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 .
C: um Because something is a good feature uh by itself , you don't really know how it 'll behave in combination
C: 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 .
C: There 's cool graphic things people have had sometimes to put together three or four in some funny funny way .
C: 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
E: Yeah .
D: Well , especially for normalizing .
D: I mean , it 's really important to pick a normalization that matches the distribution for that feature .
C: Mm - hmm .
C: Mm - hmm .
D: And it may not be the same for all the types of overlaps or the windows may not be the same .
D: 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 ,
D: but we don't really know which speaker is overlapping with which speaker ,
D: right ?
C: Right .
E: Yeah .
D: So I mean another way would just be to take the speech from just , say , Morgan , And just Jane and then just their overlaps ,
D: like but by hand ,
D: by cheating ,
D: and looking at you know , if you can detect something that way ,
D: because if we can't do it that way , there 's no good way that we 're going to be able to do it .
A: No prayer .
D: That You know , there might be something helpful and cleaner about looking at just individuals and then that combination alone .
E: Yeah .
D: Plus , I think it has more elegant e
C: Mm - hmm .
D: The m the right model will be easier to see that way .
D: 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
E: Yeah .
D: and just sort of look at those three cases of Adam and the other person and the overlaps ,
E: Yeah .
D: maybe D: and just look at the distributions , maybe there is a clear pattern
E: Yeah .
E: Uh - huh .
D: but we just can't see it because there 's too many combinations of of people that can overlap .
E: Yeah .
B: I had the same intuition last last last week .
D: So .
E: Yeah .
D: Just seems sort of complex .
B: I think it 's to start with it 's s your your idea of simplifying ,
B: starting with something that you can see eh you know without the extra layers of D: Right .
D: Cuz if energy doesn't matter there , like D: I don't think this is true , but what if
E: To study individual ?
D: Hmm ?
B: Sorry ,
B: what ?
E: To study individual ?
B: Well , you you you don't have to study everybody individually
D: Well , to study the simplest case to get rid of extra E: The the the But Consider B: but just simple case
B: and the one that has the lot of data associated with it .
D: Right .
D: Cuz what if it 's the case and I don't think this is true A: That was a great overlap by the way .
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 D: both people talk more softly ?
D: I don't think this happens at all .
B: Or or what if what if the equipment what if the equipment adjusts somehow ,
D: Or they get louder .
B: there 's some equalizing in there ?
D: Yeah or C: Uh , no we don't have that .
D: I mean .
C: But .
A: Well , but But I think that 's what I was saying about different types of overlap .
B: OK .
B: Saturation .
D: There are there are different types ,
D: and within those types ,
D: like as Jose was saying , that sounded like a backchannel overlap ,
D: meaning the kind that 's a friendly encouragement , like " Mm - hmm . " ,
D: " Great ! " ,
D: " Yeah ! "
E: Yeah .
D: And it doesn't take you don't take the floor .
D: Um , but , some of those , as you showed , I think can be discriminated by the duration of the overlap .
E: Yeah .
D: So .
D: It Actually the s new student , Don , who um Adam has met , and he was at one of our meetings D: He 's getting his feet wet and then he 'll be starting again in mid - January .
D: He 's interested in trying to distinguish the types of overlap .
D: I don't know if he 's talked with you yet .
E: Yeah .
D: But in sort of honing in on these different types
E: I don't consi Now I don't consider that possibility .
D: and So maybe E: This is a s a general studio of the overlapping we 're studying the i
C: Yeah .
D: So it might be something that we can help by categorizing some of them and then , you know , look at that .
C: Well I I I I would s actually still recommend that he do the overall thing
C: because it would be the quickest thing for him to do .
C: He could You see , he already has all his stuff in place ,
D: Yeah .
C: he has the histogram mechanism ,
C: he has the stuff that subtracts out C: 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 .
C: And then he should go on and do the other stuff bec
D: Yeah .
C: but But this will D: Yeah , no . I didn't mean that that for you to do that ,
D: but I was thinking if if Don and I are trying to get categories
C: Mm - hmm .
D: and we label some data for you , and we say this is what we think is going C: Mm - hmm .
D: So you don't have to worry about it .
D: And here 's the three types of overlaps .
E: Yeah .
D: And we 'll we 'll do the labelling for you .
E: Yeah .
C: Hm - hmm .
D: Um .
E: Consider different class of overlap ?
D: Yeah , that we would be working on anyway .
E: If there 's time .
E: Yeah .
D: Then maybe you can try some different things for those three cases , and see if that helps , or E: Yeah .
E: This is the thing I I comment with you before , that uh we have a great variation of th situation of overlapping .
C: Mm - hmm .
E: And the behavior for energy is , uh log energy , is not uh the same all the time .
D: Mm - hmm .
E: And 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 C: and I would imagine there should be some you know , there should be The distributions should be somewhat separated .
E: Yeah .
E: Yeah .
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 .
E: Yeah .
E: Yeah .
E: Yeah .
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 ,
E: Yeah .
C: although sometimes , as you say , it would .
E: Yeah .
D: Yeah , no , that was That was a jok
C: So . So .
D: or a sort of , a case where where you would never know that unless you actually go and look at two individuals .
C: Yeah .
C: I mean .
C: No .
C: It could it probably does happen sometimes .
E: Yeah .
C: Yeah .
A: Right .
C: Yeah .
E: Yeah .
A: Mind if I turned that light off ?
D: So .
A: The flickering is annoying me .
C: OK .
D: It might the case , though , that the significant energy , just as Jose was saying , comes in the non - backchannel cases .
D: Because in back Most people when they 're talking don't change their own energy when they get a backchannel ,
D: cuz they 're not really predicting the backchannel .
C: Mm - hmm .
D: And sometimes it 's a nod and sometimes it 's an " mm - hmm " .
E: Yeah .
D: And the " mm - hmm " is really usually very low energy .
E: Yeah .
D: So maybe those don't actually have much difference in energy .
D: But all the other cases might .
D: and the backchannels are sort of easy to spot s in terms of their words or C: e e and and again what they what difference there was would kind of be lost in taking the log ,
D: I mean , just listen to it .
E: Yeah .
D: So .
C: so ,
D: Well , it would be lost no matter what you do .
E: But C: as well .
E: Yeah .
D: It just C: Mmm , no ,
C: if it 's if i if it 's A: Tone
C: Well , it won't be as big .
D: I mean , even if you take the log , you can your model just has a more sensitive measures .
E: Yeah .
A: Sure , but tone might be very
D: So .
A: Yeah , you 're " mm - hmm " tone is going to be very different .
D: Yeah .
D: Right . Right .
A: You could imagine doing specialized ones for different types of backchannels , if you could if you had a good model for it .
A: Your " mm - hmm " detector .
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 .
A: Right .
E: Yeah .
C: So it 's So , uh if you i i So i if there if there close to things it does
D: Yeah .
C: it 's a nonlinear operation that does in fact change the distinction .
C: If you 're doing a non if you 're doing some fancy thing then then
C: yeah .
C: And right now we 're essentially doing this linear thing by looking across here and and saying we 're going to cut it here .
C: Um and that that 's the indicator that we 're getting .
C: But anyway , yeah , we 're not disagreeing on any of this ,
C: 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 .
C: 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 .
E: Uh - huh .
E: Uh - huh .
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 .
C: And then you can move on to the uh uh more <nonvocalsound> pitch related stuff .
E: Mm - hmm .
E: I I I think this is a good idea .
C: OK .
E: Not consider the log energy .
B: Mm - hmm .
C: Yeah .
C: But then the C: Have you started looking at the pitch related stuff at all ,
C: or ?
E: The ?
C: Pitch related ?
C: Harmonicity and so on ?
E: I I 'm preparing the the program but I don't I don't begin because eh I saw your email
C: Preparing to C: Yeah .
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
C: Yeah .
C: Oh , that 's not what I meant .
C: No , no .
C: I I I I C: Well , we certainly should see this but I I I I think that the harm
C: I certainly wasn't saying this was better than the harmonicity and pitch related things
C: I was just saying
E: I I go on with the with the pitch ,
C: Yeah .
E: aha ! OK .
C: Yeah , I was just saying 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 .
C: OK .
C: OK . OK .
C: But I think , like , all these derivatives and second derivatives and all these other very fancy things ,
C: I think I would just sort of look at the energy and then get into the harmonicity as as a suggestion .
E: OK .
E: I go on with the pitch .
C: Uh OK .
C: So maybe uh since w we 're trying to uh compress the meeting ,
C: um , I know Adam had some form stuff he wanted to talk about
C: and did you have some ?
B: I wanted to ask just s something on the end of this top topic .
B: So , when I presented my results about the uh distribution of overlaps and the speakers and the profiles of the speakers , at the bottom of that I did have a proposal ,
E: Uh - huh .
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 D: Oh .
D: That 'd be great .
B: and you know I said that on my in my summary ,
D: Yeah , I remem
B: that you know so it 's like people may have different amounts of being overlapped with or overlapping
D: Right .
B: but that in itself is not informative without knowing what types of overlaps they 're involved in
B: so I was planning to do a taxonomy of types overlaps with reference to that .
D: That would be great .
E: Yeah .
D: That would be really great .
B: So , but it you know it 's like it sounds like you also have uh something in that direction .
A: Hmm .
B: Is is it D: We have nothing D: You know , basically , we got his environment set up .
D: He 's he 's a double - E <comment> you know .
D: So . It 's mostly that , if we had to label it ourselves , we we would or we 'd have to , to get started ,
D: but if  It it would be much better if you can do it .
D: You 'd be much better <comment> at doing it also
D: because you know , I I 'm not I don't have a good feel for how they should be sorted out ,
B: Interesting .
D: and I really didn't wanna go into that if I didn't have to .
D: So if If you 're w willing to do that or or B: Well maybe we can
A: It would be interesting , though , to talk , maybe not at the meeting , but at some other time about what are the classes .
B: OK .
E: Yeah .
B: Mm - hmm .
D: Yeah .
E: Yeah .
B: Mm - hmm .
D: I think that 's a research effort in and of itself ,
A: Yeah , it would be interesting .
D: because you can read the literature , but I don't know how it 'll turn out
E: Yeah .
D: and , You know , it 's always an interesting question .
E: I would think it 's interesting , yeah .
B: It seems like we also s with reference to a purpose , too , that we we 'd want to have them coded .
E: Yeah .
D: That 'd be great .
E: Yeah .
A: Yep .
D: That 'd be really great .
B: OK .
D: And we 'd still have some funding for this project ,
B: I can do that .
E: uh uh
D: like probably , if we had to hire some like an undergrad , because uh Don is being covered half time on something else B: Mm - hmm .
D: I mean , he we 're not paying him the full RA - ship for all the time .
D: So . um If we got it to where we wanted we needed someone to do that D: I don't think there 's really enough data where where B: Mm - hmm .
B: Yeah , I see this as a prototype , to use the only the the already transcribed meeting as just a prototype .
E: Yeah .
D: Yeah .
E: I I think a a another parameter we c we we can consider is eh the duration .
D: But A: Mm - hmm .
E: Another e e m besides eh the the class of overlap , the duration .
E: Because is possible eh some s s um eh some classes eh has eh a type of a duration ,
E: eh , a duration very short uh when we have  we have overlapping with speech .
B: Mm - hmm .
D: Yeah , definitely .
B: Yeah , maybe It may be correlated .
E: Is possible to have .
B: Mm - hmm .
E: And it 's interesting , I think , to consider the the window of normalization ,
E: normalization window .
E: 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
E: 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
E: eh the if the fit of normalization is eh mmm bigger eh in that overlapping zone eh very short
B: Mm - hmm .
D: Yeah , that 's true .
D: The window shouldn't be larger than the backchannel .
E: I I me I I understand .
E: I mean that you have eh you have a backchannel , eh , eh you have a overlapping zone very short
D: Yeah .
E: and you consider eh n eh all the channel to normalize this very short eh C: Mm - hmm .
C: Mm - hmm .
E: for example " mmm mm - hmm hmm " eh
E: And the energy is not eh height
E: 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 ,
C: Mm - hmm .
E: eh the effect is mmm stronger
E: eh that I I mean 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 .
C: Mm - hmm .
E: Not C: You you want it around the overlapping part .
C: You want it to include something that 's not in overlapping
E: Yeah .
E: Yeah .
C: but but uh
E: Yeah .
B: Mm - hmm .
E: I I don't know .
E: Is s If C: Yeah .
D: Well it 's a sliding window ,
D: right ?
D: So if you take the the measure in the center of the overlapped piece , you know , there 'd better be some something .
C: Mm - hmm .
E: Yeah .
E: Yeah .
D: But if your window is really huge then yeah you 're right
D: you won't even E: Yeah , This is the This is the the idea , to consider only the the small window near near near the the overlapping zone .
D: The portion of the <comment> of the backchannel won't won't effect anything .
D: But you D: Yeah .
D: So .
D: You know , you shouldn't be more than
D: like  You should definitely not be three times as big as your as your backchannel .
E: Yeah .
D: Then you 're gonna w have a wash .
B: Mm - hmm .
D: And hopefully it 's more like on the order of C: I 'm not sure that 's necessarily true .
E: Yeah ?
B: It is an empirical question , it seems like .
D: Yea
C: Because because it because um again if you 're just compensating for the gain ,
E: Yeah .
B: Yeah .
C: you know , the fact that this this gain thing was crude ,
C: 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 .
C: And then you still s And then if you look at the frame with respect to that , it still should should uh change
D: Yeah , it depends how different your normalization is , as you slide your window across .
D: I mean . That 's something we don't know .
C: Mm - hmm .
B: It 's possible to try it both ways ,
A: Well , I mean we 're also talking about a couple of different things .
B: isn't it ?
B: in this small
A: I mean , one is your analysis window and then the other is any sort of normalization that you 're doing .
D: Yeah I was talking about the n normalization window .
A: And the And they could be quite different .
D: Yeah .
C: Right .
E: Yeah .
C: This was sort of where where we were last week .
D: Yeah .
D: That 's true . Yeah .
A: Yep .
C: But , anyway We we 'll have to look at some core things .
D: Um .
E: OK .
B: OK .
D: But that 'd be great if if you 're marking those
D: and um .
B: Great .
B: OK .
E: Yeah .
D: But it is definitely true that we need to have the time marks ,
B: Mm - hmm .
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