Professor C: That 's that sort of hypothesis .
PhD E: That 's right .
Professor C: And the first way you 'd look at that , uh 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: You know , but just but just  just uh do it PhD E: Yeah .
Professor C: Well in this case you have three . You have the silence , and that that 's fine .
PhD E: Yeah .
Professor C: So , uh with three colors or three shades or whatever , just just look at those distributions .
PhD E: Yeah .
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 , um and uh 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 . Let 's start off just in one , uh , with this feature .
PhD E: Yeah . Yeah .
Professor C: I think that 's probably the most basic thing , before anything very complicated .
PhD E: Yeah .
Professor C: Um 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 .
PhD D: Yeah .
PhD E: Yeah . I agree , yeah . Uh - huh .
Professor C: Um But since  uh 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 .
PhD E: OK . Yeah . Yeah . Yeah , yeah , yeah .
Professor C: And she 's right , that a that having a having {comment} 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 .
PhD E: But Uh - huh . Yeah . But It it 's curious but uh 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: Mm - hmm . Mm - hmm .
PhD E: eh in the middle eh a zone of overlapping with mmm less energy
Professor C: Mm - hmm .
PhD E: and eh come with another speaker with high energy
Professor C: Mm - hmm .
PhD E: and the overlapping zone has eh less energy .
Professor C: Yeah . 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 I mean 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 . "
PhD E: Yeah . Yeah .
PhD D: Especially locally .
Professor C: Mm - hmm .
PhD D: So . Locally .
PhD E: just locally , yeah .
Grad A: And I was just going to say that that right now we 're just exploring .
PhD D: And the other thing is I Sorry . I Grad A: What you would imagine eventually , is that you 'll feed all of these features into some discriminative system .
PhD E: Yeah .
Professor C: Yeah .
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 .
PhD E: Yeah . Yeah . Yeah .
Professor C: Right . I mean 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: um Because something is a good feature uh 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 uh 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 uh some hope
PhD E: Yeah .
PhD D: Well , especially for normalizing .
Professor C: Mm - hmm .
PhD D: I mean , it 's really important to pick a normalization that matches the distribution for that feature .
Professor C: Mm - hmm .
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 I mean 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 .
Grad A: No prayer .
PhD D: That You know , there might be something helpful and cleaner about looking at just individuals and then that combination alone .
PhD E: Yeah .
PhD D: Plus , I think it has more elegant e
Professor C: Mm - hmm .
PhD D: The m the right model will be easier to see that way . 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 .
PhD E: Uh - huh . Yeah .
Postdoc B: I had the same intuition last last last week .
PhD D: So . Just seems sort of complex .
PhD E: Yeah .
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: Hmm ?
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 .
PhD D: Right . 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: Uh , no we don't have that .
PhD D: I mean .
Grad A: Well , but But I think that 's what I was saying about different types of overlap .
Postdoc B: OK .
Professor C: But .
Postdoc B: Saturation .
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 " Mm - hmm . " , " Great ! " , " Yeah ! "
PhD E: Yeah .
PhD D: And it doesn't take you don't take the floor . Um , but , some of those , as you showed , I think can be discriminated by the duration of the overlap .
PhD E: Yeah .
PhD D: So . It Actually the s new student , Don , who um 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 . He 's interested in trying to distinguish the types of overlap . I don't know if he 's talked with you yet . But in sort of honing in on these different types
PhD E: Yeah . I don't consi Now I don't consider that possibility .
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: because it would be the quickest thing for him to do . He could You see , he already has all his stuff in place ,
PhD D: Yeah .
Professor C: he has the histogram mechanism , he has the stuff that subtracts out and all he has to do is change it uh uh 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 . Yeah , no . 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: Mm - hmm . Mm - hmm .
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 . And here 's the three types of overlaps .
PhD E: Yeah .
PhD D: And we 'll we 'll do the labelling for you .
PhD E: Yeah .
Professor C: Hm - hmm .
PhD D: Um .
PhD E: Consider different class of overlap ?
PhD D: Yeah , that we would be working on anyway .
PhD E: If there 's time .
PhD D: Then maybe you can try some different things for those three cases , and see if that helps , or PhD E: Yeah . Yeah . This is the thing I I comment with you before , that uh we have a great variation of th situation of overlapping .
Professor C: Mm - hmm .
PhD E: And the behavior for energy is , uh log energy , is not uh the same all the time .
PhD D: Mm - hmm .
PhD E: And Professor C: But I guess I was just saying that that right now uh 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 .
PhD E: Yeah . Yeah .
Professor C: Uh 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 .
PhD E: Yeah . Yeah .
Professor C: Um , but um For instance , I mean 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 .
Professor C: although sometimes , as you say , it would .
PhD E: Yeah .
Professor C: So . So .
PhD D: Yeah , no , that was That was a jok
PhD E: Yeah .
Professor C: 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 .
Professor C: I mean . No . It could it probably does happen sometimes .
PhD E: Yeah .
Grad A: Right .
Professor C: Yeah . Yeah .
Grad A: Mind if I turned that light off ?
PhD E: Yeah .
PhD D: So .
Grad A: The flickering is annoying me .
Professor C: OK .
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: Mm - hmm .
PhD D: And sometimes it 's a nod and sometimes it 's an " mm - hmm " .
PhD E: Yeah .
PhD D: And the " mm - hmm " is really usually very low energy .
PhD E: Yeah .
PhD D: So maybe those don't actually have much difference in energy . But all the other cases might .
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 I mean , just listen to it .
PhD E: Yeah .
PhD D: So .
Professor C: so , as well .
PhD D: Well , it would be lost no matter what you do .
PhD E: But Yeah .
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: I mean , even if you take the log , you can your model just has a more sensitive measures .
PhD E: Yeah .
Grad A: Sure , but tone might be very
PhD D: So .
Grad A: Yeah , you 're " mm - hmm " tone is going to be very different .
PhD D: Yeah . Right . Right .
Grad A: You could imagine doing specialized ones for different types of backchannels , if you could if you had a good model for it . Your " mm - hmm " detector .
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 .
Grad A: Right .
Professor C: So it 's So , uh if you i i So i if there if there close to things it does
PhD E: Yeah .
PhD D: Yeah .
Professor C: it 's a nonlinear operation that does in fact change the distinction . If you 're doing a non if you 're doing some fancy thing then then 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 . Um and that that 's the indicator that we 're getting . But anyway , yeah , we 're not disagreeing on any of this , we should look at it more uh more finely , but uh uh 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 uh , 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 uh uh smaller um , more coherent groups to look at , that would be another interesting thing later .
PhD E: Uh - huh .
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 .
PhD E: Uh - huh .
Professor C: And then you can move on to the uh uh more {nonvocalsound} pitch related stuff .
PhD E: Mm - hmm . I I I think this is a good idea .
Professor C: OK .
Postdoc B: Mm - hmm .
PhD E: Not consider the log energy .
Professor C: Yeah . 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 . Oh , that 's not what I meant . No , no . 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 .
PhD E: aha ! OK .
Professor C: Yeah , I was just saying PhD E: I I I I understood uh that eh I I had to finish by the moment with the and and concentrate my my energy in that problem .
Professor C: OK . OK . OK . 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 .
PhD E: OK . I go on with the pitch .
Professor C: Uh OK . So maybe uh since w we 're trying to uh compress the meeting , um , I know Adam had some form stuff he wanted to talk about and did you have some ?