grad h: Yeah , so if  So if anyone hasn't signed the consent form , please do so . phd a: Now you won't be able to walk or ride your bike , huh ? professor d: OK . grad h: And  , shall I go ahead and do some digits ? professor d: we were gonna do that at the end , remember ? grad h: OK , whatever you want . Just just to be consistent , from here on in at least , that that we 'll do it at the end . professor d: OK  Well , it ju  it might be that someone here has to go , postdoc f: Testing , one , two , three . So ,  I had asked actually anybody who had any ideas for an agenda to send it to me and no one did . professor d: postdoc f: From last time I wanted to  The An iss  one topic from last time . professor d: Right , s OK , so one item for an agenda is  Jane has some   some research to talk about , research issues . professor d: I have a list of things that I think were done over the last three months I was supposed to send off ,  and ,  I I sent a note about it to  to Adam and Jane but I think I 'll just run through it also and see if someone thinks it 's inaccurate or  insufficient . phd a: A list that you have to send off to who ? professor d: to   , IBM .  , And , Anything else ? anyone wants to talk about ? phd a: What about the ,  your trip , yesterday ? professor d: No . professor d: Cuz that 's Cuz that was all all about the ,  I I I can chat with you about that off - line .  , And , Anything else ? Nothing else ?  , there 's a  , there is a a ,   telephone call tomorrow , which will be a conference call that some of us are involved in for  a possible proposal .  , we 'll talk we 'll talk about it next week if if something grad h: Do you want me to be there for that ? I noticed you C C ' ed me , but I wasn't actually a recipient . So it sounds like the the three main things that we have to talk about are ,  this list ,  Jane and Jane and Adam have some research items , and , other than that , anything , as usual , anything goes beyond that . OK , Jane , since since you were sort of cut off last time why don't we start with yours , make sure we get to it . postdoc f: OK , it 's it 's very eh it 's very brief ,  just let me just hand these out . So , if you 've looked at this you 've seen it before , so Basically ,  as you know ,  part of the encoding includes a mark that indicates an overlap . It 's not indicated with , tight precision , it 's just indicated that OK , so , It 's indicated to to so the people know what parts of sp which which stretches of speech were in the clear , versus being overlapped by others . So , I used this mark and ,  and , divided the I wrote a script which divides things into individual minutes , of which we ended up with forty five , and a little bit . postdoc f: And ,  What you can see is the number of overlaps and then to the right , whether they involve two speakers , three speakers , or more than three speakers . And ,  and , what I was looking for sp sp specifically was the question of whether they 're distributed evenly throughout or whether they 're bursts of them . And it looked to me as though  , you know y this is just eh eh , this would this is not statistically verified , but it did look to me as though there are bursts throughout , rather than being localized to a particular region . The part down there , where there 's the maximum number of of ,  overlaps is an area where we were discussing whether or not it would be useful to indi to s to code stress , sentence stress as possible indication of ,  information retrieval . professor d: What was what 's the the parenthesized stuff that says , like e the first one that says six overlaps and then two point eight ? postdoc f: Oh , th That 's the per cent . postdoc f: So , six is ,  two point eight percent of the total number of overlaps in the session . postdoc f: At the very end , this is when people were , you know , packing up to go basically , there 's this final stuff , I think we I don't remember where the digits fell . postdoc f: So , i it seems like it goes through bursts but ,  that 's kind of it . postdoc f: Now , Another question is is there are there individual differences in whether you 're likely to be overlapped with or to overlap with others . And , again I want to emphasize this is just one particular  one particular meeting , and also there 's been no statistical testing of it all , but I ,  I took the coding of the I , you know , my I had this script figure out ,  who was the first speaker , who was the second speaker involved in a two - person overlap , I didn't look at the ones involving three or more . And ,  this is how it breaks down in the individual cells of who tended to be overlapping most often with who who else , and if you look at the marginal totals , which is the ones on the right side and across the bottom , you get the totals for an individual . So ,  If you look at the bottom , those are the ,  numbers of overlaps in which  Adam was involved as the person doing the overlapping and if you look I 'm sorry , but you 're o alphabetical , that 's why I 'm choosing you And then if you look across the right , then that 's where he was the person who was the sp first speaker in the pair and got overlap overlapped with by somebody . postdoc f: And , then if you look down in the summary table , then you see that ,  th they 're differences in whether a person got overlapped with or overlapped by . phd e: Yeah Yeah postdoc f: Yes , very true very true grad h: Normalized to how much postdoc f: it would be good to normalize with respect to that . Now on the table I did take one step toward ,  away from the raw frequencies by putting ,  percentages . So that the percentage of time of the of the times that a person spoke , what percentage eh , w so . Of the times a person spoke and furthermore was involved in a two two - person overlap , what percentage of the time were they the overlapper and what percent of the time were they th the overlappee ? And there , it looks like you see some differences , that some people tend to be overlapped with more often than they 're overlapped , but , of course ,  i e this is just one meeting ,  there 's no statistical testing involved , and that would be required for a for a finding of any kind of scientific reliability . professor d: S so , i it would be statistically incorrect to conclude from this that Adam talked too much or something . grad h: No no actually , that would be actually statistically correct , professor d: Yeah , yeah . And I 'm you know , I 'm I don't see a point of singling people out , professor d: B I I I rather enjoyed it , but but this postdoc f: now , this is a case where obviously phd a: But the numbers speak for themselves . postdoc f: Well , you know , it 's like I 'm not I 'm not saying on the tape who did better or worse grad h: Yes , that 's right , so you don't nee OK . postdoc f: because I don't think that it 's I you know , and and th here 's a case where of course , human subjects people would say be sure that you anonymize the results , and and , so , might as well do this . grad h: Yeah , when this is what This is actually when Jane sent this email first , is what caused me to start thinking about anonymizing the data . postdoc f: And actually , you know , the point is not about an individual , it 's the point about tendencies toward you know , different styles , different speaker styles . postdoc f: And it would be , you know of course , there 's also the question of what type of overlap was this , and w what were they , and i and I and I know that I can distinguish at least three types and , probably more , the general cultural idea which w  , the conversation analysts originally started with in the seventies was that we have this strict model where politeness involves that you let the person finish th before you start talking , and and you know , w we know that an and they 've loosened up on that too s in the intervening time , that that that 's that 's viewed as being a culturally - relative thing , that you have the high - involvement style from the East Coast where people will overlap often as an indication of interest in what the other person is saying . phd b: Exactly ! postdoc f: Yeah , exactly ! phd e: Yeah postdoc f: Well , there you go . And and , you know , in contrast , so Deborah d and also Deborah Tannen 's thesis she talked about differences of these types , that they 're just different styles , and it 's  you you can't impose a model of there of the ideal being no overlaps , and you know , conversational analysts also agree with that , so it 's now , universally a ag agreed with . And and , als  , I can't say universally , but anyway , the people who used to say it was strict ,  now ,  don't .  they they also you know , ack acknowledge the influence of sub of subcultural norms and cross - cultural norms and things . So ,  Then it beco though so just just superficially to give  a couple ideas of the types of overlaps involved , I have at the bottom several that I noticed . So , there are backchannels , like what Adam just did now and , anticipating the end of a question and simply answering it earlier , and there are several of those in this in these data where phd b:   postdoc f: because we 're people who 've talked to each other ,  we know basically what the topic is , what the possibilities are and w and we 've spoken with each other so we know basically what the other person 's style is likely to be and so and t there are a number of places where someone just answered early . And places also which I thought were interesting , where two or more people gave exactly th the same answer in unison different words of course but you know , the basically , you know everyone 's saying " yes " or you know , or ev even more sp specific than that . So , the point is that ,  overlap 's not necessarily a bad thing and that it would be im i useful to subdivide these further and see if there are individual differences in styles with respect to the types involved . professor d: Well , of course th the biggest ,  result here , which is one we 've we 've talked about many times and isn't new to us , but which I think would be interesting to show someone who isn't familiar with this is just the sheer number of overlaps . professor d: That that Right ? that that ,  phd e: Yes , yes ! postdoc f: Oh , OK interesting . professor d: here 's a relatively short meeting , it 's a forty forty plus minute meeting , and not only were there two hundred and fifteen overlaps but ,  I think there 's one one minute there where there where where there wasn't any overlap ? grad h: Hundred ninety - seven . professor d: it 's  throughout this thing ? phd a: It 'd be interesting professor d: It 's You have postdoc f: Well , at the bottom , you have the bottom three . grad h: S n are postdoc f: So four four minutes all together with none none . phd a: But it w professor d: Oh , so the bottom three did have s stuff going on ? There was speech ? postdoc f: Yes ,  - huh . professor d: OK , so if the this phd a: It 'd be interesting to see what the total amount of time is in the overlaps , versus postdoc f: Yes , exactly and that 's that 's where Jose 's pro project comes in . phd e: Yeah , yeah , I h I have this that infor I have th that information now . professor d: Oh , about how much is it ? phd e: The the duration of eh of each of the overlaps . professor d: O oh , what 's what 's the what 's the average length ? phd e: M I I haven't averaged it now but ,  I I will ,  I will do the the study of the with the with the program with the  , the different ,  the , nnn , distribution of the duration of the overlaps . professor d: You don't know ? OK , you you don you don't have a feeling for roughly how much it is ? Yeah . phd e: The duration is ,  the variation the variation of the duration is  , very big on the dat phd a:   postdoc f: I suspect that it will also differ , depending on the type of overlap involved . postdoc f: So backchannels will be very brief phd e: Because , on your surface eh a bit of zone of overlapping with the duration eh , overlapped and another very very short . phd e: i probably it 's very difficult to to because the the overlap is ,  on is only the in the final " S " of the of the the fin the the end the end word of the ,  previous speaker with the the next word of the the new speaker . phd e: I considered that 's an overlap but it 's very short , it 's an " X " with a and the idea is probably , eh when eh when eh , we studied th th that zone , eh eh , we h we have eh eh confusion with eh eh noise . phd e: With eh that fricative sounds , but  I have new information but I have to to study . phd g: You split this by minute ,  so if an overlap straddles the boundary between two minutes , that counts towards both of those minutes .  , so le let 's think about the case where A starts speaking and then B overlaps with A , and then the minute boundary happens . And let 's say that after that minute boundary ,  B is still speaking , and A overlaps with B , that would be a new overlap . But otherwise  , let 's say B comes to the conclusion of of that turn without anyone overlapping with him or her , in which case there would be no overlap counted in that second minute . phd g: No , but suppose they both talk simultaneously both a a portion of it is in minute one and another portion of minute two . In that case ,  my c the coding that I was using since we haven't ,  incorporated Adam 's ,  coding of overlap yets , the coding of Yeah , " yets " is not a word .  since we haven't incorporated Adam 's method of handling overl overlaps yet  then that would have fallen through the cra cracks . It would be an underestimate of the number of overlaps because ,  I wou I wouldn't be able to pick it up from the way it was encoded so far . professor d: I I postdoc f: We just haven't done th the precise second to sec you know , second to second coding of when they occur . professor d: Let 's say that in in second fifty - seven of one minute , you start talking and I start talking and we ignore each other and keep on talking for six seconds . professor d: So we go over So we were we were talking over one another , and it 's just in each case , it 's just sort of one interval . Right ? postdoc f:  -  ? professor d: So ,  we talked over the minute boundary . professor d: OK , so that 's good , i I think , in the sense that I think Andreas meant the question , phd b: That 's that 's good , yeah , cuz the overall rate is phd c:  phd g: Yeah . postdoc f: I should also say I did a simplifying ,  count in that if A was speaking B overlapped with A and then A came back again and overlapped with B again , I I didn't count that as a three - person overlap , I counted that as a two - person overlap , and it was A being overlapped with by D . postdoc f: Because the idea was the first speaker had the floor and the second person started speaking and then the f the first person reasserted the floor kind of thing . postdoc f: These are simplifying assumptions , didn't happen very often , there may be like three overlaps affected that way in the whole thing . grad h: Cuz i i I find it interesting that there were a large number of overlaps and they were all two - speaker . grad h:  what I thought what I would have thought in is that when there were a large number of overlaps , it was because everyone was talking at once , but  apparently not . postdoc f: Yeah , there 's a lot of backchannel , a lot o a lot of grad h: This is really interesting data . phd b: I think what 's really interesting though , it is before d saying " yes , meetings have a lot of overlaps " is to actually find out how many more we have than two - party . postdoc f: I think so too , I think phd b: Cuz in two - party conversations , like Switchboard , there 's an awful lot too if you just look at backchannels , if you consider those overlaps ? it 's also ver it 's huge . It 's just that people haven't been looking at that because they 've been doing single - channel processing for speech recognition . professor d:  -  ? phd b: So , the question is , you know , how many more overlaps do you have of , say the two - person type , by adding more people . professor d: Well , but see , I find it interesting even if it wasn't any more , phd b: So . professor d: because since we were dealing with this full duplex sort of thing in Switchboard where it was just all separated out we just everything was just nice , phd b:  -  ? professor d: so that so the issue is in in a situation where th that 's phd b: Well , it 's not really " nice " . So if you were actually having ,  depends what you 're doing , if Right now we 're do we have individual mikes on the people in this meeting . So the question is , you know " are there really more overlaps happening than there would be in a two - person party " . professor d:  -  ? phd b: And and there well may be , but professor d: Let let m let me rephrase what I 'm saying cuz I don't think I 'm getting it across . What what I what I shouldn't use words like " nice " because maybe that 's too i too imprecise . But what  is that ,  in Switchboard , despite the many many other problems that we have , one problem that we 're not considering is overlap . And what we 're doing now is , aside from the many other differences in the task , we are considering overlap and one of the reasons that we 're considering it , you know , one of them not all of them , one of them is that w  at least , you know I 'm very interested in the scenario in which ,  both people talking are pretty much equally audible , and from a single microphone . And so , in that case , it does get mixed in , and it 's pretty hard to jus to just ignore it , to just do processing on one and not on the other . phd b: I I agree that it 's an issue here but it 's also an issue for Switchboard and if you think of meetings being recorded over the telephone , which I think , you know , this whole point of studying meetings isn't just to have people in a room but to also have meetings over different phone lines . phd b: Maybe far field mike people wouldn't be interested in that but all the dialogue issues still apply , professor d:   phd b: so if each of us was calling and having a meeting that way you kn you know like a conference call . And , just the question is , y you know , in Switchboard you would think that 's the simplest case of a meeting of more than one person , professor d:   phd b: and I 'm wondering how much more overlap of the types that that Jane described happen with more people present . So it may be that having three people is very different from having two people or it may not be . professor d: I think what I 'm All I 'm s really saying is that I don't think we were considering that in Switchboard . But  but but professor d: Were you ? grad h: Though it wasn't in the design . professor d: Were you were you were you were you measuring it ?  , w w were phd b: There there 's actually to tell you the truth , the reason why it 's hard to measure is because of so , from the point of view of studying dialogue , which Dan Jurafsky and Andreas and I had some projects on , you want to know the sequence of turns . phd b: So what happens is if you 're talking and I have a backchannel in the middle of your turn , and then you keep going what it looks like in a dialogue model is your turn and then my backchannel , professor d: Yeah . professor d: Yeah ? phd b: So , for things like language modeling or dialogue modeling it 's We know that that 's wrong in real time . professor d: Yeah ? phd b: But , because of the acoustic segmentations that were done and the fact that some of the acoustic data in Switchboard were missing , people couldn't study it , but that doesn't mean in the real world that people don't talk that way . phd b: Well , we 've als professor d: And and and , you you maybe wanted to look at it before but , for these various technical reasons in terms of how the data was you weren't . professor d: So that 's why it 's coming to us as new even though it may well be you know , if your if your hypothes The hypothesis you were offering eh phd b:  . professor d: Right ? if it 's the null poth hypothesis , and if actually you have as much overlap in a two - person , we don't know the answer to that . The reason we don't know the answer to is cuz it wasn't studied and it wasn't studied because it wasn't set up . Right ? phd b: Yeah , all I meant is that if you 're asking the question from the point of view of what 's different about a meeting , studying meetings of , say , more than two people versus what kinds of questions you could ask with a two - person meeting . professor d:  -  ? phd b: It 's important to distinguish that , you know , this project is getting a lot of overlap but other projects were too , but we just couldn't study them . Right ? phd b: Well , there is a high rate , professor d: We do kn we don't know the numbers . It 's but I don't know how high , in fact phd a: Well , here I have a question . professor d: See , i i le let me t  , my point was just if you wanted to say to somebody , " what have we learned about overlaps here ? " just never mind comparison with something else , phd b:   professor d: what we 've learned about is overlaps in this situation , is that the first the first - order thing I would say is that there 's a lot of them . professor d: In in the sense that i if you said if i i i phd b: Yeah , I I don't di I agree with that . professor d: In a way , I guess what I 'm comparing to is more the common sense notion of how how much people overlap .  you know the fact that when when when , Adam was looking for a stretch of of speech before , that didn't have any overlaps , and he w he was having such a hard time and now I look at this and I go , " well , I can see why he was having such a hard time " . All I 'm saying is that from the professor d: I 'm saying if I I 'm saying if I have this complicated thing in front of me , and we sh which , you know we 're gonna get much more sophisticated about when we get lots more data , But Then , if I was gonna describe to somebody what did you learn right here , about , you know , the the modest amount of data that was analyzed I 'd say , " Well , the first - order thing was there was a lot of overlaps " . In fact and it 's not just an overlap bunch of overlaps second - order thing is it 's not just a bunch of overlaps in one particular point , but that there 's overlaps ,  throughout the thing . phd b: I 'm just saying that it may the reason you get overlaps may or may not be due to sort of the number of people in the meeting . phd b: because some of the data say Switchboard , which isn't exactly the same kind of context ,  these are two people who don't know each other and so forth , But we should still be able to somehow say what what is the added contra contribution to sort of overlap time of each additional person , or something like that . professor d: Yeah , that would be good to know , phd a: What professor d: but w we postdoc f: OK , now . postdoc f: And the reason is because I think there 's a limit there 's an upper bound on how many you can have , simply from the standpoint of audibility . postdoc f: children don't adjust so well , if a truck goes rolling past , adults will well , depending , but mostly , adults will will will hold off to what to finish the end of the sentence till the till the noise is past . postdoc f: And I think we generally do monitor things like that , about whether we whether our utterance will be in the clear or not . postdoc f: And partly it 's related to rhythmic structure in conversation , so , you know , you you t Yeah , this is d also  , people tend to time their their their ,  when they come into the conversation based on the overall rhythmic , ambient thing . And and , just to finish this , that  That I think that there may be an upper bound on how many overlaps you can have , simply from the standpoint of audibility and how loud the other people are who are already in the fray . Now if it 's just backchannels , people may be doing that with less intention of being heard , just sort of spontaneously doing backchannels , in which case that those might there may be no upper bound on those . phd g: I I have a feeling that backchannels , which are the vast majority of overlaps in Switchboard , don't play as big a role here , because it 's very unnatural I think , to backchannel if in a multi - audience you know , in a multi - person audience . It 's interesting , so if you watch people are going like Right right , like this here , phd g: Right . professor d: u phd g: But but , it 's sort of odd if one person 's speaking and everybody 's listening , and it 's unusual to have everybody going "  - huh ,  - huh " professor d: Actually , I think I 've done it a fair number of times today . So so actually ,  That 's in part because the nodding , if you have visual contact , the nodding has the same function , but on the phone , in Switchboard you you that wouldn't work . Your mike is phd a: So , in the two - person conversations , when there 's backchannel , is there a great deal of overlap in the speech ? grad h: That is an earphone , so if you just put it so it 's on your ear . phd a: or Cuz my impression is sometimes it happens when there 's a pause , phd b: Yes . phd a: you know , like you you get a lot of backchannel , when somebody 's pausing phd b: Yes . phd b: Sorry , what were you saying ? phd a: It 's hard to do both , huh ?  no , when when when there 's backchannel , just I was just listening , and and when there 's two people talking and there 's backchannel it seems like ,  the backchannel happens when , you know , the pitch drops and the first person phd b: Oh . phd a: and a lot of times , the first person actually stops talking and then there 's a backchannel and then they start up again , and so I 'm wondering about h I just wonder how much overlap there is . Is there a lot ? phd b: I think there 's a lot of the kind that Jose was talking about , where  , this is called " precision timing " in conversation analysis , where they come in overlapping , but at a point where the information is mostly complete . So all you 're missing is some last syllables or something or the last word or some highly predictable words . phd a: But maybe a just a small overlap ? phd b: But you know , from information flow point of view it 's not an overlap in the predictable information . phd g: So so phd b: Well , that 's exactly , exactly why we wanted to study the precise timing of overlaps ins in  Switchboard , professor d: Yeah .  , to distinguish between , say , backchannels precision timing Sort of you know , benevolent overlaps , and and and w and and sort of ,  I don't know , hostile overlaps , where someone is trying to grab the floor from someone else . I ju I I think that in this meeting I really had the feeling that wasn't happening , that the hostile hostile type . phd g: I could imagine that as there 's a fair number of  cases where , and this is sort of , not really hostile , but sort of competitive , where one person is finishing something and you have , like , two or three people jumping trying to trying to trying to ,  grab the next turn . phd g: And so it 's not against the person who talks first because actually we 're all waiting for that person to finish . professor d: I have a feeling most of these things are that that are not a benevolent kind are are are ,   are are competitive as opposed to real really really hostile . phd a:  postdoc f: Well , there are various things , you you have the professor d:  a vote vote in Florida . phd e: Voting for professor d: o one thing I I wanted to or you can tell a good joke and then everybody 's laughing and you get a chance to g break in . You know , the other thing I was thinking was that ,  these all these interesting questions are , of course , pretty hard to answer with ,  u you know , a small amount of data . professor d: So ,  I wonder if what you 're saying suggests that we should make a conscious attempt to have ,  a a fair number of meetings with ,  a smaller number of people . Right ?  we most of our meetings are  , meetings currently with say five , six , seven , eight people Should we really try to have some two - person meetings , or some three - person meetings and re record them just to to to beef up the the statistics on that ? postdoc f: That 's a control . Well , it seems like there are two possibilities there ,  i it seems like if you have just two people it 's not really , y like a meeting , w is not as similar as the rest of the of the sample . It depends on what you 're after , of course , but It seems like that would be more a case of the control condition , compared to ,  an experimental condition , with more than two . professor d: Well , Liz was raising the question of of whether i it 's the number there 's a relationship between the number of people and the number of overlaps or type of overlaps there , postdoc f:   professor d: and ,  If you had two people meeting in this kind of circumstance then you 'd still have the visuals . professor d: Yeah , acoustic is fine , but phd g: If if the goal were to just look at overlap you would you could serve yourself save yourself a lot of time but not even transcri transcribe the words . phd b: Well , I was thinking you should be able to do this from the acoustics , on the close - talking mikes , grad h: Yep . grad h: Well , that 's the that was my my status report , phd b: right ? postdoc f: You 've been working on that .  , not as well as what  , you wouldn't be able to have any kind of typology , obviously , professor d: Yeah . professor d: But what what do you think about that ? Do you think that would be useful ? I 'm just thinking that as an action item of whether we should try to record some two - person meetings or something . phd b: I guess my my first comment was ,  only that  we should n not attribute overlaps only to meetings , but maybe that 's obvious , maybe everybody knew that , professor d: Yeah . phd b: but that in normal conversation with two people there 's an awful lot of the same kinds of overlap , and that it would be interesting to look at whether there are these kinds of constraints that Jane mentioned , that what maybe the additional people add to this competition that happens right after a turn , professor d:   phd b: you know , because now you can have five people trying to grab the turn , but pretty quickly there 're they back off and you go back to this sort of only one person at a time with one person interrupting at a time . To answer your question I it I don't think it 's crucial to have controls but I think it 's worth recording all the meetings we can . phd b: D I wouldn't not record a two - person meeting just because it only has two people . phd g: Could we Could we ,  we have have in the past and I think continue will continue to have a fair number of  phone conference calls . phd g: And , and as a to ,  as another c c comparison condition , we could  see what what what happens in terms of overlap , when you don't have visual contact . phd g: So ,  phd b: Can we actually record ? grad h: It just seems like that 's a very different thing than what we 're doing . phd g: Or , this is getting a little extravagant , we could put up some kind of blinds or something to to remove ,  visual contact . grad h: Barriers ! phd b: That 's what they did on Map Task , you know , this Map Task corpus ? They ran exactly the same pairs of people with and without visual cues and it 's quite interesting . professor d: Well , we we record this meeting so regularly it wouldn't be that  a little strange . That would professor d: Oh , th that was the other thing , weren't we gonna take a picture at the beginning of each of these meetings ? grad h: what I had thought we were gonna do is just take pictures of the whiteboards . phd a: Why why do we want to have a picture of the meeting ? phd b: Ee - you mean , transc no postdoc f: The because you get then the spatial relationship of the speakers . postdoc f: And that could be phd g: Well , you could do that by just noting on the enrollment sheet the the seat number . phd a: We can can't you figure it out from the mike number ? grad h: No . And even the jacks , I 'm sitting here and the jack is over in front of you . phd g: Yeah , but It 's it would be trivial grad h: It would be another task . grad h: So I 'm gonna put little labels on all the chairs with the seat number . grad h: But ,  phd g: Put them Like , put them on the table where they phd e: The chair Yeah . postdoc f: But you know , they the s the linguistic anthropologists would say it would be good to have a digital picture anyway , phd a: Just remembered a joke . Posture , and we could like , you know , block out the person 's face or whatever phd g: What people were wearing . postdoc f: but but , you know , these are important cues , phd g: Oh , Andreas was phd a: How big their heads are . postdoc f:  the the how a person is sitting is professor d: But if you just f But from one picture , I don't know that you really get that . postdoc f: It 'd be better than nothing , is is i Just from a single picture I think you can tell some aspects . professor d: Think so ? postdoc f:  I I could tell you  , if I if I 'm in certain meetings I notice that there are certain people who really do eh The body language is very  is very interesting in terms of the dominance aspect . postdoc f: But it 's just , you know , the the body phd a: He agreed . postdoc f: you know ? grad h: Of course , the where we sit at the table , I find is very interesting , that we do tend to cong to gravitate to the same place each time . I 'm sitting here so that I can run into the room if the hardware starts , you know , catching fire or something . phd g: Oh , no , you you just like to be in charge , that 's why you 're sitting grad h: I just want to be at the head of the table . grad h: Yeah , I 've been playing with , using the close - talking mike to do to try to figure out who 's speaking . So my first attempt was just using thresholding and filtering , that we talked about about two weeks ago , and so I played with that a little bit , and it works O K , except that it 's very sensitive to your choice of your filter width and your threshold . So if you fiddle around with it a little bit and you get good numbers you can actually do a pretty good job of segmenting when someone 's talking and when they 're not . But if you try to use the same paramenters on another speaker , it doesn't work anymore , even if you normalize it based on the absolute loudness . phd b: But does it work for that one speaker throughout the whole meeting ? grad h: It does work for the one speaker throughout the whole meeting . grad h: What do you mean ? phd a: wh what was the grad h: The algorithm was ,  take o every frame that 's over the threshold , and then median - filter it , and then look for runs . grad h: So there was a minimum run length , phd a: Every frame that 's over what threshold ? grad h: so that A threshold that you pick . grad h: So you take a each frame , and you compute the energy and if it 's over the threshold you set it to one , and if it 's under the threshold you set it to zero , so now you have a bit stream of zeros and ones .  well , actually I guess depends on what you mean by long , you know , tenth of a second sorts of numbers .  and that 's to average out you know , pitch , you know , the pitch contours , and things like that . postdoc f: OK grad h: And that works O K , if you fil if you tune the filter parameters , if you tune how long your median filter is and how high you 're looking for your thresholds . phd a: Did you ever try running the filter before you pick a threshold ? grad h: No . OK and then the other thing I did , was I took Javier 's speaker - change detector acoustic - change detector , and I implemented that with the close - talking mikes , and unfortunately that 's not working real well , and it looks like it 's the problem is he does it in two passes , the first pass is to find candidate places to do a break . And he does that using a neural net doing broad phone classification and he has the the ,  one of the phone classes is silence . And what looks like it 's happening is that the even on the close - talking mike the broad phone class classifier 's doing a really bad job . Does an do you remember , Morgan , was it Broadcast News ? professor d: I think so , yeah . grad h:  So , at any rate , my next attempt , which I 'm in the midst of and haven't quite finished yet was actually using the  , thresholding as the way of generating the candidates . Because one of the things that definitely happens is if you put the threshold low you get lots of breaks . But , like , it could be someone who isn't the person here , but the person over there or it can be the person breathing . But all of this is close - talking mike , so it 's ,  just just trying to get some ground truth . phd e: Only with eh  , but eh I I I think , eh when when , y I I saw the the the the speech from PDA and , eh close talker . phd e:  but eh I but eh I I  that eh eh in the in the mixed file you can find ,  zone with , eh great different , eh level of energy . not phd e:  I I think for , eh algorithm based on energy , eh , that  h mmm , more or less , eh , like eh eh , mmm , first sound energy detector . When y you the detect the the the first at at the end of of the detector of , ehm princ  . What is the the name in English ? the the , mmm , the de detector of , ehm of a word in the in the s in an isolated word in in the background That ,  grad h: I 'm I 'm not sure what you 're saying , can you try phd e:  that when when you use , eh eh any phd a: I think he 's saying the onset detector . phd e: I I think it 's probably to work well eh , because , eh you have eh , in the mixed files a great level of energy . And probably is not so easy when you use the the PDA , eh that Because the signal is , eh the in the e energy level . I think that ,  grad h: Ah , in the clo in the P D A , you mean ? phd e: In the PDA . I think the , nnn , the , nnn , that the idea of using a neural network to to get a broad class of phonetic , eh from , eh  a candidate from the the the speech signal . If you have , eh  , I 'm considering , only because Javier , eh only consider , eh like candidate , the , nnn , eh the silence , because it is the the only model , eh eh , he used that , eh eh nnn , to detect the the possibility of a a change between the between the speaker , grad h: Right . phd e:  another another research thing , different groups , eh working , eh on Broadcast News prefer to , eh to consider hypothesis eh between each phoneme . phd e: Because , I I I think it 's more realistic that ,  only consider the the the the silence between the speaker . phd e: I I found that the , eh silence in in many occasions in the in the speech file , but , eh when you have , eh eh , two speakers together without enough silence between between them , eh I think eh is better to use the acoustic change detector basically and I I I IX or , mmm , BIC criterion for consider all the frames in my opinion . Yeah , the you know , the reason that he ,  just used silence was not because he thought it was better , it was it was it was the place he was starting . professor d: and ,  e e you know , as as as is in your case , if you 're here for only a modest number of months you try to pick a realistic goal , phd e: Yeah , yeah , yeah , yeah . professor d: But his his goal was always to proceed from there to then allow broad category change also . But , eh do do you think that if you consider all the frames to apply the the , eh the BIC criterion to detect the the the different acoustic change , eh between speaker , without ,  with ,  silence or with overlapping , I think like like , eh eh a general , eh eh way of process the the acoustic change . phd e: An - and then , eh eh without considering the you you you ,  you can consider the energy like a another parameter in the in the feature vector , eh . And if , if you do that , eh eh , with a BIC  criterion for example , or with another kind of , eh of distance in a first step , and then you , eh you get the , eh the hypothesis to the this change acoustic , eh to po process grad h: Right . phd e: Because , eh eh , probably you you can find the the eh a small gap of silence between speaker with eh eh a ga mmm , small duration Less than , eh two hundred milliseconds for example professor d:   phd e: and apply another another algorithm , another approach like , eh eh detector of ene , eh detector of bass - tone energy to to consider that , eh that , eh zone . of s a small silence between speaker , or another algorithm to to process , eh the the segment between marks eh founded by the the the BIC criterion and applied for for each frame . phd e: I think is , eh nnn , it will be a an an a more general approach the if we compare with use , eh a neural net or another , eh speech recognizer with a broad class or or narrow class , because , in my opinion eh it 's in my opinion , eh if you if you change the condition of the speech , if you adjust to your algorithm with a mixed speech file and to , eh to , eh adapt the neural net , eh used by Javier with a mixed file . phd e:  With a m mixed file , grad h: With the what file ? phd a: " Mixed " . And and then you you , eh you try to to apply that , eh , eh , eh , speech recognizer to that signal , to the PDA , eh speech file , I I think you will have problems , because the the the the condition you you will need t t I I suppose that you will need to to to retrain it . professor d: u Look , I I think this is a One once It 's a I used to work , like , on voiced on voice silence detection , you know , and this is this kind of thing . professor d:  If you have somebody who has some experience with this sort of thing , and they work on it for a couple months , they can come up with something that gets most of the cases fairly easily . Then you say , " OK , I don't just wanna get most of the cases I want it to be really accurate . So , the p the problem is is that if you say , " Well I I have these other data over here , that I learn things from , either explicit training of neural nets or of Gaussian mixture models or whatever . Well , what does that mean ? That that means you set some thresholds somewhere or something , phd e: Yeah . professor d: right ? and and so where do you get your thresholds from ? phd e: Yeah . So you always have this problem , you 're going to new data  H how are you going to adapt whatever you can very quickly learn about the new data ?  , if it 's gonna be different from old data that you have ? And I think that 's a problem with this . grad h: Well , also what I 'm doing right now is not intended to be an acoustic change detector for far - field mikes . What I 'm doing is trying to use the close - talking mike and just use Can - and just generate candidate and just try to get a first pass at something that sort of works . phd g: Or grad h: and I haven't spent a lot of time on it and I 'm not intending to spend a lot of time on it . phd g: but ,  I can imagine  building a  model of speaker change detection that takes into account both the far - field and the  actually , not just the close - talking mike for that speaker , but actually for all of th for all of the speakers . phd g:  If you model the the effect that me speaking has on your microphone and everybody else 's microphone , as well as on that , and you build ,  basically I think you 'd you would build a an  that has as a state space all of the possible speaker combinations grad h: All the Yep . professor d: But Actually , Andreas may maybe maybe just something simpler but but along the lines of what you 're saying , grad h: Anyway . professor d: I was just realizing , I used to know this guy who used to build , mike mixers automatic mike mixers where , you know , t in order to able to turn up the gain , you know ,  as much as you can , you you you lower the gain on on the mikes of people who aren't talking , phd g: Mmm . professor d: right ? And then he had some sort of reasonable way of doing that , phd g:   professor d: but  , what if you were just looking at very simple measures like energy measures but you don't just compare it to some threshold overall but you compare it to the energy in the other microphones . grad h: I was thinking about doing that originally to find out who 's the loudest , and that person is certainly talking . I I have found that when when I I analyzed the the speech files from the , eh mike , eh from the eh close eh microphone , eh I found zones with a a different level of energy . because , eh eh depend on the position of the of the microph of the each speaker to , eh , to get more o or less energy i in the mixed sign in the signal . and then , if you consider energy to to detect overlapping in in , and you process the the in the the the speech file from the the the mixed signals . I I think it 's it 's difficult ,  only to en with energy to to consider that in that zone We have eh , eh , overlapping zone Eh , if you process only the the energy of the , of each frame . professor d: Well , it 's probably harder , but I I think what I was s nnn noting just when he when Andreas raised that , was that there 's other information to be gained from looking at all of the microphones and you may not need to look at very sophisticated things , phd e: Yeah . professor d: because if there 's if most of the overlaps you know , this doesn't cover , say , three , but if most of the overlaps , say , are two , if the distribution looks like there 's a couple high ones and and the rest of them are low , phd e: Yeah . professor d: by the way , I had an idea with while I was watching Chuck nodding at a lot of these things , is that we can all wear little bells on our heads , so that then you 'd know that phd e: Yeah . That 's cute ! phd b: I think that 'd be really interesting too , with blindfolds . The question is , like whether grad h: " what are you nodding about ? " phd b: Well , trying with and with and without , yeah . phd a: Actually , I saw a  a woman at the bus stop the other day who , was talking on her cell phone speaking Japanese , and was bowing . phd b: It 's very difficult if you try while you 're trying , say , to convince somebody on the phone it 's difficult not to move your hands . I still think we should try a a meeting or two with the blindfolds , at least of this meeting that we have lots of recordings of grad h:   phd b: maybe for part of the meeting , we don't have to do it the whole meeting . It 'll be too hard to make barriers , I was thinking because they have to go all the way professor d: W Yeah . postdoc f: Actually well also I I can say I made barr barriers for so that the stuff I was doing with Collin wha which just used ,  this kind of foam board . You can you can masking tape it together , these are you know , pretty l large partitions . phd b: But then we also have these mikes , is the other thing I was thinking , so we need a barrier that doesn't disturb the sound , postdoc f: It 's true , it would disturb the ,  the the long - range grad h: The acoustics . phd b: it sounds weird but but you know it 's it 's cheap and ,  Be interesting to have the camera going . grad h: Yeah , that 's right , we didn't tell them we would be blindfolding . professor d: That 's postdoc f: " Do you mind being blindfolded while you 're interviewed ? " professor d: that 's that 's that 's the one that we videotape .  I did have this other agenda item which is ,  @ @ it 's  a list which I sent to  a couple folks , but  I wanted to get broader input on it , So this is the things that I think we did in the last three months obviously not everything we did but but sort of highlights that I can can tell s some outside person , you know , what what were you actually working on .  in no particular order  , one , ten more hours of meeting r meetings recorded , something like that , you know from from ,  three months ago .  , pilot data put together and sent to IBM for transcription ,  next batch of recorded data put together on the CD - ROMs for shipment to IBM , grad h: Hasn't been sent yet , but It 's getting ready .  human subjects approval on campus ,  and release forms worked out so the meeting participants have a chance to request audio pixelization of selected parts of the spee their speech .  preliminary analysis of overlaps in the pilot data we have transcribed , and exploratory analysis of long - distance inferences for topic coherence , that was I was wasn't sure if those were the right way that was the right way to describe that because of that little exercise that that you and and Lokendra did . postdoc f: What was that called ? professor d: I well , I I 'm probably saying this wrong , but what I said was exploratory analysis of long - distance inferences for topic coherence .  so ,  I a lot of that was from , you know , what what what you two were doing so I I sent it to you , and you know , please mail me , you know , the corrections or suggestions for changing grad h:   professor d: I I don't want to make this twice it 's length but but you know , just im improve it . professor d: " Bunch of stuff for s " OK , maybe maybe send me a sentence that 's a little thought through about that . grad h: So , OK , I 'll send you a sentence that doesn't just say " a bunch of " ? professor d: " Bunch of stuff " , yeah , " stuff " is probably bad too , grad h: Yep .  and and you know , I sort of threw in what you did with what Jane did on in under the ,   preliminary analysis of overlaps .   Thilo , can you tell us about all the work you 've done on this project in the last ,  last three months ? phd e: Yeah . Wh - what is " audio pixelization " ? professor d: audio pix wh he did it , so why don't you explain it quickly ? grad h: It 's just ,  beeping out parts that you don't want included in the meeting so , you know you can say things like , " Well , this should probably not be on the record , but beep " phd c: OK , OK . We we we spent a a a fair amount of time early on just talk dealing with this issue about op w e e we realized , " well , people are speaking in an impromptu way and they might say something that would embarrass them or others later " , and , how do you get around that phd c:  OK . professor d: so in the consent form it says , well you we will look at the transcripts later and if there 's something that you 're unhappy with , yeah . professor d: But you don't want to just totally excise it because   , well you have to be careful about excising it , how how you excise it keeping the timing right and so forth so that at the moment tho th the idea we 're running with is is h putting the beep over it . grad h: if someone is listening to it , there 's no mistake that it 's been beeped out , phd c: Yeah . You can you know , you could make a m as long as you keep using the same beep , people could make a model of that beep , postdoc f:  . And I use it 's it 's ,  it 's an A below middle C beep , phd b: I think the beep is a really good idea . phd b: Also phd a: Yeah , it 's more obvious that there was something there than if there 's just silence . professor d: Yeah , that  , he 's he 's removing the old thing phd e: Yeah professor d: and and and grad h: Yep . But  if you just replaced it with silence , it 's not clear whether that 's really silence or grad h: Yeah , it 's not phd c: Yeah . postdoc f: Well , the other thing that you know ,  the the alternative might be to s grad h: Yeah . grad h: Sorry , and then the DARPA meeting I just excised completely , postdoc f: Yeah . professor d: Alright , so ,  I think we should , go on to the digits ? postdoc f: I have one concept a t I I want to say , which is that I think it 's nice that you 're preserving the time relations , grad h: OK . postdoc f: s so you 're you 're not just cutting you 're not doing scissor snips . phd b: So I guess if there 's an overlap , like , if I 'm saying something that 's bleepable and somebody else overlaps during it they also get bleeped , too ? professor d: Yeah .  I d I did before we do the digits , I did also wanna remind people ,  please do send me , you know ,  thoughts for an agenda , grad h: Agenda ? professor d: yeah that that would be that 'd be good . professor d: Eh So that , people 's ideas don't get grad h: Thursday crept up on me this week . professor d: yeah , well it does creep up , doesn't it ? phd b: And , I wanted to say , I think this is really interesting analysis . grad h: I was gonna say " can you do that for the other meetings , phd b: It 's neat . phd a: Actually actually I I thought that 's what you were giving us was another meeting and I was like , " Oh , OK ! " phd b: Does it take postdoc f: Thank you . I have the script now , so , it can work off the ,  other thing , grad h: It 's As soon as we get labels , yep . phd a: But it has to be hand - labeled first ? postdoc f: but  , well , yeah . postdoc f: But I I just worked off of my phd b: It 's really neat . I think what I what this has , caused me so this discussion caused me to wanna subdivide these further . I 'm gonna take a look at the ,  backchannels , how much we have anal I hope to have that for next time . grad h: Yeah , my my algorithm worked great actually on these , but when you wear it like that or with the  , lapel or if you have it very far from your face , that 's when it starts failing . phd b: Well , I can wear it ,  if you grad h: It doesn't matter . grad h: right ? I I don't want to change the way we do the meeting . grad h: It 's  so , it was just a comment on the software , not a comment on prescriptions on how you wear microphones . grad h: Get the bolts , " whh whh " postdoc f: Let 's do it . postdoc f: Do you want us to put a mark on the bottom of these when they 've actually been read , or do you just i i the only one that wasn't read is is known , so we don't do it


professor c: Typically when you you 're ab r starting around forty for most people , it starts to harden and then it 's just harder for the lens to shift things phd d: Oh . professor c: and th the the symptom is typically that you you have to hold stuff   further away to to see it . professor c: In fact ,  m my brother 's a gerontological psychologist and he he  came up with an an  a  body age test which  gets down to sort of only three measurements that are good enough st statistical predictors of all the rest of it . grad a: We 're we 're live by the way , so we 've got a good intro here professor c: Oh .  , she 's probably least involved in the signal - processing stuff so maybe we can just just  , I don't think we should go though an elaborate thing , but   Jose and I were just talking about the   , speech e energy thing , phd e: The @ @ professor c: and I  phd e: Yeah . But I think , you know , the the i if I can if you don't mind my my speaking for you for a bit ,   . Right now , that he 's not really showing any kind of  distinction , but  but we discussed a couple of the possible things that  he can look at . And  one is that  this is all in log energy and log energy is basically compressing the distances  between things . He was he he was taking everything over two hundred milliseconds  , and  he 's going to vary that number and also look at moving windows , as we discussed before .  And  and the other thing is that the yeah doing the subtracting off the mean and the variance in the  and dividing it by the standard deviation in the log domain , may not be the right thing to do . grad a: Could you take that mike there ? phd d: Are these the long term means ? Like , over the whole  , the means of what ? grad a: Thanks . professor c:  B Between between phd d: All the frames in the conversation ? professor c: No . professor c: And so i i his his He 's making the constraint it has to be at least two hundred milliseconds . And then he 's he 's  measuring at the frame level still at the frame level , of what phd d: Right .  and But one thing he was pointing out is when he he looked at a bunch of examples in log domain , it is actually pretty hard to see the change . And you can sort of see that , because of j of just putting it on the board that if you sort of have log - X plus log - X , that 's the log of X plus the log of two phd e: Yep . professor c: and it 's just , you know , it it diminishes the effect of having two of them . phd d: But you could do like a C D F there instead ?  , we don't know that the distribution here is normally . So So what I was suggesting to him is that phd d: So just some kind of a simple professor c: Actually , a PDF . professor c: Yeah , but I think also u I think a good first indicator is when the the the researcher looks at examples of the data and can not see a change in how big the the signal is , when the two speaker phd e: Yeah . professor c: doing casual looking and can get the sense , " Hey , there 's something there . And when he 's looking in straight energy he is , so that 's a good place to start .  , when you did the sampling ?  over the speech segments or s or sampling over the the individual channels in order to do the e  the amplitude equalization , did you do it over just the entire everything in the mike channels ? phd e: How professor c: You didn't try to find speech ? grad a: No , I just took over the entire s  entire channel  sampled ten minutes randomly . professor c: And someone who would who would be So the normalization factor probably is i i i is is grad a: Yeah , this was quite quick and dirty , and it was just for listening . So yeah there there there There 's a good chance then given that different people do talk different amounts that there is there there is still a lot more to be gained from gain norm normalization with some sort phd e: Yeah . But we were agreed that in addition to that  there should be s stuff related to pitch and harmonics and so forth . professor c: So we didn't talk at all about  the other derivatives , but  again just just looking at  , I think  Liz has a very good point , that in fact it would be much more graphic just to show Well , actually , you do have some distributions here ,  for these cases . professor c: You have some histograms ,  and  , they don't look very separate . phd e: This is the the first derivate of log of frame energy  without any kind of normalization . grad a: Except that it 's hard to judge this because the they 're not normalized . phd d: W  , what I meant is , even if you use linear , you know , raw measures , like raw energy or whatever , professor c: " Number " phd d: maybe we shouldn't make any assumptions about the distribution 's shape , and just use you know , use the distribution to model the the mean , or what y you know , rather than the mean take some professor c: Yeah . grad a: Interesting phd e: Here I I professor c: Oh that yeah that 's a good q phd e: in No I I I haven't the result professor c: did did you have this sort of thing , for just the just the l r  the the unnormalized log energy ? OK . professor c: That 's a phd d: Well it might be just good to know what it looks like . That 's That 's  cuz I 'd mentioned scatter plots before but she 's right , phd d: Cuz phd e: Huh ? professor c: even before you get the scatter plots , just looking at a single feature  , looking at the distribution , is a good thing to do . professor c: Yeah , but but what she 's saying is , which is right , is le phd e: combination of two , of energy and derivate professor c: let 's start with the Before we get complicated , let 's start with the most basic wh thing , which is we 're arguing that if you take energy  if you look at the energy , that , when two people are speaking at the same time , usually there 'll be more energy than when one is right ? phd e: Yeah . professor c: And the first way you 'd look at that ,  s she 's , you know , absolutely right , is that you would just take a look at the distribution of those two things , much as you 've plotted them here , phd e: Yeah . professor c: So ,  with three colors or three shades or whatever , just just look at those distributions . professor c: And then , given that as a base , you can see if that gets improved , you know , or or or worsened by the looking at regular energy , looking at log energy , we were just proposing that maybe it 's you know , it 's harder to see with the log energy ,  and  also these different normalizations , does a particular choice of normalization make it better ? phd e: Yeah . professor c: But I had maybe made it too complicated by suggesting early on , that you look at scatter plots because that 's looking at a distribution in two dimensions . professor c:  And then we w I think we 're agreed that pitch - related things are are are going to be a a really likely candidate to help . professor c:  But since  your intuition from looking at some of the data , is that when you looked at the regular energy , that it did in fact usually go up , when two people were talking , that 's eh you know , you should be able to come up with a measure which will match your intuition . professor c: And she 's right , that a that having a having having this table , with a whole bunch of things , with the standard deviation , the variance and so forth , it 's it 's it 's harder to interpret than just looking at the the same kind of picture you have here . But It it 's curious but  I f I found it in the in the mixed file , in one channel that eh in several oh e eh several times eh you have an speaker talking alone with a high level of energy professor c:   phd e: eh in the middle eh a zone of overlapping with mmm less energy professor c:   So there 'll be some cases for which phd e: Because there reach very many professor c: But , the qu So So they 'll be phd d: Right . professor c: This is I w want to point to visual things , But  they there 'll be time There 'll be overlap between the distributions , but the question is , " If it 's a reasonable feature at all , there 's some separation . grad a: And I was just going to say that that right now we 're just exploring . I grad a: What you would imagine eventually , is that you 'll feed all of these features into some discriminative system . grad a: And so even if if one of the features does a good job at one type of overlap , another feature might do a good job at another type of overlap .  the the reason I had suggested the scatter f p features is I used to do this a lot , when we had thirteen or fifteen or twenty features to look at . phd e: Yeah , this is the professor c:  Because something is a good feature  by itself , you don't really know how it 'll behave in combination and so it 's nice to have as many as many together at the same time as possible in  in some reasonable visual form . There 's cool graphic things people have had sometimes to put together three or four in some funny funny way . But it 's true that you shouldn't do any of that unless you know that the individual ones , at least , have have some  some hope phd e: Yeah . phd d: it 's really important to pick a normalization that matches the distribution for that feature . phd d: And it may not be the same for all the types of overlaps or the windows may not be the same . e Actually , I was wondering , right now you 're taking a all of the speech , from the whole meeting , and you 're trying to find points of overlap , but we don't really know which speaker is overlapping with which speaker , professor c: Right . phd d: right ? So  another way would just be to take the speech from just , say , Morgan , And just Jane and then just their overlaps , phd e: Yeah . phd d: like but by hand , by cheating , and looking at you know , if you can detect something that way , because if we can't do it that way , there 's no good way that we 're going to be able to do it . phd d: That You know , there might be something helpful and cleaner about looking at just individuals and then that combination alone . So if I don't know , if you go through and you find Adam , cuz he has a lot of overlaps and some other speaker who also has e enough speech phd e: Yeah . phd d: and just sort of look at those three cases of Adam and the other person and the overlaps , phd e: Yeah . phd d: maybe and just look at the distributions , maybe there is a clear pattern phd e: Yeah . phd d: but we just can't see it because there 's too many combinations of of people that can overlap . postdoc b: I think it 's to start with it 's s your your idea of simplifying , starting with something that you can see eh you know without the extra layers of phd d: Right . Cuz if energy doesn't matter there , like I don't think this is true , but what if phd e: To study individual ? postdoc b: Sorry , what ? phd d:  ? phd e: To study individual ? postdoc b: Well , you you you don't have to study everybody individually phd d: Well , to study the simplest case to get rid of extra phd e: The the the But Consider postdoc b: but just simple case and the one that has the lot of data associated with it . Cuz what if it 's the case and I don't think this is true grad a: That was a great overlap by the way . phd d: What if it 's the case that when two people overlap they equate their you know , there 's a conservation of energy and everybody both people talk more softly ? I don't think this happens at all . postdoc b: Or or what if what if the equipment what if the equipment adjusts somehow , phd d: Or they get louder . postdoc b: there 's some equalizing in there ? phd d: Yeah or professor c: no we don't have that . grad a: Well , but But I think that 's what I was saying about different types of overlap . phd d: There are there are different types , and within those types , like as Jose was saying , that sounded like a backchannel overlap , meaning the kind that 's a friendly encouragement , like "    , but , some of those , as you showed , I think can be discriminated by the duration of the overlap . It Actually the s new student , Don , who  Adam has met , and he was at one of our meetings He 's getting his feet wet and then he 'll be starting again in mid - January . phd d: and So maybe phd e: This is a s a general studio of the overlapping we 're studying the i professor c: Yeah . Well I I I I would s actually still recommend that he do the overall thing phd d: So it might be something that we can help by categorizing some of them and then , you know , look at that . professor c: he has the histogram mechanism , he has the stuff that subtracts out and all he has to do is change it   from from log to plain energy and plot the histogram and look at it . And then he should go on and do the other stuff bec but But this will phd d: Yeah . I didn't mean that that for you to do that , but I was thinking if if Don and I are trying to get categories professor c:   phd d: and we label some data for you , and we say this is what we think is going So you don't have to worry about it . phd e: Consider different class of overlap ? phd d: Yeah , that we would be working on anyway . phd d: Then maybe you can try some different things for those three cases , and see if that helps , or phd e: Yeah . This is the thing I I comment with you before , that  we have a great variation of th situation of overlapping . phd e: And the behavior for energy is ,  log energy , is not  the same all the time . phd e: And professor c: But I guess I was just saying that that right now  from the means that you gave , I don't have any sense of whether even , you know , there are any significant number of cases for which there is distinct and I would imagine there should be some you know , there should be The distributions should be somewhat separated . professor c:  and I I would still guess that if they are not separated at all , that there 's some there 's there 's most likely something wrong in the way that we 're measuring it . professor c: but  For instance ,  I wouldn't expect that it was very common overall , that when two people were talking at the same time , that it would that it really was lower , phd e: Yeah . phd d: or a sort of , a case where where you would never know that unless you actually go and look at two individuals . phd d: It might the case , though , that the significant energy , just as Jose was saying , comes in the non - backchannel cases . Because in back Most people when they 're talking don't change their own energy when they get a backchannel , cuz they 're not really predicting the backchannel . professor c: e e and and again what they what difference there was would kind of be lost in taking the log , phd d: and the backchannels are sort of easy to spot s in terms of their words or  , just listen to it . phd d: It just professor c: Mmm , no , if it 's if i if it 's grad a: Tone professor c: Well , it won't be as big . phd d: even if you take the log , you can your model just has a more sensitive measures . grad a: You could imagine doing specialized ones for different types of backchannels , if you could if you had a good model for it . professor c: If if you 're a I guess my point is , if you 're doing essentially a linear separation , taking the log first does in fact make it harder to separate . professor c: So it 's So ,  if you i i So i if there if there close to things it does phd e: Yeah . And right now we 're essentially doing this linear thing by looking across here and and saying we 're going to cut it here . But anyway , yeah , we 're not disagreeing on any of this , we should look at it more  more finely , but   I think that This often happens , you do fairly complicated things , and then you stand back from them and you realize that you haven't done something simple . So  , if you generated something like that just for the energy and see , and then , a a a as as Liz says , when they g have   smaller  , more coherent groups to look at , that would be another interesting thing later . professor c: And then that should give us some indication between those , should give us some indication of whether there 's anything to be achieved f from energy at all . But then the Have you started looking at the pitch related stuff at all , or ? Pitch related ? phd e: The ? professor c: Harmonicity and so on ? phd e: I I 'm preparing the the program but I don't I don't begin because eh I saw your email professor c: Preparing to Yeah . phd e: and I agree with you it 's better to I suppose it 's better to to consider the the energy this kind of parameter bef professor c: Yeah . I I I I Well , we certainly should see this but I I I I think that the harm I certainly wasn't saying this was better than the harmonicity and pitch related things I was just saying phd e: I I go on with the with the pitch , professor c: Yeah . professor c: Yeah , I was just saying phd e: I I I I understood  that eh I I had to finish by the moment with the and and concentrate my my energy in that problem . But I think , like , all these derivatives and second derivatives and all these other very fancy things , I think I would just sort of look at the energy and then get into the harmonicity as as a suggestion . So maybe  since w we 're trying to  compress the meeting , I know Adam had some form stuff he wanted to talk about and did you have some ? postdoc b: I wanted to ask just s something on the end of this top topic . So , when I presented my results about the  distribution of overlaps and the speakers and the profiles of the speakers , at the bottom of that I did have a proposal , phd e:  - huh . postdoc b: and I had plan to go through with it , of of co coding the types of overlaps that people were involved in s just with reference to speaker style so , you know , with reference phd d: Oh . postdoc b: and you know I said that on my in my summary , phd d: That 'd be great . postdoc b: that you know so it 's like people may have different amounts of being overlapped with or overlapping phd d: Yeah , I remem Right . postdoc b: but that in itself is not informative without knowing what types of overlaps they 're involved in so I was planning to do a taxonomy of types overlaps with reference to that . postdoc b: So , but it you know it 's like it sounds like you also have  something in that direction . postdoc b: Is is it phd d: We have nothing You know , basically , we got his environment set up . It 's mostly that , if we had to label it ourselves , we we would or we 'd have to , to get started , but if It it would be much better if you can do it . You 'd be much better at doing it also because you know , I I 'm not I don't have a good feel for how they should be sorted out , postdoc b: Interesting . phd d: and I really didn't wanna go into that if I didn't have to . So if If you 're w willing to do that or or grad a: It would be interesting , though , to talk , maybe not at the meeting , but at some other time about what are the classes . phd d: I think that 's a research effort in and of itself , phd e: Yeah . phd d: because you can read the literature , but I don't know how it 'll turn out phd e: Yeah . postdoc b: It seems like we also s with reference to a purpose , too , that we we 'd want to have them coded . phd d: And we 'd still have some funding for this project , phd e:   phd d: like probably , if we had to hire some like an undergrad , because  Don is being covered half time on something else postdoc b:   phd d: he we 're not paying him the full RA - ship for all the time .  If we got it to where we wanted we needed someone to do that I don't think there 's really enough data where where postdoc b:   Yeah , I see this as a prototype , to use the only the the already transcribed meeting as just a prototype . phd e: I I think a a another parameter we c we we can consider is eh the duration . Because is possible eh some s s  eh some classes eh has eh a type of a duration , eh , a duration very short  when we have we have overlapping with speech . And it 's interesting , I think , to consider the the window of normalization , normalization window . Eh because eh if we have a type of , a kind of eh overlap , eh backchannel overlap , with a short duration , is possible eh to normali i i that if we normalize eh with eh eh consider only the the eh window eh by the left eh ri eh side on the right side overlapping with a a very eh oh a small window eh the if the fit of normalization is eh mmm bigger eh in that overlapping zone eh very short postdoc b:    that you have eh you have a backchannel , eh , eh you have a overlapping zone very short phd d: Yeah . phd e: and you consider eh n eh all the channel to normalize this very short eh professor c:   phd e: for example " mmm  -   " eh And the energy is not eh height eh I think if you consider all the channel to normalize and the channel is mmm bigger eh eh eh compared with the with the overlapping eh duration , professor c:   phd e: eh the effect is mmm stronger eh that I  the the e effect of the normalization eh with the mean and the and the variance eh is different that if you consider only a window compared eh with the n the duration of overlapping . phd e: Is s If phd d: Well it 's a sliding window , right ? So if you take the the measure in the center of the overlapped piece , you know , there 'd better be some something . phd d: But if your window is really huge then yeah you 're right you won't even phd e: Yeah , This is the This is the the idea , to consider only the the small window near near near the the overlapping zone . You know , you shouldn't be more than like You should definitely not be three times as big as your as your backchannel . And hopefully it 's more like on the order of professor c: I 'm not sure that 's necessarily true . professor c: Because because it because  again if you 're just compensating for the gain , phd d: Yea phd e: Yeah . professor c: you know , the fact that this this gain thing was crude , and the gain wh if someone is speaking relatively at consistent level , just to to give a an extreme example , all you 're doing is compensating for that . And then you still s And then if you look at the frame with respect to that , it still should should  change phd d: Yeah , it depends how different your normalization is , as you slide your window across . postdoc b: It 's possible to try it both ways , grad a: Well ,  we 're also talking about a couple of different things . postdoc b: isn't it ? in this small grad a: one is your analysis window and then the other is any sort of normalization that you 're doing . phd d: But it is definitely true that we need to have the time marks , phd e: Yeah . phd d: and I was assuming that will be inherited because , if you have the words and they 're roughly aligned in time via forced alignment or whatever we end up using , then you know , this student and I would be looking at the time marks postdoc b: Yep , I agree . Coming off of the other phd d: and classifying all the frames inside those as whatever labels Jane gave phd e: Yeah . phd d:  phd e: I can give you my transcription file , postdoc b: I was thinking that that would come from the engineering side , phd d: I don't think you need to . That should be linked to the words which are linked to time somehow , postdoc b: There you go . grad a:  If it 's not hand - marked then we 're not going to get the times . phd d: Well , it 's something that w Well , we we wouldn't be able to do any work without a forced alignment anyway , phd e: Yes phd d: so somehow if once he gets going we 're gonna hafta come up with one professor c: Yes . professor c: Well again for the close mike stuff , we could come up take a s take the Switchboard system or something , grad a: That might be good enough . professor c: Just , you know , low - pass filter the speech and phd d: Cuz there 's there 's a lot of work you can't do without that , how how would you professor c: Yeah . phd d: You 'd have to go in and measure every start and stop point next to a word grad a: Yep . But , should talk about our options as far as the   transcription grad a: Yep , if IBM doesn't professor c: But . postdoc b: How So it 's two pages per person ? grad a: Nope . postdoc b: Oh ! grad a: So one is a one time only speaker form and the other is the digits .  why did you switch the order of the Date and Time fields ? This is rather a low - level , but grad a: On which one ? postdoc b: On on the new one , Time comes first and then Date , but I thought grad a: Oh you mean on the digit form ? postdoc b: This is this is rather a low level question , but but it used used to be Date came first . postdoc b: Well , how would the How would the user know the time if they didn't know the date ? grad a: It 's an interesting observation , but it was intentional . Because the date is when you actually read the digits and the time and , excuse me , the time is when you actually read the digits , but I 'm filling out the date beforehand . If you look at the form in front of you ? that you 're going to fill out when you read the digits ? you 'll see I 've already filled in the date but not the time . I always assumed So the time is supposed to be pretty exact , because I 've just been taking beginning time time of the meeting . grad a: The the reason I put the time in , is so that the person who 's extracting the digits , meaning me , will know where to look in the meeting , to try to find the digits . professor c: Why What what were you putting in ? postdoc b: Oh , well , I was saying if we started the meeting at two thirty , phd e: Yeah . postdoc b: I 'd put two thirty , and I guess d e everyone was putting two thirty , professor c: Oh . postdoc b: and I didn't realize there was "  oh I 'm about to read this and I should " grad a: Actually it 's about one third each . About one third of them are blank , about one third of them are when the digits are read , and about one third of them are when the meeting starts . postdoc b: This would be a radical suggestion but grad a: I could put instructions ? Nah . postdoc b: Ei - either that or maybe you could maybe write down when people start reading digits on that particular session . grad a: But if I 'm not at the meeting , I can't do that . professor c: For some reason he doesn't want to sit through every meeting that 's grad a: Yep , but that is the reason Name , Email and Time are where they are . phd e: " use only " postdoc b: Actually you could Well that does raise another question , which is why is the " Professional use only " line not higher ? Why doesn't it come in at the point of Date and Seat ? Oh . grad a: What ? professor c: What ? postdoc b: Well , because If y your your professional use , you 're gonna already have the date , and the s grad a: What which form are you talking about ? postdoc b: Well I 'm comparing the new one with the old one . grad a: The digit form doesn't The digit postdoc b: Oh ! I wasn't supposed to phd e: Yeah . grad a: So on the digits form , everything above the line is a fill - in form postdoc b: Sorry about that . Alright s but I didn't mean to derail our discussion here , so you really wanted to start with this other form . grad a: No , either way is fine I just You just started talking about something , and I didn't know which form you were referring to . postdoc b: Alright yeah , I was comparing so th this is So I was looking at the change first . So it 's like we started with this and now we 've got a new version of it wi with reference to this . professor c: So the main thing that the person fills out  is the name and email and time ? phd e: Yeah . postdoc b: What And there 's an addition of the native language , which is a bit redundant . grad a: That 's because the one , the digit form that has native language is the old form not the new form . professor c: " South Midland , North Midland " postdoc b: That 's the old and that 's the new . grad a: Yeah this was the problem with these categories , I I picked those categories from TIMIT . phd d: Actually , the only way I know is from working with the database and having to figure it out . grad a: So , I was gonna ask phd e: What i professor c: So is South Midland like Kansas ? grad a: wh w  . grad a: So so what accent are we speaking ? Western ? professor c: By definition ? phd e: And for simple for for me ? professor c: Well , phd d: Probably Western , yeah . phd e: Is mean my native language Spanish Spanish ? eh The original is the center of Spain and the beca grad a: Yeah ,  you could call it whatever you want . phd e: Because is different , the Span -  the Spanish language from the the north of Spain , of the south , of the west and the grad a: Sure . grad a: So I 'm not sure what to do about the Region field for English variety . You know , when I wrote I was writing those down , I was thinking , " You know , these are great if you 're a linguist " . grad a: But I don't know how to I don't know how to I don't know how to categorize them . professor c: If you 're if e if y phd d: This wasn't developed by th these regions weren't professor c: if you 're a TI or MIT from nineteen eighty - five . grad a: Yeah So I guess my only question was if if you were a South Midland speaking region , person ? Would you know it ? professor c: Yeah . grad a: Is that what you would call yourself ? phd d: I don't know . professor c: You know , I think if you 're talking if you 're thinking in terms of places , as opposed to names different peop names people have given to different ways of talking , I would think North Midwest , and South Midwest would be more common than saying Midland , right , I I went to s phd d: Yeah . Now the usage Maybe we can give them a li like a little map ? with the regions and they just No , I 'm serious . phd d: Because it takes less time , and it 's sort of cute phd e: i at this in that side in that side of the the paper . phd d: Well just a little You know , it doesn't have all the detail , but you sort of professor c: But what if you moved five times and and  postdoc b: Well , I was thinking you could have ma multiple ones and then the amount of time phd d: No , but you 're categorized . You could say , you know " ten years on the east coast , five years on the west coast " or something or other . grad a: Well , We I think we don't want to get that level of detail at this form . phd d:  I As I said , I don't think there 's a huge benefit to this region thing . It it gets The problem is that for some things it 's really clear and usually listening to it you can tell right away if it 's a New York or Boston accent , but New York and Boston are two well , I guess they have the NYC , but New England has a bunch of very different dialects and postdoc b:   grad a: Yeah , so I picked these regions cuz we had talked about TIMIT , and those are right from TIMIT . And so these would be satisfying like a speech research community if we released the database , grad a: So . phd d: but as to whether subjects know where they 're from , I 'm not sure because  I know that they had to fill this out for Switchboard . phd d: Also Northwest you 've got Oreg - Washington and Oregon now which  y people don't know if it 's western or northern . phd d: It 's like Northwest grad a: Am I speaking Am I speaking Western ? professor c: Oh , what is Northern ? Well and what and what 's Northern ? phd d: I think originally it was North Northwest grad a: Northwest ? phd d: But phd e: Yeah . And and so I would think I would say , I 've I 've got a mix of California and Ohio . grad a: I c I think at the first level , for example , we speak the same . grad a: a techno - speak accent ? phd d: Yeah , you know ? phd e: A techno grad a: A a geek region ? phd d: Well it 's  I you can sort of identify postdoc b: Geek region . phd d: it f It 's it 's not not that that 's phd e: Is different . phd d: but but maybe that maybe we could leave this and see what people See what people choose and then  let them just fill in if they don't  I don't know what else we can do , cuz That 's North Midland . postdoc b: I 'm wondering about a question like , " Where are you from mostly ? " phd e: Yeah . professor c: But I I 'm s I 'm now that you mentioned it though , I am really am confused by " Northern " . professor c: If you 're i if you 're postdoc b: Scandinavian , the Minnesota area 's north . professor c: right ? postdoc b:  professor c: And and and Oregon and and Oregon and Washington are are Western , but they 're also Northern . professor c: Maybe Maybe we Maybe we should put a little map and say " put an X on where you 're from " , phd d: No , that 's phd e: And is in those grad a: Yeah really . phd e: And if you put postdoc b: It 'd be pretty simple , yeah . If you put eh the state ? grad a: Well well we sort of postdoc b: Where are you from mostly ? phd d: We we went we went around this and then a lot of people ended up saying that it phd e:  - huh . grad a: Well , I like the idea of asking " what variety of English do you speak " as opposed to where you 're from Because th if we start asking where we 're from , again you have to start saying , " well , is that the language you speak or is that just where you 're from ? " phd e: Yeah . phd d:  it gives us good information on where they 're from , but that doesn't tell us anything grad a: And professor c: We could always ask them if they 're from phd d: well , enough about their grad a:  . So so I would say Germany phd d: like grad a: You know am I speaking with German accent postdoc b: Oh . postdoc b: Well , see , I 'm thinking " Where are you from mostly " phd d: Right . postdoc b: because , you know , then you have some some kind of subjective amount of time factored into it . grad a:  there 's not a lot of r of room professor c: I 'd say , " Boston , New York City , the South and Regular " . grad a: I think of those , Northern is the only one that I don't even know what they 're meaning . phd e: And And  And usually here people here know what is their kind of mmm lang English language ? professor c: That 's a joke . Right ? We can make up our own So we can say " Northwest " , " Rest of West " or something . postdoc b: I professor c: " Do you come from the Louisiana Purchase ? " phd d: So we could take out " North " " Northern " . phd e: eh here Is easy for people to know ? phd d: That 's Yeah , w It 's In It 's it 's harder in America anywhere else , basically . postdoc b: I can't do it , but phd e: Or Boston ? professor c: Yeah . phd d: They know they don't speak the same as the grad a: But is Boston New England ? postdoc b: And they 're proud of it . phd e:  phd d: But that 's why they have New York City but postdoc b: Well , we ca Well , why can't we just say characterize something like char characterize your accent professor c: Well , Boston 's @ @ , too . " postdoc b: and and so I would say , " I don't know " . Right , which probably means you have a very postdoc b: But someone from Boston with a really strong coloration would know . And so would an R - less Maine or something , phd d: And that 's actually good . phd d: I was I was thinking of something along that line professor c: How postdoc b: Good . phd d: because if you don't know , then , you know , ruling out the fact that you 're totally inept or something , postdoc b:  . phd d: if somebody doesn't know , it probably means their accent isn't very strong compared to the sort of midwest standard . professor c: Well , it wasn't that long ago that we had somebody here who was from Texas who was absolutely sure that he didn't have any accent left . I I would say more more sweepingly , " how would you characterize your accent ? " phd e: Yeah . grad a: So you want to change the instructions also not just say region ? phd d: W postdoc b: Well , I think this discussion has made me think that 's s something to consider . grad a: I don't know if I if I read this form , I think they 're going to ask it they 're going to answer the same way if you say , " What 's variety of English do you speak ? Region . " as if you say " what variety of region region do you speak ? Please characterize your accent ? " They 're going to answer the same way . grad a: Well what we talked about with that is is so that they would understand the granularity . postdoc b: Yes , but if , as Liz is suggesting , people who have strong accents know that they do grad a:  that 's what I had before , and you told me to list the regions to list them . phd d:  we we professor c: Yeah last week last week I was sort of r arguing for having it wide open , but then everybody said " Oh , no , but then it will be hard to interpret because some people will say Cincinnati and some will say Ohio " . phd d: What if we put in both ? grad a: That 's what the " Other " is for . phd d: And Would people No ,  what if we put in both ways of asking them ? So . One is Region and the another one is " if you had to characterize yourself your accent , what would you say ? " grad a: Won't they answer the same thing ? phd d: Well they might only answer only one of the questions but if postdoc b: Yeah that 's fine . postdoc b: They might say " Other " for Region because they don't know what category to use phd d: Actually postdoc b: but they might have something phd d: Right . phd d: It just And we we might learn from what they say , as to which one 's a better way to ask it . professor c: W This is just a small thing phd d: But I Cuz I really don't know .  professor c: but  It says " Variety " and then it gives things that e have American as one of the choices . But then it says " Region " , but Region actually just applies to  , US , grad a: Right . professor c: right ? grad a:  that 's why I put the " Other " in . We just We sort of thought , " yes , " y y  professor c: S postdoc b: At the last meeting , my recollection was that we felt people would have  less that that there are so many types and varieties of these other languages and we are not going to have that many subjects from these different language groups grad a: Yep . grad a: So  , I  the way I had it last time was Region was blank , postdoc b: That 's what I thought . grad a: And and I think that that 's the best way to do it , postdoc b: Yeah . grad a: because because of the problems we 're talking about but what we said last week , was no , put in a list , so I put in a list . grad a: Well , certainly dropping " Northern " I think is right , because none of us know what that is . phd d: Cuz ,  And keeping " Other " , and then maybe this North Midland , we call it " North Midwest " . Does that make sense ? phd e: South Midwest ? phd d: That would help me professor c: U unless you 're from Midland , Kansas . phd d: I don't know where Midland is professor c: There 's a Or Midland Midland grad a: Is " Midwest " one word ? professor c: Is it Midland Midland Midland , Texas or Midland , Kansas ? I forget . It 's just one big shebang , where , of course , you have huge variation in dialects , grad a: But that 's true of New England too . I was going to say the only one that doesn't have a huge variety is New York City . I I would think that these categories would be more w would be easier for an an analyst to put in rather than the subject himself . professor c: U grad a: I think that that was what happened with TIMIT , was that it was an analyst . Where does Where does d w Where Where 's where does  New New York west of west of  New York City and Pennsylvania  and  phd d: Yeah , I don't know how it came from . Pennsylvania is not grad a: " Other " , it goes under " Other " , definitely under " Other " . phd d: Well , you know , Pennsylvania has a pretty strong dialect and it 's totally different than professor c: Pennsylvania Yeah . and  New Jersey is not New England and Maryland is not New England and none of those are the South . Rather than have circle fill in forms , say " Region , open paren , E G Southern comma Western comma close paren colon . grad a: Is that a real accent ? postdoc b: Sure , yeah ! grad a: How do you spell it ? phd e: Yeah . Because that get 's at both of the things we were trying to do , professor c: OK . grad a: the granularity , and the person can just self - assess and we don't have to argue about what these regions are . postdoc b: So you have native language , you have region , and then you have time spent in English speaking country . Now , I wonder if it might be useful to have another open field saying " which one parenthesis S paren closed parenthesis " . postdoc b: It doesn't have to be ex all at all exact , just in the same open field format that you have .  s e Any any other  open mike topics or should we go right to the digits ? grad a: did you guys get my email on the multitrans ? That OK . postdoc b: So you this is n Dan 's patches , Dan Ellis 's patches . grad a: The what the ones I applied , that you can actually do are Dan 's , because it doesn't slow it down . phd d: M postdoc b: Fantastic ! grad a: Just uses a lot of memory . phd d: So when you say " slow " , does that mean to grad a: No , the the one that 's installed is fine . Which , instead of having the one pane with the one view , It has multiple panes with the views . grad a: But the problem with it is the drawing of those waveforms is so slow that every time you do anything it just crawls . phd d: It 's So , it it 's the redrawing of the w postdoc b: That 's a consideration . postdoc b: And this 'll be a hav having the multiwave will be a big help cuz in terms of like disentangling overlaps and things , that 'll be a big help . postdoc b: e so you e the patches that you grad a: No , he suggested that , but he didn't It 's not installed . phd d: So is there any hope for actually displaying the wave form ? grad a: not if we 're going to use Tcl - TK At least not if we 're going to use Snack . And it 's useful to have the grad a: Why don't we we see how Dan 's works and if it If we really need the display phd d: Yeah . And it 's just , you know , a a uni - dimensional feature , varying in time . phd d: Right ? grad a: We we could do that but that would mean changing the code . grad a: This is a program that we got from someone else , and we 've done patches on . professor c: If there was some Is there some way to have someone write patches in something faster and and link it in , or something ? phd d: That 's grad a: Not easily . grad a: Let 's try it with Dan 's and if that isn't enough , we can do it otherwise . grad a: I think it is , cuz when I was playing with it , the mixed signal has it all in there . And so it 's really It 's not too bad to find places in the in the stream where things are happening . postdoc b: And it 's also also the case that that  this multi - wave thing is proposed to the phd e:  ? postdoc b: So . postdoc b: They said that the only reason they hadn't had the multi the parallel  stream one before was simply that they hadn't had time to do it . grad a: And if if professor c: They may well have not had much demand for it . You mean they could they could do it and it would be fast enough if they do it ? phd e: Yeah . I just mean I just mean that it 's that that his phd d: Or ? phd e: Oh . This one that we now have does have the status of potentially being incorporated l likely being incorporated into the central code . postdoc b: Now , tha Now , if we develop further then , y  , I don't grad a: I think if if if one of us sat down and coded it , so that it could be displayed fast enough I 'm sure they would be quite willing to incorporate it . postdoc b:  it 's I think it 's a nice feature to have it set that way . postdoc b: I just like the idea of it being something that 's , you know , tied back into the original , so that other people can benefit from it . I also understand that you can have widgets that are very useful for their purpose and that you don't need to always go that w route . Let 's do digits , and then we 'll turn off the mikes , and then I have one other thing to discuss . phd d: so I can Well , I can wait for the digits but I can't stay for the discussion grad a: Well , you want to go first ? Or . postdoc b: Well , should we e should we switch off the g professor c: Well , we 'll talk to you about it  grad a: Do you wanna go do digits or do you wanna just skip digits ? phd d:  . phd d: But if there 's something on the rest of the I 'm I 'll be around just have to make call before quarter of . postdoc b: Ke grad a: Why don't you read the digits ? professor c: Yeah , why don't you read the digits and then you can go