the end of today , once the archive is complete , and I 've verified it . professor d: OK , @ @ So , then I guess th the last thing I 'd had on my my agenda was just to hear hear an update on what what Jose has been doing , phd c:  - huh . professor d: so phd c: I have , eh , The result of my work during the last days . Eh , and the the last , eh , days , eh , I work , eh , in my house , eh , in a lot of ways and thinking , reading eh , different things about the the Meeting Recording project . phd c: But for me , eh is interesting because , eh , eh , here 's i is the demonstration of the overlap , eh , problem . phd c: It 's a real problem , a frequently problem  , because you have overlapping zones eh , eh , eh , all the time . phd c: Eh , by a moment I have , eh , nnn , the , eh , n I I did a mark of all the overlapped zones in the meeting recording , with eh , a exact mark . Oh , you did that by hand ? phd c: Heh ? That 's eh , yet b b Yeah , by b b by hand by hand because , eh , eh " Why . " grad b: Can I see that ? Can I get a copy ? professor d: Oh . phd c: My my idea is to work phd a: Wow ! phd c: I I I do I don I don't @ @ I don't know , eh , if , eh , it will be possible because I I I haven't a lot eh , enough time to to to work .  , only just eh , six months , as you know , but , eh , my idea is , eh , is very interesting to to work in in the line of , eh , automatic segmenter . phd c: Eh but eh , eh , in my opinion , we need eh , eh , a reference eh session to t to to evaluate the the the tool . And so are you planning to do that or have you done that already ? phd c: And No , no , with i grad b: Have you done that or are you planning to do that ? phd c: Sorry ? No , I I plan to do that . Darn ! phd c: I plan I plan , but eh , eh , the idea is the is the following . I I will I will eh , talk about eh , in the in the blackboard about the my ideas . phd c: Eh , eh This information eh , with eh , exactly time marks eh , for the overlapping zones eh overlapping zone , and eh , a speaker a a pure speech eh , eh , speaker zone .  , eh zones eh of eh speech of eh , one speaker without any any eh , noise eh , any any acoustic event eh that eh , eh , w eh , is not eh , speech , real speech . And , I need t true eh , silence for that , because my my idea is to to study the nnn the the set of parameters eh , what , eh , are more m more discriminant to eh , classify . The idea is to eh to use eh , I 'm not sure to eh yet , but eh my idea is to use a a cluster eh algorithm or , nnn , a person strong in neural net algorithm to eh to eh study what is the , eh , the property of the different feat eh feature , eh , to classify eh speech and overlapping eh speech . phd c: And my idea is eh , it would be interesting to to have eh , a control set . And my control set eh , will be the eh , silence , silence without eh , any any noise . postdoc e: Which means that we 'd still You 'd hear the grad b: Yeah , fans . phd c: Eh , I  eh , noise eh , eh claps eh , tape clips , eh , the difference eh , professor d:   phd c: eh , eh , event eh , which , eh , eh , has , eh eh , a hard effect of distorti spectral distortion in the in the eh speech . grad b: So so you intend to hand - mark those and exclude them ? professor d:   phd c: Yeah , I have mark in in in in that Not in all in all the the file , grad b:   phd c: only eh , eh , nnn , mmm , I have eh , ehm I don't remind what is the the the the quantity , but eh , I I have marked enough speech on over and all the overlapping zones . I have , eh , two hundred and thirty , more or less , overlapping zones , and is similar to to this information , grad b: Whew !   phd c: because with the program , I cross the information of  , of Jane with eh , my my segmentation by hand . phd c: And the the idea is , eh , I I will use , eh , I want My idea is , eh , to eh to classify . phd c: I I need eh , the exact eh , mark of the different , eh , eh , zones because I I want to put , eh , for eh , each frame a label indicating . I I I put , eh , eh , for each frame a label indicating what is th the type , what is the class , eh , which it belong . phd c: Eh , the class you will overlapping speech " overlapping " is a class , eh , " speech " @ @ the class that 's grad b: Nonspeech . phd a: These will be assigned by hand ? phd c: a I I I ha I h I I put the mark by hand , phd a: Based on the  - huh . phd c: because , eh , my idea is , eh , in in the first session , I need , eh , I I need , eh , to be sure that the information eh , that , eh , I I will cluster , is is right . Because , eh , eh , if not , eh , I will I will , eh , return to the speech file to analyze eh , what is the problems , grad b: Well , training , and validation . And I I 'd prefer I would prefer , the to to have , eh , this labeled automatically , but , eh , eh , fro th I need truth . So , the difference between the top two , i So so I start at the bottom , so " silence " is clear . By " speech " do you mean speech by one sp by one person only ? phd c: Speech Yeah . postdoc e: So this is un OK , and then and then the top includes people speaking at the same time , or or a speaker and a breath overlapping , someone else 's breath , or or clicking , overlapping with speech So , that that 's all those possibilities in the top one . phd c: Eh , in the first moment , because , eh , eh , I I have information , eh , of the overlapping zones , eh , information about if the , eh , overlapping zone is , eh , from a speech , clear speech , from a one to a two eh speaker , or three speaker , or is is the zone where the breath of a speaker eh , overlaps eh , onto eh , a speech , another , especially speech . postdoc e: So it 's basi it 's basically speech wi som with with something overlapping , which could be speech but doesn't need to be . phd c: No , no , es especially eh , overlapping speech from , eh , different eh , eh , speaker . Eh professor d: No , but there 's but , I think she 's saying " Where do you In these three categories , where do you put the instances in which there is one person speaking and other sounds which are not speech ? " phd c: Ah ! professor d: Which category do you put that in ? postdoc e: Yeah , that 's right . Yeah , he here I I put eh speech from eh , from , eh , one speaker without , eh , eh , any any any events more . postdoc e: Oh ! professor d: Right , so where do you put speech from one speaker that does have a nonspeech event at the same time ? phd c: Where ? Where What is the class ? professor d: Which catege which category ? postdoc e: Like a c phd c: No . phd c: For for the by the @ @ no , @ @ because I I I I want to limit the the nnn , the the study . grad b: Yeah , so that 's what he was saying before , is that he excluded those . phd c: " Why ? Why ? What 's the reason ? " because i it 's the first study . In the in the future , the the idea is to to extend the class , phd a: Is is phd c: to consider all the all the information , you you mentioned before professor d: Yeah . phd c: but eh , the the first idea Because eh , I don't know what hap what will happen with the study . i it 's pure phd a: What if there was a door - slam or something ? phd c: No , no , it 's pure silence . professor d: What you Well w I I think what you m I think what you mean is that it 's nonspeech segments that don't have impulsive noises . professor d: Right ? Cuz you 're calling what you 're calling " event " is somebody coughing or clicking , or rustling paper , or hitting something , which are impulsive noises . Right ? phd c: h here yet , yet I I I I I think I I think , eh , there are that some kind of noises that , eh , don't don't wanted to to be in that , eh , in that control set . phd c: But I prefer , I prefer at at the first , eh , the the silence with eh , this eh this kind of the of eh of noise . professor d: Right , it 's  , it 's " Background " might be might be a better word than " silence " . phd c: And , with this information The idea is eh , eh , nnn , I have a label for for each , eh , frame and , eh with a cluster eh algorithm I and postdoc e: Well , we needed to get the categories , yeah . And eh I am going to prepare a test bed , eh , well , eh , a a set of feature structure eh , eh , models . phd c: I have to to test , but eh I phd a: You have your own ? phd c: Yeah , yeah , yeah . Is a modified version of of of a pitch tracker , eh , from , eh , Standar - eh Stanford University in Stanford ? No . phd a: Oh ! What 's it written in ? phd c: Eh , em , I I I don't remember what is the the name of the of the author , because I I have several I have eh , eh , em , eh , library tools , from eh , Festival and of from Edinburgh eh , from Cambridge , eh , and from our department . phd c: And And I have to because , in general the pitch tracker , doesn't work very well and grad b: Bad . This this is And th the idea is to to , eh , to obtain , eh , for example , eh , eh diff eh , eh , different well , no , a great number of eh FEC for example , eh , eh , twenty - five , eh , thirty thirty parameters , eh , for for each one . And in a first eh , nnn , step in the investi in the research in eh , my idea is try to , eh , to prove , what is the performance of the difference parameter , eh to classify the different , eh , what is the the the the front - end approach to classify eh , the different , eh , frames of each class eh and what is the the , nnn , nnn , nnn , eh , what is the , the error eh , of the data grad b: Supervised clustering . phd c: and the second is try to eh , to use some ideas eh , similar to the linear discriminant analysis . phd c: Eh ? Eh , similar , because the the idea is to to study what is the contribution of eh , each parameter to the process of classify correctly the different the different parameters . What sort of classifier ar ? phd c: Eh , the the the classifier is nnn by the moment is eh is eh , similar , nnn , that the classifier used eh , in a quantifier vectorial quantifier is eh , used to to eh , some distance to to put eh , a vector eh , in in a class different . grad b: Unimodal ? phd c: Is Yeah ? W with a model , is is only to cluster using a eh , @ @ or a similarity . grad b: So is it just one cluster per phd c: A another possibility it to use eh a netw netw a neural network . phd c: But eh what 's the p What is my idea ? What 's the problem I I I I see in in in if you you use the the neural network ? If w when this kind of eh , mmm , cluster , clustering algorithm to can test , to can eh observe what happened you you can't you can't eh , eh put up with your hand in the different parameter , grad b: Right , you can't analyse it . phd c: but eh If you use a neural net is is a good idea , but eh you don't know what happened in the interior of the neural net . professor d: Well , actually , you can do sensitivity analyses which show you what the importance of the different parce pieces of the input are . professor d: It 's hard to w w what you It 's hard to tell on a neural net is what 's going on internally . professor d: But it 's actually not that hard to analyse it and figure out the effects of different inputs , especially if they 're all normalized . professor d: Well , this isn't tru if if if you really wonder what different if if phd c: Yeah . phd c: But professor d: Yeah , then a decision tree is really good , but the thing is here he 's he 's not he 's not like he has one you know , a bunch of very distinct variables , like pitch and this he 's talking about , like , a all these cepstral coefficients , and so forth , grad b: Right . professor d: in which case a a any reasonable classifier is gonna be a mess , and it 's gonna be hard to figure out what what  phd c: And grad b: Right . professor d: I  , I think the other thing that one  , this is , I think a good thing to do , to sort of look at these things at least See what I 'd I 'd Let me tell you what I would do . Instead of taking all the MFCC 's , or all the PLP 's or whatever , I would just take a couple . professor d: OK ? Like like C - one , C - two , something like that , so that you can visualize it . professor d: OK , so before you do build up any kind of fancy classifiers , just take a look in two dimensions , at how these things are split apart . professor d: That I think will give you a lot of insight of what is likely to be a useful feature when you put it into a more complicated classifier . professor d: And the second thing is , once you actually get to the point of building these classifiers , @ @ what this lacks so far is the temporal properties . So if you 're just looking at a frame and a time , you don't know anything about , you know , the structure of it over time , and so you may wanna build @ @ build a Markov model of some sort  , or or else have features that really are based on  on on some bigger chunk of time . But don't  anyway , this is my suggestion , is don't just , you know , throw in twenty features at it , the deltas , and the delta del and all that into some classifier , even even if it 's K - nearest - neighbors , you still won't know phd c: Yeah . professor d: what it 's doing , even You know it 's  , I think to know what it 's to have a better feeling for what it 's grad b: Yep . professor d: look at at som some picture that shows you , " Here 's These things  ,  are offer some separation . " And , in LPC , the thing to particularly look at is , I think is something like , the residual phd c: Yeah . S postdoc e: Can I ask ? It strikes me that there 's another piece of information  , that might be useful and that 's simply the transition . So , w if you go from a transition of silence to overlap versus a transition from silence to speech , there 's gonna be a b a big informative area there , it seems to me . phd c: I eh the the Meeting Recorder project , for me , has eh , two eh , w has eh several parts , several p objective professor d:   But eh , at the first , in the acoustic , eh , eh , parts of the project , eh I think you eh we have eh two main eh objective . And for that , if you don't use , eh , eh , a speech recognizer , eh broad class , or not broad class to to try to to to label the different frames , I think the Ike criterion or BIC criterion eh will be enough to detect the change . When you you have , eh , eh s eh the transition of speech or or silence eh to overlap zone , this criterion is enough with probably with , eh , this kind of , eh , eh the the the more eh use eh use eh used eh em normal , regular eh parameter MF - MFCC . But eh eh I I understand that you your objective is to eh classify , to know that eh that zone not is only a new zone in the in the file , that eh you have eh , but you have to to to know that this is overlap zone . because in the future you will eh try to to process that zone with a non - regular eh eh speech recognizer model , I suppose . phd c: you you will pretend to to to process the overlapping z eh zone with another kind of algorithm professor d:   phd c: because it 's very difficult to to to obtain the transcription from eh using eh eh a regular , normal speech recognizer . phd c: A model to detect more acc the mor most accurately possible that is p  , will be possible the , eh the mark , the change and another another model will @ @ or several models , to try s but eh several model eh robust models , sample models to try to classify the difference class . grad b: I 'm I 'm I 'm sorry , I didn't understand you what you said . What what model ? postdoc e:  phd c: Eh , the the classifiers of the of the n to detect the different class to the different zones before try to to recognize , eh with eh to transcribe , with eh a speech recognizer . phd c: And my idea is to use eh , for example , a neural net postdoc e: So p phd c: with the information we obtain from this eh this eh study of the parameter with the selected parameter to try to eh to put the class of each frame . phd c: you you eh , eh have obtained in the first eh , step with the for example , BIC eh , eh criterion compare model postdoc e:   phd c: And You I don't - u professor d: OK , but , I I think in any event we 're agreed that the first step is phd c: i postdoc e: Yeah . professor d: Because what we had before for for  , speaker change detection did not include these overlaps . professor d: So the first thing is for you to to build up something that will detect the overlaps . professor d: Right ? So again , I think the first thing to do to detect the overlaps is to look at these  , in in in in grad b: Features ? phd c: Yeah . professor d: Well , I again , the things you 've written up there I think are way too way too big . professor d: OK ? If you 're talking about , say , twelfth twelfth - order  MFCC 's or something like that it 's just way too much . All you 'll be able to do is put it into a classifier and see how well it does . professor d: Whereas I think if you have things if you pick one or two dimensional things , or three of you have some very fancy display , and look at how the the different classes separate themselves out , you 'll have much more insight about what 's going on . professor d: Well , you 'll you 'll get a feeling for what 's happening , you know , phd c: Yeah . professor d: so if you look at Suppose you look at first and second - order cepstral coefficients for some one of these kinds of things and you find that the first - order is much more effective than the second , and then you look at the third and there 's not and not too much there , you may just take first and second - order cepstral coefficients , phd c: Yeah . professor d: right ? And with LPC , I think LPC per se isn't gonna tell you much more than than than the other , maybe .  , and  on the other hand , the LPC residual , the energy in the LPC residual , will say how well ,  the low - order LPC model 's fitting it , which should be pretty poorly for two two or more people speaking at the same time , and it should be pretty well , for w for for one . professor d: And so I i again , if you take a few of these things that are are prob  promising features and look at them in pairs , I think you 'll have much more of a sense of " OK , I now have  , doing a bunch of these analyses , I now have ten likely candidates . phd c: but eh , eh eh eh eh I don't know it is the first eh way to to do that and I would eh like to to know what eh , your opinion . Eh all this study in the f in the first moment , I I w I I will pretend to do with eh eh equalizes speech . grad b: With postdoc e: With what ? With what ? grad b: Right . phd c: Eh , why ? Because eh the spectral distortion is more eh a lot eh clearer , very much clearer if we compare with the PDA . phd c: fff ! Because the n the noise eh to sp the signal - to - noise relation is eh is is low . phd c: I don't know eh  i i that eh the the result of the of the study eh with eh with eh this eh this speech , the mix speech eh will work exactly with the eh PDA files . phd c: eh What , I  , what what is the effect of the low ' signal to to to noise relation , you know , eh with professor d: N u We Well , I think I think I think it 's not a it 's not at all unreasonable . It makes sense to start with the simpler signal because if you have features which don't aren't even helpful in the high signal - to - noise ratio , then there 's no point in putting them into the low signal ratio , one would think , anyway . professor d: And so , if you can get @ @  again , my prescription would be that you would , with a mixed signal , you would take a collection of possible  , features look at them , look at how these different classes that you 've marked , separate themselves , and then collect ,  in pairs , and then collect ten of them or something , and then proceed with a bigger classifier . professor d: And then if you can get that to work well , then you go to the other signal . And then , and you and you know , they won't work as well , but how m you know , how much grad b: Right . Because it I think it would be interesting to see if some features work well with close mixed , and And don't professor d:  . professor d: That 's well , the It it 's it 's true that it also , it could be useful to do this exploratory analysis where you 're looking at scatter plots and so on in both cases . phd c: I I I I think that the the eh parameter we found , eh , eh worked with both eh , speech file , postdoc e: That 's good . phd c: but eh what is the the the relation of eh of the performance when eh you use eh the , eh eh speech file the PDA speech files . Is eh is not easy eh to to solve , because if you I I I have seen the the the speech file from eh PDA , and s some parts is very difficult because you you don't see the spectrum the spectrogram . phd c: Is very difficult to apply eh , eh a parameter to detect change when you don't see . Well , that that that 's another reason why very simple features , things like energy , and things things like harmonicity , and residual energy are  , yeah are are better to use than very complex ones because they 'll be more reliable . phd a: Yeah , I maybe this is a dumb question , but w I thought it would be I thought it would be easier if you used a PDA professor d: Nah . phd a: because can't you , couldn't you like use beam - forming or something to detect speaker overlaps ?  grad b: Well , if you used the array , rather than the signal from just one . professor d: Yeah , no , you you 're you 're right grad b: But that 's professor d: that In fact , if we made use of the fact that there are two microphones , you do have some location information . which we don't have with the one and and so that 's phd a: Is that not allowed with this project ? professor d: well , no , we we don't have any rules , r really . professor d: I think I I think I think it 's it 's it 's a it 's an additional interesting question . professor d: I think you wanna know whether you can do it with one , because you know it 's not necessarily true that every device that you 're trying to do this with will have two . professor d: if , on the other hand , we show that there 's a huge advantage with two , well then that could be a real point . professor d: But , we don't n even know yet what the effect of detecting having the ability to detect overlaps is . postdoc e: There there is a complication though , and that is if a person turns their back to the to the PDA , then some of the positional information goes away ? phd c: Yeah . professor d: Well , it it it does , i it d it does , but the the the issue is that that phd a: No , it 's not it 's not that so much as postdoc e: And then , And if they 're on the access on the axis of it , that was the other thing I was thinking . postdoc e: He You mentioned this last time , that that if if you 're straight down the midline , then then the r the left - right 's gonna be different , grad b: Yeah , we hav need to put it on a little turntable , phd c: I I I I I th grad b: and phd a: Well , it 's phd c: Yeah . postdoc e: It seems to me that that it 's not a p  , you know , it 's this the topograph the topology of it is is a little bit complicated . phd a: I don't I don't know ho phd c: I I I think Sorry . I I I think because the the the distance between the two microph eh , microphone , eh , in the PDA is very near . But it 's  from my opinion , it 's an interesting idea to to try to study the binaural eh problem eh , with information , because I I found difference between the the speech from from each micro eh , in the PDA . It - it 's not amplitude , postdoc e: Oh yeah ! Oh I agree ! And we use it ourselves . postdoc e: But I 'm just I 'm just saying that the way we 're seated around a table , is not the same with respect to each to each person with respect to the PDA , phd c: No . postdoc e: so we 're gonna have a lot of differences with ref respect to the speaker . phd c: But professor d: That 's So so i @ @ I think the issue is , " Is there a clean signal coming from only one direction ? " phd a: Right . professor d: If it 's not coming from just one direction , if it if th if there 's a broader pattern , it means that it 's more likely there 's multiple people speaking , phd c: Yeah . phd a: So it 's sort of like how how confused is it about where the beam is . professor d: Yeah , is there a narrow Is there a narrow beam pattern or is it a a distributed beam pattern ? So if there 's a distributed beam pattern , then it looks more like it 's it 's  , multiple people . OK , it just it just seemed to me that  , that this isn't the ideal type of separation .  , I I think it 's I can see the value o professor d: Oh , ideal would be to have the wall filled with them , but  But the thing is just having two mikes If you looked at that thing on on Dan 's page , it was When when there were two people speaking , and it looked really really different . phd a: Did - Sorry , b  I 'm not sure what Dan 's page is that you mean . professor d: You take the signal from the two microphones and you cros and you cross - correlate them with different lags . So when one person is speaking , then wherever they happen to be at the point when they 're speaking , then there 's a pretty big maximum right around that point in the l in in the lag . professor d: So if at whatever angle you are , at some lag corresponding to the time difference between the two there , you get this boost in the in in the cross - correlation value function . phd a: So so if there 's two grad b: And if there are multiple people talking , you 'll see two peaks . postdoc e: Well , let me ask you , if if both people were over there , it would be less effective than if one was there and one was across , catty - corner ? phd c: Yeah . The - the Oh , I 'm sorry , postdoc e: No ? professor d: if they 're right next to one another ? phd a: If I was if I was here and Morgan was there and we were both talking , it wouldn't work . professor d: i i postdoc e: Next next one over n over on this side of the P PDA . postdoc e: Versus you versus you know , and we 're catty - corner across the table , and I 'm farther away from this one and you 're farther away from that one . grad b: Or or even if , like , if people were sitting right across from each other , you couldn't tell the difference either . Yeah , so it 's postdoc e: And so my point was just that it 's it 's gonna be differentially differentially varia valuable . postdoc e: it 's not to say  , I certainly think it 's extremely val And we we humans n n depend on you know , these these binaural cues . professor d: But it 's almost but it 's almost a I think what you 're talking about i there 's two things . professor d: There 's a sensitivity issue , and then there 's a pathological error  issue . professor d: If someone just happens to be sitting right there then we won't get good information from it . and i and if there So it And if it 's the two of you guys on the same side professor d: if they 're if they 're close , it 's just a question of the sensitivity . professor d: So if the sensitivity is good enough and we just we just don't have enough , experience with it to know how postdoc e: Yeah . postdoc e: Oh I 'm not I 'm not trying to argue against using it , by any means . I just wanted to point out that that weakness , that it 's topo topologically impossible to get it perfect for everybody . professor d: the other thing you can do  , if  , i We 're assuming that it would be a big deal just to get somebody convince somebody to put two microphones in the PDA . And then you know then you 're sort of Yeah , then then you pretty much could cover phd a: Once you got two postdoc e: Interesting . phd a: Well what about just doing it from these mikes ? postdoc e: Interesting . phd c: It will be more interesting to study the PZM because the the the separation I I think professor d:  @ @ But - but that 's  , we can we 'll be all of this is there for us to study . professor d: But but but the thing is , one of the at least one of the things I was hoping to get at with this is what can we do with what we think would be the normal situation if some people get together and one of them has a PDA . phd a: That 's what I was asking about , what are the constraints ? phd c: Yeah . professor d: Well , that 's that 's the constraint of one question that I think both Adam and I were were were interested in . professor d: but you know if you can instrument a room , this is really minor league compared with what some people are doing , right ? Some people at at  , yeah , at Brown and and and and at   and at Cape , grad b: Big micro @ @ arrays . phd a: Didn't they have something at Cape ? professor d: they both have these , you know , big arrays on the wall . And you know , if you could do that , you 've got microphones all over the place grad b: Very finely . professor d: you know p tens of microphones , and and  phd a: Oh ! I saw a demo . professor d: And if you do that then you can really get very nice  kind of selectivity phd a: Yeah . grad b: Oh , I saw one that was like a hundred microphones , a ten by ten array . phd a: And you could In a noisy room , they could have all kinds of noises and you can zoom right in on somebody . grad b: It was all in software and they and you could pick out an individual beam and listen to it . professor d: But , the reason why I haven't focused on that as the fir my first concern is because  , I 'm interested in what happens for people , random people out in some random place where they 're p having an impromptu discussion . And you can't just always go , " well , let 's go to this heavily instrumented room that we spent tens of thousands of dollars to se to set up " . phd a: No , what you need to do is you 'd have a little fabric thing that you unroll and hang on a wall . phd a: It has all these mikes and it has a plug - in jack to the PDA . grad b: But I think professor d: The other thing actually , that gets at this a little bit of something else I 'd like to do , is what happens if you have two P D grad b: Yep . professor d: and they communicate with each other ? And then You know , they 're in random positions , the likelihood that  , basically there wouldn't be any l likely to be any kind of nulls , if you even had two . grad b: Though All sorts of interesting things you can do with that , postdoc e: Interesting . grad b: not only can you do microphone arrays , but you can do all sorts of  multi - band as well . postdoc e: Ah ! phd a: I still like my rug on the wall idea , so if anybody patents that , then grad b: But I think postdoc e: Well , you could have strips that you stick to your clothing . phd a: Hats ? grad b: In terms of the research th research , it 's really it 's whatever the person who is doing the research wants to do .  , I i I i I would actually kind of like us to wind it down , see if we can still get to the end of the , birthdays thing there . professor d: So grad b: Well , I had a couple things that I did wanna bring out . grad b: One is , do we need to sign new these again ? postdoc e: Well , it 's slightly different . phd a: Are they new ? postdoc e: Cuz it it 's slightly different . professor d: Oh , this morning we didn't sign anything cuz we said that if anybody had signed it already , we didn't have to . grad b: Yeah , I I should 've checked with Jane first , but the ch the form has changed . grad b: I had some things I wanted to talk about with the thresholding stuff I 'm doing . postdoc e: I had to make one grad b: But , if we 're in a hurry , we can put that off . postdoc e: Well , should I  I have some results to present , but  I guess we won't have time to do that this time . But it seems like  the anonymization is  , is also something that we might wanna discuss in greater length .  , wha what postdoc e: If if we 're about to wind down , I think what I would prefer is that we  , delay the anonymization thing till next week , and I would like to present the results that I have on the overlaps . @ @ It sounds like u  , there were there were a couple technical things people would like to talk about . Why don't we just take a couple minutes to to briefly do them , and then and then and then and then and then we grad b: OK , go ahead , Jane . postdoc e: I 'd Oh , I 'd prefer to have more time for my results . postdoc e: And I think the anonymization , if y if you want to proceed with that now , I just think that that 's that 's a discussion which also n really deserves a lo a you know , more that just a minute . postdoc e: I really do think that , because you raised a couple of possibilities yourself , you and I have discussed it previously , and there are different ways that people approach it , e and I think we should grad b: Alright . We 're we 're just We 're getting enough data now that I 'd sort of like to do it now , before I get overwhelmed with once we decide how to do it postdoc e: Well , OK . I I 'll give you the short version , but I do think it 's an issue that we can't resolve in five minutes . postdoc e: OK , so the the short thing is  , we have  , tape recording  , sorry , digitized recor recordings . postdoc e: Now , in terms of like the transcript , the question becomes what symbol are you gonna put in there for everybody 's name , and whether you 're gonna put it in the text where he says " Hey Roger " or are we gonna put that person 's anonymized name in instead ? grad b: No , because then that would give you a mapping , and you don't wanna have a mapping . postdoc e: OK , so first decision is , we 're gonna anonymize the same name for the speaker identifier and also in the text whenever the speaker 's name is mentioned . Because that would give you a mapping between the speaker 's real name and the tag we 're using , and we don't want postdoc e: I I don't think you understood what I what I said . postdoc e: So  , so in within the context of an utterance , someone says " So , Roger , what do you think ? " OK . Then , it seems to me that Well , maybe I  it seems to me that if you change the name , the transcript 's gonna disagree with the audio , and you won't be able to use that . grad b: We don't we wanna we ha we want the transcript to be " Roger " . grad b: Because if we made the the transcript be the tag that we 're using for Roger , someone who had the transcript and the audio would then have a mapping between the anonymized name and the real name , and we wanna avoid that . postdoc e: OK , well , but then there 's this issue of if we 're gonna use this for a discourse type of thing , then and , you know , Liz was mentioning stuff in a previous meeting about gaze direction and who 's who 's the addressee and all , then to have " Roger " be the thing in the utterance and then actually have the speaker identifier who was " Roger " be " Frank " , that 's going to be really confusing and make it pretty much useless for discourse analysis . postdoc e: Now , if you want to , you know , in some cases , I I I know that Susan Ervin - Tripp in some of hers , actually did do  , a filter of the s signal where the person 's name was mentioned , except professor d: Yeah Yeah , once you get to the publication you can certainly do that . postdoc e: And and I cer and I So , the question then becomes one level back .  , how important is it for a person to be identified by first name versus full name ? Well , on the one hand , it 's not a full identity , we 're taking all these precautions ,  and they 'll be taking precautions , which are probably even the more important ones , to they 'll be reviewing the transcripts , to see if there 's something they don't like OK . On the other hand , this is a small this is a small pool , and people who say things about topic X e who are researchers and well - known in the field , they 'll be identifiable and simply from the from the first name . However , taking one step further back , they 'd be identifiable anyway , even if we changed all the names . postdoc e: So , is it really ,  You know ? grad b: Ugh ! postdoc e: Now , in terms of like so I I did some results , which I 'll report on n next time , which do mention individual speakers by name . Now , it would be very possible for me to take those data put them in a in a study , and just change everybody 's name for the purpose of the publication . And someone who looked professor d: You can go , you know , " Z "  , for instance .  , yeah , t it doesn't  , I 'm not knowledgeable about this , but it certainly doesn't bother me to have someone 's first name in in the in the transcript . postdoc e: Yeah , and and in the form that they sign , it does say " your first name may arise in the course of the meetings " . So again , th the issue is if you 're tracking discourse things , you know , if someone says , " Frank said this " and then you wanna connect it to something later , you 've gotta have this part where that 's " Frank colon " . grad b: Yeah , shoot ! professor d: Right ? postdoc e: Yeah , and and you know , even more i i  , immediate than that just being able to ,  Well , it just seems like to track track from one utterance to the next utterance who 's speaking and who 's speaking to whom , cuz that can be important . postdoc e: S i You know , " You raised the point , So - and - so " , it 's be kind of nice to be able to know who " you " was . postdoc e: And ac and actually you remember furthermore , you remember last time we had this discussion of how you know , I was sort of avoiding mentioning people 's names , professor d: Yeah , I was too . Well , if we 're going to step in after the fact and change people 's names in the transcript , we 've basically done something one step worse . Well , I would sug I I don't wanna change the names in the transcript , phd c: Yeah . grad b: but that 's because I 'm focused so much on the acoustics instead of on the discourse , and so I think that 's a really good point . L let me just back up this to make a a brief comment about the , what we 're covering in the meeting .  I realize when you 're doing this that   , I didn't realize that you had a bunch of things that you wanted to talk about . So I think what would be helpful would be  , i and I 'll I 'll mention this to to Liz and Andreas too , that  , before the meeting if anybody could send me , any any , agenda items that they were interested in and I 'll I 'll take the role of organizing them  , into into the agenda , postdoc e: OK . I 've no desire to to make it up , but if if no one 's told me things , then I 'm just proceeding from my my guesses , and and  , and i ye yeah , I I 'm sorry it ended up with your out your time to  , I 'm just always asking Jose what he 's doing , you know , and and so it 's There 's  , there 's obviously other things going on . grad b: How will we how would the person who 's doing the transcript even know who they 're talking about ? Do you know what I 'm saying ? phd a: " The person who 's doing the transcript " The IBM people ? grad b: Yeah .  , so so how is that information gonna get labeled anyway ? postdoc e: How do you mean , who what they 're who they 're talking about ? grad b: so if I 'm saying in a meeting , " oh and Bob , by the way , wanted wanted to do so - and - so " , postdoc e: How do you mean ? phd a: They 're just gonna write " Bob " on it or do @ @ grad b: if you 're doing Yeah , @ @ they 're just gonna write " Bob " . If you 're if you 're doing discourse analysis , postdoc e: They won't be able to change it themselves . professor d: What ar how are they gonna do any of this ? grad b: Yeah , really . postdoc e: Well , I I 'm betting we 're gonna have huge chunks that are just totally un untranscribable by them . professor d: they 're gonna say speaker - one , or speaker - two or speaker  I I phd a: They can't do that . phd c: Yeah , I think grad b: Well , the current one they don't do speaker identity . phd c:  grad b: because in NaturallySpeaking , or , excuse me , in ViaVoice , it 's only one person . I think that My understanding from Yen Is it Yen - Ching ? Is that how you pronounce her name ? professor d:  Yu - Ching , Yu - Ching . postdoc e: Oh ,  Yu - Ching ? Yu - Ching ? grad b: y Yu - Ching . postdoc e: was that  , they will that they will adopt the part of the conventions that that we discussed , where they put speaker identifier down . But , you know , h they won't know these people , so I think it 's Well , they 'll they 'll adopt some convention but we haven't specified to them So they 'll do something like speaker - one , speaker - two , is what I bet , but I 'm betting there 'll be huge variations in the accuracy of of their labeling the speakers . professor d: And it and it may very well be  , since they 're not going to sit there and and and worry ab about , it being the same speaker , they may very well go the eh the the first se the first time it changes to another speaker , that 'll be speaker - two . professor d: And the next time it 'll be speaker - three even if it 's actually speaker - one . grad b: Yeah we we can probably regenerate it pretty easily from the close - talking mikes . phd c:  postdoc e: Yes , I was thinking , the temp the time values of when it changes . grad b: The p It 's a good point , " which what do you do for discourse tracking ? " phd c: Because y y you don't know to know , eh you don't need to know what i what is the iden identification of the of the speakers . If if if if someone says , " what what is Jose doing ? " and then Jose says something , you need to know that that was Jose responding . postdoc e: Unless we adopt a different set of norms which is to not id to make a point of not identifying people by name , which then leads you to be more contextually ex explicit . You know ?  , so when we did this las last week , I felt that you know , now , Andreas may , @ @  , he he i sometimes people think of something else at the same time and they miss a sentence or something , and and because he missed something , then he missed the r the initial introduction of who we were talking about , and was was unable to do the tracking . postdoc e: But I felt like most of us were doing the tracking and knew who we were talking about and we just weren't mentioning the name . phd a: But , you know , like , at the beginning of this meeting Or , you I think said , you know , or s Liz , said something about  , " is Mari gonna use the equipment ? "  , how would you say that ? postdoc e: Yeah ? phd a: you have to really think , you know , about what you 're saying bef grad b: if you wanted to anonymize . Yeah , is professor d: " Is you know who up in you know where ? " phd a: Yeah . professor d: Right ? Use the phd a: I think it would be really hard if we made a policy where we didn't say names , plus we 'd have to tell everybody else . grad b: Yeah , darn !  , what I was gonna say is that the other option is that we could bleep out the names . phd a: I I think the I think I don't know , my own two cents worth is that you don't do anything about what 's in the recordings , you only anonymize to the extent you can , the speakers have signed the forms and all . grad b: Well , but that but that as I said , that that that works great for the acoustics , but it it hurts you a lot for trying to do discourse . grad b: Because you don't have a map of who 's talking versus their name that they 're being referred to . phd a: I thought we were gonna get it labelled speaker - one , speaker - two grad b: Sure but , h then you have to know that Jose is speaker - one and phd a: Why do you have to know his name ? professor d: OK , so suppose someone says , " well I don't know if I really heard what  , what Jose said . professor d: So  u phd a: Oh , I see , you wanna associated the word " Jose " in the dialogue with the fact that then he responded . professor d: And so , if we pass out the data to someone else , and it says " speaker - five " there , we also have to pass them this little guide that says that speaker - five is Jose , grad b: And that violates our privacy . professor d: and if were gonna do that we might as well give them " Jose " say it was " Jose " . postdoc e: Now , I I think that we have these two phases in the in the data , which is the one which is o our use , University of Washington 's use , IBM , SRI . postdoc e: And within that , it may be that it 's sufficient to not  change the to not incorporate anonymization yet , but always , always in the publications we have to . postdoc e: And I think also , when we take it that next step and distribute it to the world , we have to . But I but I don that 's that 's a long way from now and and it 's a matter of between now and then of d of deciding how grad b: Making some decisions ? postdoc e: i i it You know , it may be s that we we 'll need to do something like actually X out that part of the  the audio , and just put in brackets " speaker - one " . phd c: the ? ? grad b: You know , what we could do also is have more than one version of release . professor d: I I think that we we have a need to have a consistent licensing policy of some sort , and postdoc e: But I also think a consistent licensing policy is important . phd a: Well , one thing to to take into consideration is w are there any  For example , the people who are funding this work , they want this work to get out and be useful for discourse . phd a: If we all of a sudden do this and then release it to the public and it 's not longer useful for discourse , you know grad b: Well , depending on how much editing we do , you might be able to still have it useful . grad b: But , n excuse me , but you could bleep out just the names . professor d: She No , but she 's saying , from the argument before , she wants to be able to say if someone said " Jose " in their in their thing , and then connect to so to what he said later , then you need it . But in the transcript , you could say , everywhere they said " Jose " that you could replace it with " speaker - seven " . But I I also wanna say that people grad b: And then it wouldn't meet match the audio anymore . professor d: And th and the other thing is if if if Liz were here , what she might say is that she wants to look if things that cut across between the audio and the dialogue , postdoc e: Well , you see ? So , it 's complicated . postdoc e: But it 's g but I think it was good to introduce the thing and we can do it next time . grad b: I didn't think when I wrote you that email I wasn't thinking it was a big can of worms , but I guess it is . postdoc e: Well it Discourse , you know Also I wanted to make the point that that discourse is gonna be more than just looking at a transcript . postdoc e: It 's gonna be looking at a t You know , and prosod prosodic stuff is involved , and that means you 're going to be listening to the audio , and then you come directly into this confronting this problem . phd a: Maybe we should just not allow anybody to do research on discourse , postdoc e: So . professor d: maybe we should only have meetings between people who don't know one another and who are also amnesiacs who don't know their own name . grad b: Did you read the paper on Eurospeech ? postdoc e: We could have little labels . I I I wanna introduce my Reservoir Dogs solution again , which is everyone has like " Mister White " , " Mister Pink " , " Mister Blue " . Did you read the paper a few years ago where they were reversing the syllables ? They were di they they had the utterances . phd a: But so , the syllables were in the same order , with respect to each other , but the acous grad b: Everything was in the same order , but they were the individual syll syllables were played backwards . phd a: What did it sound like ? grad b: People had no difficulty in interpreting it . So what we need is something that 's the reverse , that a speech recognizer works exactly the same on it but people can't understand it . professor d: What , what does the speech recognizer care ? grad b: Ah , anyway . professor d: postdoc e: Oh , do we do digits ? Or ? What do we do ? grad b:  OK , we 'll quickly do digits . like as if you were talking like , " nine eight six eight seven ? " postdoc e: Well , you know , in the in the one I transcribed , I did find a couple instances I found one instance of contrastive stress , where it was like the string had a li So it was like " nine eight two four , nine nine two four " . So they were like looking ahead , postdoc e: And phd a: huh ? postdoc e: Well , they differed .  , I 've I am sort of interested in in And sometimes , you know , I s And I I never know . phd a: Well , I was thinking that it must get kind of boring for the people who are gonna have to transcribe this postdoc e: and I phd a: They may as well throw in some interesting intonations 
To ensure accuracy, a small percentage of transcripts will be modified to correct any misread or uncorrected digits. In order to establish consistent links between speakers and their corresponding identification tags, a speaker database will be compiled. The initial test set of digits has been completed, consisting of 4,000 lines with each line containing 1-10 digits. To explore different prosodic groupings of numbers, new digit forms were distributed. The group also discussed new naming conventions to facilitate the sorting process. In order to standardize file names, existing files will be modified to have equal lengths. Similar changes will be made to speaker identification tags. Additionally, files will include information specifying the channel, microphone, and broadcaster details. PhD F suggested recording different meetings with the same group to aid in tasks like summarization, while Professor D advocated for random meeting participants to ensure diversity for acoustical research purposes.
Summarize the discussion on the problems, solutions, and ideas about recording data and the current status of the recordings.