Professor A: I 'm a little confused . I thought that that one of the reason we thought we were so much faster than than , uh , the the other transcription , uh , thing was that that we were using the mixed file .
Postdoc F: Oh , yes . OK . But , um , with the mixed , when you have an overlap , you only have a a choice of one start and end time for that entire overlap , which means that you 're not tightly , uh , tuning the individual parts th of that overlap by different speakers .
Professor A: Mm - hmm . Yeah .
Postdoc F: So someone may have only said two words in that entire big chunk of overlap .
Professor A: Yeah .
Postdoc F: And for purposes of of , uh , things like well , so things like training the speech - nonspeech segmentation thing .
PhD E: Yeah .
Postdoc F: Th - it 's necessary to have it more tightly tuned than that .
Professor A: OK .
Postdoc F: And w and w and , you know , is a It would be wonderful if , uh , it 's possible then to use that algorithm to more tightly tie in all the channels after that but , um , you know , I 've th the So , I I don't know exactly where that 's going at this point . But m I was experimenting with doing this by hand and I really do think that it 's wise that we 've had them start the way we have with , uh , m y working off the mixed signal , um , having the interface that doesn't require them to do the ti uh , the time bins for every single channel at a t uh , through the entire interaction .
Professor A: Mm - hmm .
Postdoc F: Um , I did discover a couple other things by doing this though , and one of them is that , um , um , once in a while a backchannel will be overlooked by the transcriber .
Professor A: Mm - hmm .
Postdoc F: As you might expect ,
Professor A: Sure .
Postdoc F: because when it 's a b backchannel could well happen in a very densely populated overlap . And if we 're gonna study types of overlaps , which is what I wanna do , an analysis of that , then that really does require listening {comment} to every single channel all the way through the entire {comment} length for all the different speakers . Now , for only four speakers , that 's not gonna be too much time , but if it 's nine speakers , then that i that is more time . So it 's li you know , kind of wondering And I think again it 's like this it 's really valuable that Thilo 's working on the speech - nonspeech segmentation because maybe , um , we can close in on that wi without having to actually go to the time that it would take to listen to every single channel from start to finish through every single meeting .
PhD E: Yeah , but those backchannels will always be a problem I think . Uh especially if they 're really short and they 're not very loud and so it it can it it will always happen that also the automatic s detection system will miss some of them , so .
Postdoc F: OK . Well so then then , maybe the answer is to , uh , listen especially densely in places of overlap ,
PhD E: Yeah .
Postdoc F: just so that they 're they 're not being overlooked because of that , and count on accuracy during the sparser phases .
PhD E: Yeah .
Postdoc F: Cuz there are large s spaces of the That 's a good point . There are large spaces where there 's no overlap at all . Someone 's giving a presentation ,
PhD E: Yeah .
Postdoc F: or whatever . That 's that 's a good that 's a good thought . And , um , let 's see , there was one other thing I was gonna say . I I think it 's really interesting data to work with , I have to say , it 's very enjoyable . I really , not not a problem spending time with these data . Really interesting . And not just because I 'm in there . No , it 's real interesting .
Professor A: Uh , well I think it 's a short meeting . Uh , you 're you 're you 're still in the midst of what you 're doing from what you described last time , I assume ,
PhD C: Is true .
Postdoc F :
Professor A: and PhD C: I haven't results , eh , yet
Professor A: Yeah .
PhD C: but , eh , I I 'm continue working with the mixed signal now , {comment} after the the last experience .
Professor A: Yeah . Yeah .
PhD C: And and I 'm tried to to , uh , adjust the to to improve , eh , an harmonicity , eh , detector that , eh , I I implement .
Professor A: Yeah .
PhD C: But I have problem because , eh , I get , eh , eh , very much harmonics now .
Professor A: Yeah .
PhD C: Um , harmonic possi possible harmonics , uh , eh , and now I 'm I 'm I 'm trying to to find , eh , some kind of a , um  of h of help , eh , using the energy to to distinguish between possible harmonics , and and other fre frequency peaks , that , eh , corres not harmonics . And , eh , I have to to talk with y with you , with the group , eh , about the instantaneous frequency , because I have , eh , an algorithm , and , I get , mmm , eh , t t results similar results , like , eh , the paper , eh , that I I am following . But , eh , the the rules , eh , that , eh , people used in the paper to to distinguish the harmonics , is doesn't work well .
Professor A: Mm - hmm .
PhD C: And I I I I not sure that i eh , the the way o to ob the way to obtain the the instantaneous frequency is right , or it 's it 's not right . Eh ,
Professor A: Yeah .
PhD C: I haven't enough file feeling to to to distinguish what happened .
Professor A: Yeah , I 'd like to talk with you about it . If if if , uh If I don't have enough time and y you wanna discuss with someone else some someone else besides us that you might want to talk to , uh , might be Stephane .
PhD C: Yeah . I talked with Stephane and and Thilo
Professor A: Yeah and and Thilo , yeah .
PhD C: and ,
Professor A: Yeah , but PhD C: they nnn they  they {comment}  they didn't PhD E: I 'm not too experienced with harmonics
Professor A: I see .
PhD C: they think that {comment} the experience is not enough to PhD E: and PhD G: Is is this the algorithm where you hypothesize a fundamental , and then get the energy for all the harmonics of that fundamental ?
PhD C: No , no it 's No No . No .
PhD G: And then hypothesize a new fundamental and get the energy Professor A: Yeah , that 's wh
PhD C: No . I I I I don't proth process the the fundamental . I  I , ehm I calculate the the phase derivate using the FFT .
Professor A: Yeah .
PhD C: And The algorithm said that , eh , if you if you change the the  the , eh , nnn the X - the frequency " X " , eh , using the in the instantaneous frequency , you can find , eh , how , eh , in several frequencies that proba probably the the harmonics , eh ,
Professor A: Uh - huh .
PhD C: the errors of peaks the frequency peaks , eh , eh , move around these , eh eh frequency harmonic the frequency of the harmonic . And , eh , if you if you compare the the instantaneous frequency , eh , of the of the , eh , continuous , eh , eh , filters , that , eh that , eh , they used eh , to  to to get , eh , the the instantaneous frequency ,
Professor A: Mm - hmm .
PhD C: it probably too , you can find , eh , that the instantaneous frequency for the continuous , eh , eh the output of the continuous filters are very near . And in my case i in equal with our signal , it doesn't happened .
Professor A: Yeah . I 'd hafta look at that and think about it .
PhD C: And Professor A: It 's it 's it 's I haven't worked with that either so I 'm not sure The way  the simple - minded way I suggested was what Chuck was just saying , is that you could make a a sieve . You know , y you actually say that here is PhD C: Yeah .
Professor A: Let 's let 's hypothesize that it 's this frequency or that frequency , and and , uh , maybe you maybe you could use some other cute methods to , uh , short cut it by by uh , making some guesses ,
PhD C: Yeah .
Professor A: but but uh uh uh , I would , uh I mean you could make some guesses from , uh from the auto - correlation or something but but then , given those guesses , try , um , uh , only looking at the energy at multiples of the of that frequency , and and see how much of the take the one that 's maximum . Call that the PhD C: Yeah .
Professor A: But PhD C: Using the energy of the of the multiple of the frequency .
Professor A: Of all the harmonics of that . Yeah .
PhD C: Yeah .
PhD G: Do you hafta do some kind of , uh , low - pass filter before you do that ?
PhD C: I don't use .
PhD G: Or PhD C: But , I I know many people use , eh , low - pass filter to to to get , eh , the pitch .
Professor A: No . To get the pitch , yes .
PhD C: I don't use . To get the pitch , yes .
PhD E: To get the pitch , yeah .
PhD C: But the harmonic , no .
PhD G: But i But the harmonics are gonna be , uh , uh , I don't know what the right word is . Um , they 're gonna be dampened by the uh , vocal tract , right ? The response of the vocal tract .
Professor A: Yeah ?
PhD C: Yeah ?
PhD G: And so just looking at the energy on those at the harmonics , is that gonna ?
Professor A: Well so the thing is that the This is for , uh , a , um PhD G: I m what you 'd like to do is get rid of the effect of the vocal tract . Right ?
PhD E: Yeah .
PhD G: And just look at the at at the signal coming out of the glottis .
Professor A: Yeah . Uh , well , yeah that 'd be good .
PhD C: Yeah .
Professor A: But , uh but I but  but I don't know that you need to Grad B: Open wide !
Professor A: but I don't need you know if you need to get rid of it . I mean that 'd that 'd be nice but I don't know if it 's ess if it 's essential . Um , I mean cuz I think the main thing is that , uh , you 're trying PhD G: Uh - huh .
Professor A: wha what are you doing this for ? You 're trying distinguish between the case where there is , uh where where there are more than uh , where there 's more than one speaker and the case where there 's only one speaker .
Grad B: Sorry .
Professor A: So if there 's more than one speaker , um yeah I guess you could I guess yeah you 're so you 're not distinguished between voiced and unvoiced , so so , i if you don't if you don't care about that PhD C: Yeah .
Professor A: See , if you also wanna just determine if you also wanna determine whether it 's unvoiced , then I think you want to look look at high frequencies also , because the f the fact that there 's more energy in the high frequencies is gonna be an ob sort of obvious cue that it 's unvoiced .
PhD G: Yeah .
Professor A: But , i i uh  I mean i i but , um , other than that I guess as far as the one person versus two persons , it would be primarily a low frequency phenomenon . And if you looked at the low frequencies , yes the higher frequencies are gonna there 's gonna be a spectral slope . The higher frequencies will be lower energy . But so what . I mean  that 's that 's w
PhD C: I will prepare for the next week eh , all my results about the harmonicity and will will try to come in and to discuss here , because , eh , I haven't enough feeling to  to u many time to  to understand what happened with the with , eh , so many peaks , eh , eh , and I I see the harmonics there many time but , eh , there are a lot of peaks , eh , that , eh , they are not harmonics .
Professor A: Yeah .
PhD C: Um , I have to discover what what is the the w the best way to to {comment} to {comment} c to use them
Professor A: Well , but yeah I don't think you can I mean you 're not gonna be able to look at every frame , so I mean I I mean I I really I I really thought that the best way to do it , and I 'm speaking with no experience on this particular point , but , my impression was that the best way to do it was however you You 've used instantaneous frequency , whatever . {comment} However you 've come up you with your candidates , you wanna see how much of the energy is in that
PhD C: Yeah . Yeah .
Professor A: as coppo as opposed to all of the all the total energy . And , um , if it 's voiced , I guess so so y I think maybe you do need a voiced - unvoiced determination too . But if it 's voiced ,
PhD C: Yeah .
Professor A: um , and the , uh e the fraction of the energy that 's in the harmonic sequence that you 're looking at is relatively low , then it should be then it 's more likely to be an overlap .
PhD C: Is height . Yeah . This this is the idea the idea I I I had to to compare the the ratio of the  the energy of the harmonics with the eh , with the , eh , total energy in the spectrum and try to get a ratio to to distinguish between overlapping and speech . Mmm .
Professor A: But you 're looking a y you 're looking at Let 's take a second with this . Uh , uh , you 're looking at f at the phase derivative , um , in in , uh , what domain ? I mean this is this is in in in in bands ? Or or PhD C: No , no , no .
Professor A: Just just overall PhD C: It 's a it 's a o i w the band the band is , eh , from zero to to four kilohertz . And I I ot I Professor A: And you just take the instantaneous frequency ?
PhD C: Yeah . I u m t I I used two m two method two methods . Eh , one , eh , based on the F eh , FTT . to FFT to to obtain the or to study the harmonics from from the spectrum directly ,
Professor A: Yeah .
PhD C: and to study the energy and the multiples of
Professor A: Yeah .
PhD C: frequency . And another another algorithm I have is the in the instantaneous frequency , based on on on the FFT to to to calculate the the phase derivate in the time . Eh , uh n the d I mean I I have two two algorithms .
Professor A: Right .
PhD C: But , eh , in m i in my opinion the the the instantaneous frequency , the the the behavior , eh , was th it was very interesting . Because I I saw eh , how the spectrum concentrate , eh ,
Professor A: Oh !
PhD C: around the the harmonic . But then when I apply the the rule , eh , of the in the  the instantaneous frequency of the ne of the continuous filter in the the near filter , the the rule that , eh , people propose in the paper doesn't work . And I don't know why .
Professor A: But the instantaneous frequency , wouldn't that give you something more like the central frequency of the you know , of the where most of the energy is ? I mean , I think if you Does i does it Why would it correspond to pitch ?
PhD C: Yeah . I I I not sure . I I I try to to Professor A: Yeah .
PhD C: When first I   I calculate , eh , using the FFT ,
Postdoc F: Di - digital camera .
PhD C: the the Grad B: Keep forgetting .
PhD C: I get the  the spectrum ,
Professor A: Yeah .
PhD C: and I represent all the frequency .
Professor A: Yeah .
PhD C: And when ou I obtained the instantaneous frequency . And I change the the the @ @ , using the instantaneous frequency , here .
Professor A: Oh , so you scale you s you do a a scaling along that axis according to instantaneous PhD C: I use Yeah .
Professor A: It 's a kinda normalization .
PhD C: Yeah . Yeah . Because when when Professor A: OK .
PhD C: eh , when i I I use these these frequency , eh , the range is different , and the resolution is different .
Professor A: Yeah .
PhD C: And I observe more more or less , thing like this . And the paper said that , eh , these frequencies are probably , eh , harmonics .
Professor A: I see . Huh .
PhD C: But , eh , they used , eh , a rule , eh , based in the in the because to to calculate the instantaneous frequency , they use a Hanning window .
Professor A: Yeah .