G: Yeah .
D: and wh what D: nnn , how far is G: Maybe .
G: Yeah .
D: And , I I have prepared the the pitch tracker now .
A: Mm - hmm .
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 .
G: I see .
G: Ha - h have you ever looked at the , uh , uh Javier 's , uh , speech segmenter ?
D: And , nnn , nnn C: No .
G: Oh . Maybe m you could , you kn uh show Thilo that .
D: No .
C: No .
D: Yeah .
C: Yeah .
D: Yeah .
C: Sure .
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 .
D: Yeah .
G: But if you included , uh , broadened classes then in principle maybe you can cover the overlap cases .
C: OK .
D: Yeah .
D: Mmm , yeah .
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 ,
G: Uh F: Mm - hmm .
C: the to speech - nonspeech as A: I think with D: Ah .
C: it 's only speech or it 's it 's it 's nonspeech .
A: That 's right . But I think Javier 's D: Yeah .
F: Mm - hmm .
G: N n
A: I think Javier 's might be able to .
C: So .
A: It doesn't have the same Gaus - uh , H M M modeling ,
C: Yeah .
A: which is I think a drawback .
C: OK .
A: But , uh G: Well , it 's sort of has a simple one .
D: Mmm ,
D: yeah .
G: Right ?
G: It 's A: Does it ?
G: it 's just it 's just a isn't it just a Gaussian
G: for each ?
D: Yeah .
A: Yeah .
D: Hmm .
F: Mm - hmm .
G: Yeah .
A: And then he ch you choose optimal splitting .
G: Oh , it doesn't have it doesn't have any temporal , uh ?
G: I thought it A: Maybe I 'm misremembering , but I did not think it had a Markov G: Yeah .
G: I gues I guess I don't remember either .
G: Uh . It 's been a while .
D: Javier C: Yeah . Uh , I could have a look at it .
G: Uh .
D: You mean Ja - eh , eh , Javier program ?
C: So .
A: Mm - hmm .
D: No , Javier di doesn't worked with , uh , a Markov A: Yeah , I didn't think so .
D: He on only train G: Oh , OK . So he 's just he just computes a Gaussian over potential A: Yep .
D: Yeah . It was only Gaussian .
G: Oh , I see . I see .
G: And and D: This is the idea .
A: And so I I think it would work fine for detecting overlap .
A: It 's just , uh , that i it A: he has the two - pass issue that A: 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 .
A: And that 's just a data reduction step ,
A: so that you 're not trying at every time interval .
C: OK .
A: And so those are the putative places where he tries .
D: Yeah .
C: Yeah .
C: OK .
A: And right now he 's doing that with silence
A: and that doesn't work
A: with the Meeting Recorder .
C: Yeah .
A: So if we used another method to get the first pass , I think it would probably work .
C: Yeah . Sure .
C: Yeah .
C: Yeah , OK .
A: It 's a good method .
A: As long as the len as long the segments are long enough .
D: Yeah .
A: That 's the other problem .
G: O - k OK . So let me go back to what you had , though .
C: So C: Yeah .
G: Um .
G: The other thing one could do is Couldn't D: Mm - hmm .
G: I mean , it 's So you have two categories
G: and you have Markov models for each .
C: Yeah .
G: Couldn't you have a third category ?
G: So you have , uh you have , uh , nonspeech , single - person speech , and multiple - person speech ?
F: He has this on his board actually .
F: Don't you have , like those those several different categories on the board ?
G: Right ?
G: And then you have a Markov model for each ?
C: Um C: I 'm not sure .
C: I I thought about , uh , adding , uh , uh , another class too .
C: 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 .
C: But it it it could be possible , I think ,
G: I see .
G: I see .
C: in principle .
G: Yeah , I mean , I G: This is all pretty gross .
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 ,
D: Yeah .
C: Yeah .
G: we should at least look at the space and understand D: Yeah .
C: Yeah .
G: It seems like if two people two or more people talk at once , it should get louder ,
C: Yeah .
G: uh , and , uh , uh , there should be some discontinuity in pitch contours ,
C: I had the impression .
D: Yeah .
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 .
C: Yeah .
C: Yeah .
A: Right .
G: So those are all things that should be there .
G: So far , um , uh , Jose has has been D: Mm - hmm .
C: Yeah .
G: By the way , I was told I should be calling you Pepe ,
G: but G: by your friends ,
D: Yeah .
G: but
G: Anyway ,
G: um ,
D: Yeah .
G: uh , the has has , uh , been exploring , uh , e largely the energy issue
G: and ,
G: um ,
G: as with a lot of things , it is not uh , like this , it 's not as simple as it sounds .
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 ?
C: Yeah .
G: Uh , what is it no
G: Obviously , just a simple number  absolute number isn't gonna work .
G: So it should be with compared to what ?
G: Should there be a long window for the normalizing factor and a short window for what you 're looking at ?
C: Yeah .
G: Or , you know , how b short should they be ?
G: So ,
G: th he 's been playing around with a lot of these different things
D: Hmm .
G: and and so far at least has not come up with any combination that really gave you an indicator .
D: Yeah .
G: So
G: I I still have a hunch that there 's it 's in there some place ,
G: 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 .
D: Yeah .
D: To overrule , yeah .
G: So pitch - related and harmonic - related , I 'm I 'm somewhat more hopeful for it .
D: Yeah .
D: Yeah .
G: But it seems like if we just wanna get something to work ,
C: Yeah .
D: Yeah .
G: that , uh , their suggestion of of G: Th - they were suggesting going to Markov models ,
G: uh , but in addition there 's an expansion of what Javier did .
G: And one of those things , looking at the statistical component ,
D: One .
C: Yeah .
G: even if the features that you give it are maybe not ideal for it , it 's just sort of this general filter bank
G: or  or cepstrum or something , um C: Yeah .
G: Eee it 's in there somewhere probably .
D: But , eh , what did you think about the possibility of using the Javier software ?
D: Eh , I mean , the , uh the , uh the BIC criterion ,
D: the the t to train the the Gaussian ,
D: eh , using the the mark , eh , by hand , eh , eh , to distinguish be mmm , to train overlapping zone and speech zone . I mean ,
D: eh , I I I think that an interesting , eh , experiment , eh , could be , th eh , to prove that , mmm , if s
D: we suppose that , eh , the the first step  I mean , the the classifier
D: what
D: were the classifier from Javier or classifier from Thilo ?
D: W What happen with the second step ?
D: I I mean , what what happen with the , eh the , uh , clu the , uh the clu the clustering process ?
A: Mm - hmm .
D: Using the the Gaussian .
A: You mean Javier 's ?
D: Yeah .
A: What do you mean ?
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 ?