Postdoc F: or whatever . So , um .
Professor G: Mm - hmm .
PhD D: Mm - hmm .
Grad A: I think it would also be interesting to have , uh , a couple of the meetings have more than one transcriber do ,
Professor G: Yeah .
Grad A: cuz I 'm curious about inter - annotator agreement .
Postdoc F: Mm - hmm .
PhD D: Yeah .
Postdoc F: OK . Yeah . Th - that 'd be I think that 's a a good idea .
Professor G: Yeah .
Postdoc F: You know , there 's also , the e In my mind , I think A An - Andreas was leading to this topic , the idea that , um , we haven't yet seen the the type of transcript that we get from IBM , and it may just be , you know , pristine . But on the other hand , given the lesser interface Cuz this is , you know we 've got a good interface , we 've got great headphones , m um Professor G: It could be that they will uh theirs will end up being a kind of fir first pass or something .
Postdoc F: Something like that .
Professor G: Maybe an elaborate one , cuz again they probably are gonna do these alignments , which will also clear things up .
Postdoc F: That 's that 's true . Al - although you have to s Don't you have to start with a close enough approximation {nonvocalsound} of the of the verbal part {nonvocalsound} to be able to ?
Professor G: Well , tha that 's that 's debatable .
Postdoc F: OK .
Professor G: Right ? I mean , so the so the argument is that if your statistical system is good it will in fact , uh , clean things up .
Postdoc F: OK .
Professor G: Right ? So it it 's got its own objective criterion .
PhD D: Yeah .
Professor G: And , uh , so in principle you could start up with something that was kind of rough I mean , to give an example of , um , something we used to do , uh , at one point , uh , back back when Chuck was here in early times , is we would take , um , da take a word and , uh , have a canonical pronunciation and , uh , if there was five phones in a word , you 'd break up the word , uh , into five equal - length pieces which is completely gross .
Grad A: Wrong .
PhD D: Yeah .
Professor G: Right ? I mean , th the timing is off all over the place in just about any word .
Postdoc F: Mm - hmm . OK .
Professor G: But it 's O K . You start off with that and the statistical system then aligns things , and eventually you get something that doesn't really look too bad .
Postdoc F: Oh , excellent . OK .
Professor G: So so I think using a a good aligner , um , actually can can help a lot . Um . But , uh , you know , they both help each other . If you have a if you have a better starting point , then it helps the aligner . If you have a good alignment , it helps the , uh , th the human in in taking less time to correct things .
Postdoc F: OK .
Professor G: So so Postdoc F: Excellent . I guess there 's another aspect , too , and I don't know uh , this this is very possibly a different , uh , topic . But , {nonvocalsound} uh , just let me say with reference to this idea of , um , higher - order organization within meetings . So like in a you know , the topics that are covered during a meeting with reference to the other , uh , uses of the data ,
Professor G: Mm - hmm .
Postdoc F: so being able to find where so - and - so talked about such - and - such , then , um , um e I mean , I I I did sort of a  a rough pass {nonvocalsound} on encoding , like , episode - like level things on the , uh , transcribed meeting Professor G: Mm - hmm .
Postdoc F: already transcribed meeting . And I don't know if , um Professor G: Mm - hmm .
Postdoc F: where {nonvocalsound} that i if that 's something that we wanna do with each meeting , sort of like a , um it 's like a manifest , when you get a box full of stuff , or or if that 's , um Professor G: Mm - hmm .
Postdoc F: I mean , i I I don't know what uh , level of detail would be most useful . I don't know i if that 's something that I should do when I look over it , or if we want someone else to do , or whatever .
Professor G: Mm - hmm .
Postdoc F: But this issue of the contents of the meeting in an outline form . OK .
Professor G: Yeah . Meaning really isn't my thing . Um Grad A: I think it just whoever is interested can do that . I mean , so if someone wants to use that data Postdoc F: OK .
Professor G: We 're running a little short here .
Postdoc F: That 's fine .
Professor G: We , uh , uh , cou trying to Postdoc F: I 'm finished .
Professor G: eh , was p Well , you know , the thing I 'm concerned about is we wanted to do these digits
Postdoc F: Oh , yeah .
Professor G: and and I haven't heard , uh , from Jose yet .
Postdoc F: Oh , yes .
PhD D: OK . What do you want ?
Postdoc F: Mm - hmm .
Professor G: So Grad A: We could skip the digits .
Professor G: Uh Grad A: We don't have to read digits each time .
Professor G: Uh I I I think it you know , another another bunch of digits . More data is good .
Grad A: OK .
PhD D: Yeah . Sure .
Professor G: So so I 'd like to do that . But I think , do you , maybe , eh ? Did you prepare some whole thing you wanted us just to see ?
PhD D: Yeah . It 's it 's prepared .
Professor G: Or what was that ? Yeah .
Postdoc F: Oh , k Sorry .
Professor G: Uh , how long a ?
PhD D: I I think it 's it 's fast , because , uh , I have the results , eh , of the study of different energy without the law length . Eh , um , eh , in the in the measurement , uh , the average , uh , dividing by the by the , um , variance . Um , I th i
Professor G: Yeah .
PhD D: the other , uh the the last w uh , meeting eh , I don't know if you remain we have problem to with the  with with the parameter with the representations of parameter , because the the valleys and the peaks in the signal , eh , look like , eh , it doesn't follow to the to the energy in the signal .
Professor G: Yes . Right .
PhD D: And it was a problem , uh , with the scale .
Grad A: With what ?
PhD D: Eh , the scale .
Postdoc F: Scale .
Grad A: Scale .
PhD D: Eh , and I I change the scale and we can see the the variance .
Professor G: OK . But the bottom line is it 's still not , uh , separating out very well .
PhD D: Yeah . Yeah .
Professor G: Right ?
PhD D: The distribution the distribution is is similar .
Professor G: OK . So that 's that 's that 's enough then . OK .
PhD D: Yeah .
Professor G: No , I mean , that there 's no point in going through all of that if that 's the bottom line , really .
PhD D: Yeah .
Postdoc F: Mm - hmm .
PhD D: Yeah .
Professor G: So , I I think we have to start Uh , I mean , there there 's two suggestions , really , which is , uh what we said before is that ,
PhD D: Mmm , yeah .
Professor G: um , it looks like , at least that you haven't found an obvious way to normalize so that the energy is anything like a reliable , uh , indicator of the overlap .
PhD D: Yeah . Yeah .
Professor G: Um , I I 'm I 'm still a little f think that 's a little funny . These things l @ @ seems like there should be ,
PhD D: Yeah .
Professor G: but  but you don't want to keep , uh keep knocking at it if it 's if you 're not getting any any result with that . But , I mean , the other things that we talked about is , uh , pitch - related things and harmonicity - related things ,
PhD D: Yeah .
Professor G: so which we thought also should be some kind of a reasonable indicator . Um But , uh , a completely different tack on it wou is the one that was suggested , uh , by your colleagues in Spain ,
PhD D: Yeah .
Professor G: which is to say , don't worry so much about the , uh , features .
PhD D: Yeah .
Professor G: That is to say , use , you know , as as you 're doing with the speech , uh , nonspeech , use some very general features .
PhD C: Yeah .
PhD D: Yeah .
Professor G: And , uh , then , uh , look at it more from the aspect of modeling .
PhD D: Yeah .
Professor G: You know , have a have a couple Markov models and and , uh , try to indi try to determine , you know , w when is th when are you in an overlap , when are you not in an overlap .
PhD D: Hmm .
Professor G: And let the , uh , uh , statistical system determine what 's the right way to look at the data .
PhD D: Yeah .
Professor G: I I , um , I think it would be interesting to find individual features and put them together . I think that you 'd end up with a better system overall .
PhD D: Yeah .
Professor G: But given the limitation in time and given the fact that Javier 's system already exists doing this sort of thing ,
PhD D: Yeah .
Professor G: uh , but , uh , its main limitation is that , again , it 's only looking at silences which would PhD D: Yeah . Yeah .
Professor G: maybe that 's a better place to go .
PhD D: Yeah .
Postdoc F: Mm - hmm .
Professor G: So .
PhD D: I I I think that , eh , the possibility , eh , can be that , eh , Thilo , eh , working , eh , with a new class , not only , eh , nonspeech and speech , but , eh , in in in the speech class ,
Professor G: Mm - hmm . Mm - hmm .
PhD D: dividing , eh , speech , eh , of from a speaker and overlapping , to try to to do , eh , eh , a fast a fast , eh , experiment to to prove that , nnn , this fea eh , general feature , eh , can solve the the the problem ,
Professor G: Yeah .
PhD D: and wh what nnn , how far is Professor G: Maybe . Yeah .
PhD D: And , I I have prepared the the pitch tracker now .
Grad A: Mm - hmm .
PhD D: And I hope the the next week I will have , eh , some results and we we will show we will see , eh , the the parameter the pitch , eh , tracking in with the program .
Professor G: I see .
PhD D: And , nnn , nnn Professor G: Ha - h have you ever looked at the , uh , uh Javier 's , uh , speech segmenter ?
PhD C: No . No .
PhD D: No .
Professor G: Oh . Maybe m you could , you kn uh show Thilo that .
PhD D: Yeah .
PhD C: Yeah . Sure .
PhD D: Yeah .
Professor G: Cuz again the idea is there the limitation there again was that he was he was only using it to look at silence as a as a as a as a p putative split point between speakers .
PhD D: Yeah .
Professor G: But if you included , uh , broadened classes then in principle maybe you can cover the overlap cases .
PhD C: OK .
PhD D: Yeah .
PhD C: Yeah , but I 'm not too sure if if we can really represent overlap with with the s detector I I I used up to now ,
PhD D: Mmm , yeah .
Professor G: Uh Postdoc F: Mm - hmm .
Grad A: I think with PhD C: the to speech - nonspeech as Grad A: That 's right . But I think Javier 's PhD C: it 's only speech or it 's it 's it 's nonspeech .
PhD D: Ah . Yeah .
Postdoc F: Mm - hmm .
Grad A: I think Javier 's might be able to .
PhD C: So .
Professor G: N n
Grad A: It doesn't have the same Gaus - uh , H M M modeling ,
PhD C: Yeah .
Grad A: which is I think a drawback .
PhD C: OK .
Grad A: But , uh Professor G: Well , it 's sort of has a simple one .
PhD D: Mmm , yeah .
Grad A: Does it ?
Professor G: Right ? It 's it 's just it 's just a isn't it just a Gaussian
PhD D: Yeah .
Professor G: for each ?
Grad A: Yeah . And then he ch you choose optimal splitting .
PhD D: Hmm .
Postdoc F: Mm - hmm .
Professor G: Yeah . Oh , it doesn't have it doesn't have any temporal , uh ?
Grad A: Maybe I 'm misremembering , but I did not think it had a Markov Professor G: I thought it Yeah . I gues I guess I don't remember either . Uh . It 's been a while .
PhD C: Yeah . Uh , I could have a look at it .
PhD D: Javier Professor G: Uh .
PhD C: So .
PhD D: You mean Ja - eh , eh , Javier program ?
Grad A: Mm - hmm .
PhD D: No , Javier di doesn't worked with , uh , a Markov Grad A: Yeah , I didn't think so .
PhD D: He on only train Professor G: Oh , OK . So he 's just he just computes a Gaussian over potential Grad A: Yep .
PhD D: Yeah . It was only Gaussian .
Professor G: Oh , I see . I see .
Grad A: And so I I think it would work fine for detecting overlap .
PhD D: This is the idea .
Professor G: And and Grad A: It 's just , uh , that i it he has the two - pass issue that What he does is , as a first pass he he p he does , um , a guess at where the divisions might be and he overestimates . And that 's just a data reduction step , so that you 're not trying at every time interval .
PhD C: OK .
Grad A: And so those are the putative places where he tries .
PhD D: Yeah .
PhD C: Yeah . OK .
Grad A: And right now he 's doing that with silence and that doesn't work with the Meeting Recorder . So if we used another method to get the first pass , I think it would probably work .
PhD C: Yeah . Yeah . Sure . Yeah . Yeah , OK .
Grad A: It 's a good method . As long as the len as long the segments are long enough .
PhD D: Yeah .
Grad A: That 's the other problem .
PhD C: So Professor G: O - k OK . So let me go back to what you had , though .
PhD C: Yeah .
Professor G: Um .
PhD D: Mm - hmm .
Professor G: The other thing one could do is Couldn't I mean , it 's So you have two categories
PhD C: Yeah .
Professor G: and you have Markov models for each . Couldn't you have a third category ? So you have , uh you have , uh , nonspeech , single - person speech , and multiple - person speech ?
Postdoc F: He has this on his board actually . Don't you have , like those those several different categories on the board ?
Professor G: Right ? And then you have a Markov model for each ?
PhD C: Um I 'm not sure . I I thought about , uh , adding , uh , uh , another class too . But it 's not too easy , I think , the the transition between the different class , to model them in in the system I have now . But it it it could be possible , I think ,
Professor G: I see . I see .
PhD C: in principle .
Professor G: Yeah , I mean , I This is all pretty gross .
PhD D: Yeah .
Professor G: I mean , the th the reason why , uh , I was suggesting originally that we look at features is because I thought , well , we 're doing something we haven't done before ,
PhD C: Yeah .
Professor G: we should at least look at the space and understand PhD C: Yeah .
PhD D: Yeah .
Professor G: It seems like if two people two or more people talk at once , it should get louder ,
PhD C: Yeah .
Professor G: uh , and , uh , uh , there should be some discontinuity in pitch contours ,
PhD C: I had the impression .
PhD D: Yeah .
PhD C: Yeah .
Professor G: and , uh , there should overall be a , um , smaller proportion of the total energy that is explained by any particular harmonic sequence in the spectrum .
Grad A: Right .
PhD C: Yeah .
Professor G: So those are all things that should be there .
PhD C: Yeah .
PhD D: Mm - hmm .
Professor G: So far , um , uh , Jose has has been By the way , I was told I should be calling you Pepe , but PhD D: Yeah .
Professor G: by your friends , but Anyway ,
PhD D: Yeah .
Professor G: um , uh , the has has , uh , been exploring , uh , e largely the energy issue and , um , as with a lot of things , it is not uh , like this , it 's not as simple as it sounds .
PhD C: Yeah .
Professor G: And then there 's , you know Is it energy ? Is it log energy ? Is it LPC residual energy ? Is it is it  is it , uh , delta of those things ? Uh , what is it no Obviously , just a simple number  absolute number isn't gonna work . So it should be with compared to what ? Should there be a long window for the normalizing factor and a short window for what you 're looking at ?
PhD C: Yeah .
Professor G: Or , you know , how b short should they be ? So ,
PhD D: Hmm .
Professor G: th he 's been playing around with a lot of these different things and and so far at least has not come up with any combination that really gave you an indicator .
PhD D: Yeah .
Professor G: So I I still have a hunch that there 's it 's in there some place , but it may be given that you have a limited time here , it it just may not be the best thing to  to to focus on for the remaining of it .
PhD D: Yeah . To overrule , yeah .
Professor G: So pitch - related and harmonic - related , I 'm I 'm somewhat more hopeful for it .
PhD D: Yeah . Yeah .
Professor G: But it seems like if we just wanna get something to work ,
PhD C: Yeah .
PhD D: Yeah .
Professor G: that , uh , their suggestion of of Th - they were suggesting going to Markov models , uh , but in addition there 's an expansion of what Javier did . And one of those things , looking at the statistical component ,
PhD D: One .
PhD C: Yeah .
Professor G: even if the features that you give it are maybe not ideal for it , it 's just sort of this general filter bank
PhD C: Yeah .
Professor G: or  or cepstrum or something , um Eee it 's in there somewhere probably .
PhD D: But , eh , what did you think about the possibility of using the Javier software ? Eh , I mean , the , uh the , uh the BIC criterion , the the t to train the the Gaussian , eh , using the the mark , eh , by hand , eh , eh , to distinguish be mmm , to train overlapping zone and speech zone . I mean , eh , I I I think that an interesting , eh , experiment , eh , could be , th eh , to prove that , mmm , if s we suppose that , eh , the the first step  I mean , the the classifier what were the classifier from Javier or classifier from Thilo ? W What happen with the second step ? I I mean , what what happen with the , eh the , uh , clu the , uh the clu the clustering process ?
Grad A: Mm - hmm .
PhD D: Using the the Gaussian .
Grad A: You mean Javier 's ?
PhD D: Yeah .
Grad A: What do you mean ?
PhD D: I I mean , that is is enough is enough , eh , to work well , eh , to , eh , separate or to distinguish , eh , between overlapping zone and , eh , speaker zone ? Because th if if we if we , eh , nnn , develop an classifier and the second step doesn't work well , eh , we have another problem .
Grad A: I Yeah . I had tried doing it by hand at one point with a very short sample ,
PhD D: N
Grad A: and it worked pretty well , but I haven't worked with it a lot . So what I d I d I took a hand - segmented sample
PhD D: Nnn , yeah .
Grad A: and I added ten times the amount of numbers at random ,
PhD D: Yeah .
Grad A: and it did pick out pretty good boundaries .
PhD D: Oh . Yeah . But is is if Grad A: But this was just very anecdotal sort of thing .
PhD D: But it 's possible with my segmentation by hand that we have information about the the overlapping ,
Grad A: Right . So if we if we fed the hand - segmentation to Javier 's and it doesn't work , then we know something 's wrong .
PhD D: uh Yeah . The N n Yeah . No . The demonstration by hand . Segmentation by hand I I I think is the fast experiment .
Grad A: Yeah . I think that 's probably worthwhile doing .
PhD D: Uh , we can prove that the Professor G: Uh - huh .
Grad A: Whether it 'll work or not .
PhD D: this kind o emph emphasises parameter and Gaussian Grad A: Yeah .
Professor G: Yeah .
Grad A: Yep . Y do you know where his software is ? Have you used it at all ?
PhD D: I yeah have . I have .
Grad A: OK .
PhD D :
Grad A: So . I I have as well , so if you need need help let me know .
PhD D: OK .
Professor G: Let 's read some digits .
Grad A: OK . uuh
Postdoc F: Mm - hmm .
Grad A: And we are