PhD C: And I have , eh , some ideas . Eh , this information is very very useful . Because you have the the the distribution , now .
Postdoc E: I 'm glad to hear it . Glad to hear it .
PhD C: But for me , eh is interesting because , eh , eh , here 's i is the demonstration of the overlap , eh , problem .
Grad B: I 've seen it already .
PhD C: It 's a real problem , {comment} a frequently problem {comment} uh , because you have overlapping zones eh , eh , eh , all the time .
Postdoc E: Yeah . Yeah .
Grad B: Yep .
PhD C: Yeah .
Grad B: Throughout the meeting .
PhD C: Eh , by a moment I have , eh , nnn , the , eh , n I I did a mark of all the overlapped zones in the meeting recording , with eh , a exact mark .
Grad B: Mm - hmm . Oh , you did that by hand ?
PhD C: Heh ? That 's eh , yet b b Yeah , by b b by hand by hand because , eh , eh " Why . "
Grad B: Can I see that ? Can I get a copy ?
Professor D: Oh .
PhD C: My my idea is to work PhD A: Wow !
PhD C: I I I do I don I don't @ @ I don't know , eh , if , eh , it will be possible because I I I haven't a lot eh , enough time to to to work . uh , only just eh , six months , as you know , but , eh , my idea is , eh , is very interesting to to work in in the line of , eh , automatic segmenter .
Grad B: Mm - hmm .
PhD C: Eh but eh , eh , in my opinion , we need eh , eh , a reference eh session to t to to evaluate the the the tool .
Grad B: Yes , absolutely . And so are you planning to do that or have you done that already ?
PhD C: And No , no , with i
Grad B: Have you done that or are you planning to do that ?
PhD C: Sorry ? No , I I plan to do that .
Grad B: OK . Darn !
PhD C: I plan I plan , but eh , eh , the idea is the is the following . Now , eh , I need ehm , to detect eh all the overlapping zones exactly . I I will I will eh , talk about eh , in the in the blackboard about the my ideas .
Postdoc E: Yeah .
Professor D: Mm - hmm .
Postdoc E: Duration .
PhD C: Eh , um ,  eh This information eh , with eh , exactly time marks eh , for the overlapping zones eh overlapping zone , and eh , a speaker a a pure speech eh , eh , speaker zone . I mean , eh zones eh of eh speech of eh , one speaker without any any eh , noise eh , any any acoustic event eh that eh , eh , w eh , is not eh , speech , real speech . And , I need t true eh , silence for that , because my my idea is to to study the nnn the  the set of parameters eh , what , eh , are more m more discriminant to eh , classify .
Grad B: Right .
PhD C: the overlapping zones in cooperation with the speech eh zones . The idea is to eh to use eh , I 'm not sure to eh yet , but eh my idea is to use a a cluster  eh algorithm or , nnn , a person strong in neural net algorithm to eh to eh study what is the , eh , the property of the different feat eh feature , eh , to classify eh speech and overlapping eh speech .
PhD A: Mmm .
PhD C: And my idea is eh , it would be interesting to to have eh , a control set . And my control set eh , will be the eh , silence , silence without eh , any any noise .
Professor D: Mm - hmm .
Postdoc E: Which means that we 'd still You 'd hear the Grad B: Yeah , fans .
PhD C: Yeah , acoustic with this . {comment} With with , yeah , the background .
Postdoc E: Yeah . {comment} That 's interesting . This is like a ground level , with It 's not it 's not total silence .
PhD C: Eh , I I mean eh , noise eh , eh claps eh , tape clips , eh , the difference eh ,
Professor D: Mm - hmm .
PhD C: eh , eh , event eh , which , eh , eh , has , eh eh , a hard effect of distorti spectral distortion in the in the eh speech .
Grad B: So so you intend to hand - mark those and exclude them ?
Professor D: Mm - hmm .
Postdoc E: Mm - hmm .
PhD C: Yeah , I have mark in in in in that Not in all in all the the file ,
Grad B: Mm - hmm .
PhD C: only eh , eh , nnn , mmm , I have eh , ehm I don't remind {comment} what is the the the the quantity , but eh , I I have marked enough speech on over and all the overlapping zones . I have , eh , two hundred and thirty , more or less , overlapping zones , and is similar to to this information ,
Grad B: Whew ! Mm - hmm .
Postdoc E: Great . Great .
PhD C: because with the program , I cross the information of uh , of Jane {comment} with eh , my my segmentation by hand . And is eh , mor more similar .
Postdoc E: Excellent . Glad to hear it . Good .
PhD C: But Sorry , sorry .
Professor D: Go ahead .
PhD C: And the the idea is , eh , I I will use , eh , I want  My idea is , eh ,  to eh {comment} {nonvocalsound} to classify .
Grad B: I should 've got the digital camera . Oh well .
PhD C: I I need eh , the exact eh , mark of the different , eh , eh , zones because I I want to put , eh , for eh , each frame a label indicating . It 's a sup supervised and , eh , hierarchical clustering process . I I I put , eh , eh , for each frame {nonvocalsound} a label indicating what is th the type , what is the class , eh , which it belong .
Grad B: Mm - hmm .
PhD C: Eh , I mean , the class you will {nonvocalsound} overlapping speech " overlapping " is a class , eh , " speech " {nonvocalsound} @ @ the class that 's
Grad B: Nonspeech .
PhD A: These will be assigned by hand ?
PhD C: a I I I ha I h I I put the mark by hand ,
PhD A: Based on the Uh - huh .
PhD C: because , eh , my idea is , eh , in in the first session , I need , eh , I I need , eh , to be sure that the information eh , that , eh , I I will cluster , is is right . Because , eh , eh , if not , eh , I will I will , eh , return to the speech file to analyze eh , what is the problems ,
Grad B: Well , training , and validation . Sure . Mm - hmm .
PhD C: eh . And I I 'd prefer I would prefer , the to to have , eh , this labeled automatically , but , eh , eh , fro th I need truth .
PhD A: You need truth . Hmm .
Grad B: Yeah , but this is what you 're starting with .
PhD C: Yeah . Yeah . Yeah . Yeah .
Postdoc E: I 've gotta ask you . So , uh , the difference between the top two , i So so I start at the bottom , so " silence " is clear . By " speech " do you mean speech by one sp by one person only ?
PhD C: Speech Yeah .
Postdoc E: So this is un OK , and then and then the top includes people speaking at the same time , or or a speaker and a breath overlapping , someone else 's breath , or or clicking , overlapping with speech So , that that 's all those possibilities in the top one .
PhD C: Yeah . Yeah . Is Grad B: One or two or more .
PhD C: One , two , three . but No , by th by the moment n Yeah . Yeah . Yeah . Yeah . Yeah .
Postdoc E: OK .
PhD C: Eh , in the first moment , because , eh , eh , I I have information , eh , of the overlapping zones , eh , information about if the , eh , overlapping zone is , eh , from a speech , clear speech , from a one to a two eh speaker , or three speaker , or is is the zone where the breath of a speaker eh , overlaps eh , onto eh , a speech , another , especially speech .
Postdoc E: So it 's basi it 's basically speech wi som with with something overlapping , which could be speech but doesn't need to be .
PhD C: No , no , es especially eh , overlapping speech from , eh , different eh , eh , speaker . Eh Professor D: No , but there 's but , I think she 's saying " Where do you In these three categories , where do you put the instances in which there is one person speaking and other sounds which are not speech ? "
PhD C: Ah !
Professor D: Which category do you put that in ?
Postdoc E: Yeah , that 's right . That 's my question .
PhD C: Yeah . Yeah , he here I I put eh speech from eh , from , eh , one speaker without , eh , eh , any any any events more .
Postdoc E: Oh !
Professor D: Right , so where do you put speech from one speaker that does have a nonspeech event at the same time ?
PhD C: Where ? Where What is the class ?
Professor D: Which catege which category ?
Postdoc E: Like a c
PhD C: No . By the moment , no .
Grad B: Yeah , yeah , that 's what he was saying before .
PhD C: For for the by the @ @ no , @ @ because I I I I want to limit the the nnn , the the study .
Professor D: Oh , so you not not marked .
Postdoc E: Oh . So you don't i i it 's not in that Professor D: OK . Got it . Fine . So so PhD A: So you 're not using all of the data .
Grad B: Yeah , so that 's what he was saying before , is that he excluded those .
PhD C: The All I Exactly .
Grad B: Yeah .
PhD C: Yeah , you mean Professor D: Yeah .
Postdoc E: So you 're ignoring overlapping events unless they 're speech with speech .
PhD C: Yeah , be Yeah .
Professor D: Yeah , that 's fine .
Postdoc E: OK .
PhD C: " Why ? Why ? What 's the reason ? " because i it 's the first study . the first
Professor D: Oh , no no , it 's a perfectly sensible way to go . We just wondered trying to understand what what you were doing .
Postdoc E: We 're just
PhD C: Yeah .
Postdoc E: Yeah .
Professor D: OK .
Postdoc E: Yeah cuz you 've talked about other overlapping events in the past .
PhD C: Yeah .
Postdoc E: So , this is this is a subset .
PhD C: Yeah . In the in the future , the the idea is to to extend the class ,
PhD A: Is is PhD C: to consider all the all the information , you you mentioned before
Professor D: Yeah . Yeah , I I don't think we were asking for that .
Postdoc E: OK .
PhD C: but eh , the the first idea Because eh , I don't know what hap what will happen {comment} with the study .
Professor D: We were jus just trying to understand Postdoc E: Yeah . Yeah , we just wanted to know what the category was here .
Grad B: Right .
Professor D: Yeah . Sure .
PhD A: Is your silence category pure silence , or ?
PhD C: Yeah . i it 's pure PhD A: What if there was a door - slam or something ?
PhD C: No , no , it 's pure silence .
PhD A: Pure silence .
PhD C: It 's the control set .
PhD A: OK .
PhD C: OK ? It 's the control set . It 's pure si pure silence {comment} with the with the machine on the on the roof .
Professor D: What you Well w I I think what you m I think what you mean is that it 's nonspeech segments that don't have impulsive noises .
Grad B: With the fan .
PhD C: Yeah .
Professor D: Right ? Cuz you 're calling what you 're calling " event " is somebody coughing or clicking , or rustling paper , or hitting something , which are impulsive noises .
PhD C: Yeah .
Professor D: But steady - state noises are part of the background .
PhD C: Yeah .
Professor D: Which , are being , included in that . Right ?
PhD C: h here yet , yet I I I I I think I I think , eh , there are that some kind of noises that , eh , don't don't wanted to to be in that , eh , in that control set .
Professor D: Yeah .
Postdoc E: So it 's like a signal - noise situation . Yeah .
Professor D: Well Yeah .
PhD C: But I prefer , I prefer at at the first , eh , the the silence with eh , this eh this kind of the of eh of noise .
Postdoc E: Well , steady state .
Professor D: Right , it 's I mean , it 's " Background " might be might be a better word than " silence " .
PhD C: Yeah .
Professor D: It 's just sort of that the the background acoustic PhD C: Yeah .
Grad B: Right . So Fine . Go on .
PhD C: Yeah .
Professor D: Yeah .
PhD C: Is is is only OK .
Professor D: Yeah .
PhD C: And , um , with this information The idea is eh , eh , nnn , I have a label for for each , eh , frame and , eh with a cluster eh algorithm I and Postdoc E: Well , we needed to get the categories , yeah .
PhD C: Sorry . And eh I am going to prepare a test bed , eh , well , eh , a a set of feature structure eh , eh , models .
Grad B: Right .
PhD C: And my idea is
Grad B: " Tone " , whatever .
PhD C: so so on because I have a pitch extractor yet .
Professor D: Right .
Grad B: Mm - hmm .
PhD C: I have to to test , but eh I PhD A: You have your own ?
PhD C: Yeah , yeah , yeah .
PhD A: Oh !
PhD C: I ha I have prepare . Is a modified version of of of a pitch tracker , eh , from , eh , Standar - eh Stanford University in Stanford ? No . From , eh , em , Cambridge University .
PhD A: Oh ! What 's it written in ?
PhD C: Eh , em , I I I don't remember what is the the name of the of the author , because I I have several I have eh , eh , em , eh , library tools , from eh , Festival and of from Edinburgh eh , from Cambridge , eh , and from our department .
PhD A: Ah .
Professor D: Mm - hmm . Mm - hmm .
PhD C: And And I have to because , in general the pitch tracker , doesn't work {comment} very well and Grad B: Bad . Right . But , you know , as a feature , it might be OK . So , we don't know .
PhD C: Yeah . Yeah . This this is And th the idea is to to , eh , to obtain , eh , for example , eh ,  eh diff eh , eh , different well , no , a great number of eh FEC for example , eh , eh , twenty - five , eh , thirty thirty parameters , eh , for for each one . And in a first eh , nnn , step in the investi in the research in eh , my idea is try to , eh , to prove , what is the performance of the difference parameter , eh to classify the different , eh , what is the the the the front - end approach to classify eh , the different , eh , frames of each class eh and what is the the , nnn , nnn , nnn , eh , what is the , the error eh , of the data
Grad B: Supervised clustering . Mm - hmm .
PhD C: This is the the eh , first idea
Postdoc E: Mm - hmm .
PhD C: and the second is try to eh , to use some ideas eh , similar to the linear discriminant analysis .
Grad B: Mm - hmm .
PhD C: Eh ? Eh , similar , because the the idea is to to study what is the contribution of eh , each parameter to the process of classify correctly the different the different parameters .
Grad B: Mm - hmm . What sort of classifier ar ?
PhD C: Eh , the the the classifier is nnn by the moment is eh is eh , similar , nnn , that the classifier used eh , in a quantifier vectorial quantifier is eh , used to to eh , some distance to to put eh , a vector eh , in in a class different .
Grad B: Unimodal ?
PhD C: Is Yeah ? W with a model , is is only to cluster using a eh , @ @ or a similarity .
Postdoc E: Mm - hmm .
Grad B: So is it just one cluster per PhD C: A another possibility it to use eh a netw netw a neural network .
Grad B: Right .
PhD C: But eh what 's the p What is my idea ? What 's the problem I I I I see in in in  if you you use the the neural network ? If w when this kind of eh , mmm , cluster , clustering algorithm to can test , to can eh observe what happened you you can't you can't eh , eh put up with your hand {comment} in the different parameter ,
Grad B: Right , you can't analyse it .
PhD C: but eh If you use a neural net is is a good idea , but eh you don't know what happened in the interior of the neural net .
Professor D: Well , actually , you can do sensitivity analyses which show you what the importance of the different parce pieces of the input are .
PhD C: Yeah .
Professor D: It 's hard to w w what you It 's hard to tell on a neural net is what 's going on internally .
PhD C: Yeah .
Professor D: But it 's actually not that hard to analyse it and figure out the effects of different inputs , especially if they 're all normalized .
PhD C: Yeah . Yeah .
Professor D: Um , but Grad B: Well , using something simpler first I think is probably fine .
Professor D: Well , this isn't tru if if if you really wonder what different if if PhD C: Yeah .
Grad B: Decision tree .
PhD C: But Professor D: Yeah , then a decision tree is really good , but the thing is here he 's he 's not he 's not like he has one you know , a bunch of very distinct variables , like pitch and this he 's talking about , like , a all these cepstral coefficients , and so forth ,
Grad B: Right .
PhD C: Yeah . Yeah .
Grad B: Right .
PhD C: Yeah .
Professor D: in which case a a any reasonable classifier is gonna be a mess , and it 's gonna be hard to figure out what what uh PhD C: And Grad B: Right .
PhD C: I I I will include too the the the differential de derivates too .
Grad B: Deltas ,
Professor D: Yeah .
Grad B: yeah . So .
Professor D: I I mean , I think the other thing that one I mean , this is , I think a good thing to do , to sort of look at these things at least See what I 'd I 'd Let me tell you what I would do . I would take just a few features . Instead of taking all the MFCC 's , or all the PLP 's or whatever , I would just take a couple .
PhD C: Yeah .
Professor D: OK ? Like like C - one , C - two , something like that , so that you can visualize it .
PhD C: Yeah .
Professor D: and look at these different examples and look at scatter plots .
PhD C: Yeah .
Professor D: OK , so before you do build up any kind of fancy classifiers , just take a look in two dimensions , at how these things are split apart .
PhD C: Yeah .
Professor D: That I think will give you a lot of insight of what is likely to be a useful feature when you put it into a more complicated classifier .
PhD C: Yeah .
Professor D: And the second thing is , once you actually get to the point of building these classifiers , @ @ what this lacks so far is the temporal properties . So if you 're just looking at a frame and a time , you don't know anything about , you know , the structure of it over time , and so you may wanna build @ @ build a Markov model of some sort uh , or or else have features that really are based on um on on some bigger chunk of time .
PhD C: Yeah .
Grad B: Context window ?
PhD C: Yeah . Yeah .
Professor D: But I think this is a good place to start . But don't uh anyway , this is my suggestion , is don't just , you know , throw in twenty features at it , the deltas , and the delta del and all that into some classifier , even even if it 's K - nearest - neighbors , you still won't know
PhD C: Yeah . Yeah , yeah .
Professor D: what it 's doing , even You know it 's Uh , I think to know what it 's to have a better feeling for what it 's
Grad B: Yep .
Professor D: look at at som some picture that shows you , " Here 's These things uh , uh are offer some separation . " And , uh , in LPC , uh , the thing to particularly look at is , I think is something like , uh , the residual PhD C: Yeah .
Professor D: Um So .
PhD C: Yeah . S
Postdoc E: Can I ask ? It strikes me that there 's another piece of information um , that might be useful and that 's simply the transition . So , w if you go from a transition of silence to overlap versus a transition from silence to speech , there 's gonna be a b a big informative area there , it seems to me .
PhD C: Yeah , because Yeah yeah . Yeah . Yeah . I Yeah . But eh I I Is my my my own vision , of the of the project .
Grad B: So , some sort of That 's Postdoc E: Mm - hmm .
PhD C: I eh the the Meeting Recorder project , for me , has eh , two eh , w has eh several parts , several p objective
Professor D: Mm - hmm .
PhD C: eh , because it 's a a great project . But eh , at the first , in the acoustic , eh , eh , parts of the project , eh I think you eh we have eh  two main eh objective . One one of these is to eh to detect the change , the acoustic change . And for that , if you don't use , eh , eh , a speech recognizer , eh broad class , or not broad class to to try to to   to label the different frames , I think the Ike criterion or BIC criterion eh will be enough to detect the change .
Postdoc E: OK .
PhD C: And Probably . {comment} I I I I would like to to t prove . Uh , probably . When you you have , eh , eh s eh the transition of speech or or silence eh to overlap zone , this criterion is enough with  probably with , eh , this kind of , eh , eh the the the more eh use eh use eh used eh em normal , regular eh parameter MF - MFCC . you you have to to to find you can find the the mark . You can find the nnn , the the acoustic change . But eh eh I I understand that you your objective is to eh classify , to know that eh that zone not is only {comment} a new zone in the in the file , that eh you have eh , but you have to to to know that this is overlap zone . because in the future you will eh try to to process that zone with a non - regular eh eh speech recognizer model , I suppose .
Professor D: Mm - hmm .
PhD C: you you will pretend {comment} to to to process the overlapping z eh zone with another kind of algorithm
Professor D: Mm - hmm .
PhD C: because it 's very difficult to to to obtain the transcription from eh using eh eh a regular , normal speech recognizer . That , you know , I I I think is the idea . And so eh the , nnn the {nonvocalsound} the system eh will have two models .
Postdoc E: Clustering .
PhD C: A model to detect more acc the mor most accurately possible that is p uh , will be possible the , eh the mark , the change and another another model will @ @ or several models , to try s but eh several model eh robust models , sample models to try to classify the difference class .
Postdoc E: OK .
Grad B: I 'm I 'm I 'm sorry , I didn't understand you what you said . What what model ?
Postdoc E :
PhD C: Eh , the the classifiers of the of the n to detect the different class to the different zones before try to to recognize , eh with eh to transcribe , with eh a speech recognizer .
Grad B: Mm - hmm .
PhD C: And my idea is to use eh , for example , a neural net
Postdoc E: So p
PhD C: with the information we obtain from this eh this eh study of the parameter with the selected parameter to try to eh to put the class of each frame . Eh for the difference zone
Grad B: Features . Yeah .
PhD C: you you eh , eh have obtained in the first eh , step with the for example , BIC eh , eh criterion compare model
Postdoc E: Mm - hmm .
PhD C: And  You I don't - u
Professor D: OK , but , I I think in any event we 're agreed that the first step is PhD C: i
Postdoc E: Yeah .
Professor D: Because what we had before for for uh , speaker change detection did not include these overlaps .
PhD C: Yeah .
Professor D: So the first thing is for you to to build up something that will detect the overlaps .
PhD C: Yeah .
Professor D: Right ? So again , I think the first thing to do to detect the overlaps is to look at these uh , in in in in Grad B: Features ?
PhD C: Yeah .
Professor D: Well , I again , the things you 've written up there I think are way too way too big .
PhD C: Yeah .
Professor D: OK ? If you 're talking about , say , twelfth twelfth - order uh MFCC 's or something like that it 's just way too much .
PhD C: Yeah .
Professor D: You won't be able to look at it . All you 'll be able to do is put it into a classifier and see how well it does .
PhD C: Yeah .
Professor D: Whereas I think if you have things if you pick one or two dimensional things , or three of you have some very fancy display , uh , and look at how the the different classes separate themselves out , you 'll have much more insight about what 's going on .
PhD C: It will be enough .
Professor D: Well , you 'll you 'll get a feeling for what 's happening , you know ,
PhD C: Yeah .
Professor D: so if you look at  Suppose you look at first and second - order cepstral coefficients for some one of these kinds of things and you find that the first - order is much more effective than the second , and then you look at the third and there 's not and not too much there , you may just take first and second - order cepstral coefficients ,
PhD C: Yeah . Yeah .
Professor D: right ? And with LPC , I think LPC per se isn't gonna tell you much more than than than the other , maybe . Uh , and uh on the other hand , the LPC residual , the energy in the LPC residual , will say how well , uh the low - order LPC model 's fitting it , which should be pretty poorly for two two or more people speaking at the same time , and it should be pretty well , for w for for one .
PhD C: Yeah . Yeah . Yeah .
Professor D: And so I i again , if you take a few of these things that are are prob um {comment} promising features and look at them in pairs , uh , I think you 'll have much more of a sense of " OK , I now have  uh , doing a bunch of these analyses , I now have ten likely candidates . " And then you can do decision trees or whatever to see how they combine .
PhD C: Yeah . Yeah .
PhD A: I 've got a question .
PhD C: Yeah . This
Postdoc E: Interesting .
PhD C: Sorry .
Postdoc E: Hmm .
PhD C: but eh , eh eh eh eh I don't know it is the first eh way to to do that and I would eh like to to know what eh , your opinion . Eh all this study in the f in the first moment , I I w I I will pretend to do {comment} with eh eh equalizes speech . The the equalizes speech , the speech eh , the mixes of speech .
Grad B: With Postdoc E: With what ? With what ?
Grad B: Right . Mixed .
PhD C: the the mix , mixed speech .
Postdoc E: " Mixed " . Thank you .
PhD C: Eh , why ? Because eh the spectral distortion is {comment} more eh a lot eh clearer , very much clearer if we compare with the PDA .
Grad B: Right .
PhD C: PDA speech file is eh it will be eh difficult . I Postdoc E: So it 's messier .
PhD C: Yeah ,
Postdoc E: The the PDA is messier .
PhD C: fff ! {comment} Because the n the noise eh to sp the signal - to - noise relation is eh is is low .
Professor D: OK .
Grad B: Yeah , I think that that 's a good way to start .
PhD C: And , I don't know Grad B: But .
PhD C: I don't know eh uh i i that eh the  the result of the of the study eh with eh with eh this eh this speech , the mix speech eh will work exactly with the eh PDA files .
Grad B: It would be interesting in itself to see . Well , I think that would be an interesting result .
PhD C: eh What , I I mean , what what is the effect of the low ' signal to to to noise relation , you know , eh with Professor D: N u We Well , I think I think I think it 's not a it 's not at all unreasonable . It makes sense to start with the simpler signal because if you have features which don't aren't even helpful in the high signal - to - noise ratio , then there 's no point in putting them into the low signal ratio , one would think , anyway .
PhD C: Yeah .
Professor D: And so , if you can get @ @ {comment} Uh again , my prescription would be that you would , with a mixed signal , you would take a collection of possible uh , features look at them , look at how these different classes that you 've marked , separate themselves , {comment} and then collect , uh in pairs , and then collect ten of them or something , and then proceed with a bigger classifier .
PhD C: Yeah . Yeah .
Professor D: And then if you can get that to work well , then you go to the other signal . And then , and you and you know , they won't work as well , but how m you know , how much Grad B: Right .
PhD C: Yeah . Yeah . Yeah .
Professor D: And then you can re - optimize , and so on .
Grad B: Yeah . But it I think it would be interesting to try a couple with both . Because it I think it would be interesting to see if some features work well with close mixed , and And don't Professor D: Hmm .
PhD C: Ah , yeah , yeah yeah yeah .
Professor D: That 's well , the It it 's it 's true that it also , it could be useful to do this exploratory analysis where you 're looking at scatter plots and so on in both cases . Sure .
PhD C: But Grad B: Mm - hmm .
PhD C: I I I I think that the the eh parameter we found , eh , eh worked with both eh , speech file ,
Postdoc E: That 's good .
PhD C: but eh what is the the the relation of eh of the performance when eh you use eh the , eh eh speech file the PDA speech files .
Professor D: Hmm .
PhD C: Yeah , I don't know .
Professor D: Right .
PhD C: But it I I I I think it will be important . Because eh people eh eh , different groups eh has eh experience with this eh kind of problem . Is eh is not easy eh to to solve , because if you I I  I have seen the the the speech file from eh PDA , and s some parts is {comment} very difficult because you you don't see the spectrum the spectrogram .
Grad B: Right . Yeah , they 're totally hidden .
PhD C: Is very difficult to apply eh , eh a parameter to detect change when you don't see .
Professor D: Yeah . Yeah . Well , that that that 's another reason why very simple features , things like energy , and things things like harmonicity , and residual energy are uh , yeah are are better to use than very complex ones because they 'll be more reliable .
PhD C: But I suppose Grad B: Are probably better , yep .
PhD C: Yeah , yeah yeah , I I I will put eh the energy here . Yeah . Yeah . Yeah .
Professor D: Ch - Chuck was gonna ask something I guess .
PhD C: You have a question .
PhD A: Yeah , I maybe this is a dumb question , but w I thought it would be  I thought it would be easier if you used a PDA
Professor D: Nah .
PhD A: because can't you , couldn't you like use beam - forming or something to detect speaker overlaps ? I mean Grad B: Well , if you used the array , rather than the signal from just one .
PhD A: Uh - huh .
Professor D: Yeah , no , you you 're you 're right
Grad B: But that 's Professor D: that In fact , if we made use of the fact that there are two microphones , you do have some location information . which we don't have with the one and and so that 's PhD A: Is that not allowed with this project ?
Professor D: Uh , well , no , I mean , we we don't have any rules , r really .
PhD A: But I didn't mean I w Given given the goal .
Professor D: I think  I I think I think it 's it 's it 's a it 's an additional interesting question .
PhD A: I mean , is is that violation of the PhD C: Oh . No . Yeah .
Professor D: I mean , I think you wanna know whether you can do it with one , because you know it 's not necessarily true that every device that you 're trying to do this with will have two .
PhD A: Mm - hmm
PhD C: Yeah .
Professor D: Uh , if , on the other hand , we show that there 's a huge advantage with two , well then that could be a real point .
PhD C: Yeah .
Professor D: But , we don't n even know yet what the effect of detecting having the ability to detect overlaps is . You know , maybe it doesn't matter too much .
PhD A: Right . Right . OK .
PhD C: Yeah . Yeah .
Professor D: So , this is all pretty early stages .
PhD A: I see .
PhD C: Yeah . Yeah , yeah , yeah .
Professor D: But no , you 're absolutely right . That 's a good thing to consider .
PhD A: OK .
Postdoc E: There there is a complication though , and that is if a person turns their back to the to the PDA , then some of the positional information goes away ?
PhD C: Yeah .
Professor D: Well , it it it does , i it d it does , but the the the issue is that that PhD A: No , it 's not it 's not that so much as Postdoc E: And then , And if they 're on the access {comment} on the axis of it , that was the other thing I was thinking .
Grad B: Mm - hmm .
Postdoc E: He You mentioned this last time , that that if if you 're straight down the midline , then then the r the left - right 's gonna be different ,
Grad B: Yeah , we hav need to put it on a little turntable ,
PhD C: I I  I I I th
Grad B: and PhD A: Well , it 's
PhD C: Yeah .
Postdoc E: and and and in his case , I mean , he 's closer to it anyway .
PhD C: Yeah . Yeah .
Postdoc E: It seems to me that that it 's not a p uh , you know , it 's this the topograph the topology of it is is a little bit complicated .
Grad B: But it 's another source of information .
PhD C: I I Yeah .
PhD A: I don't I don't know ho
PhD C: I I I think Sorry . I I I think because the the the distance between the two microph eh , microphone , eh , in the PDA is very near . But it 's uh from my opinion , it 's an interesting idea to to try to study the binaural eh problem eh , with information , because I I found difference between the the speech from from each micro eh , in the PDA .
PhD A: I would guess Grad B: Yep .
Professor D: Yeah , it 's timing difference . It - it 's not amplitude ,
Postdoc E: Oh yeah ! Oh I agree ! And we use it ourselves .
Professor D: right ? S Right .
Postdoc E: I mean , I know I n I know that 's a very important cue .
Grad B: Yep .
PhD C: Yeah .
Postdoc E: But I 'm just I 'm just saying that the way we 're seated around a table , is not the same with respect to each to each person with respect to the PDA ,
PhD C: No . No . No , no , no .
Postdoc E: so we 're gonna have a lot of differences with ref respect to the speaker .
Professor D: That 's That 's fine .
PhD A: But th I don't think that matters , though .
PhD C: But Professor D: That 's So so i @ @ {comment} I think the issue is , " Is there a clean signal coming from only one direction ? "
PhD A: Right .
Professor D: If it 's not coming from just one direction , if it if th if there 's a broader pattern , it means that it 's more likely there 's multiple people speaking ,
PhD C: Yeah .
Professor D: wherever they are .
PhD A: So it 's sort of like how how confused is it about where the beam is .
Professor D: Is it a is it PhD C: Yeah .
Professor D: Yeah , is there a narrow Is there a narrow beam pattern or is it a a distributed beam pattern ? So if there 's a distributed beam pattern , then it looks more like it 's it 's uh , multiple people .
PhD C: Yeah .
Professor D: Wherever you are , even if he moves around .
Postdoc E: OK . Yeah . OK , it just it just seemed to me that uh , that this isn't the ideal type of separation . I mean , I I think it 's I can see the value o
Professor D: Oh , ideal would be to have the wall filled with them , but I mean  But the thing is just having two mikes If you looked at that thing on on Dan 's page , it was When when there were two people speaking , and it looked really really different .
PhD C: Yeah .
Postdoc E: Yeah , OK .
PhD C: Yeah . Yeah .
Grad B: Yep .
Postdoc E: Oh yeah yeah . OK .
PhD A: What looked different ?
PhD C: Yeah .
Postdoc E: Yeah .
Professor D: Uh , well , basic he was looking at correlation .
Grad B: Cross - co cross - correlation .
PhD C: Correlation , yeah .
Professor D: Just cross - correlation between two sides .
PhD A: Did - Sorry , b uh I 'm not sure what Dan 's page is that you mean . He was looking at the two Professor D: So cross - correlation is pretty sensitive .
Postdoc E: Uh , his a web page .
Professor D: You take the signal from the two microphones and you cros and you cross - correlate them with different lags .
Grad B: Subtract them .
PhD A: OK .
Postdoc E: Mm - hmm .
PhD A: Uh - huh .
PhD C: Yeah .
Grad B: And you find They get peaks .
Professor D: OK . So when one person is speaking , then wherever they happen to be at the point when they 're speaking , then there 's a pretty big maximum right around that point in the l in in the lag .
PhD A: OK . OK .
Professor D: So if at whatever angle you are , at some lag corresponding to the time difference between the two there , you get this boost in the in in the cross - correlation value function .
PhD A: So so if there 's two Grad B: And if there are multiple people talking , you 'll see two peaks .
Professor D: It 's spread out .
PhD C: Yeah .
Postdoc E: Well , let me ask you , if if both people were over there , it would be less effective than if one was there and one was across , catty - corner ?
PhD C: Yeah .
Professor D: Yeah . The - the Oh , I 'm sorry ,
Postdoc E: No ?
Professor D: if they 're right next to one another ?
PhD A: If I was if I was here and Morgan was there and we were both talking , it wouldn't work .
Professor D: i i
Postdoc E: Next next one over n over {comment} on this side of the P PDA .
Grad B: Right .
PhD C: Yeah .
Postdoc E: There we go . Good example , the same one I 'm asking .
PhD C: Yeah .
Professor D: Yeah , e I see .
PhD A: Yes .
PhD C: Yeah .
Postdoc E: Versus you versus you know , and we 're catty - corner across the table , and I 'm farther away from this one and you 're farther away from that one .
Grad B: Or or even if , like , if people were sitting right across from each other , you couldn't tell the difference either .
PhD C: Yeah . Yeah . Yeah .
Professor D: Yeah . Oh , yeah .
Postdoc E: It seems like that would be pretty strong .
PhD C: Yeah .
Postdoc E: Across the same axis , you don't have as much to differentiate .
PhD C: Yeah .
Professor D: Well , we d yeah , we don't have a third dimension there . Yeah , so it 's Postdoc E: And so my point was just that it 's it 's gonna be differentially differentially varia valuable .
Grad B: Right .
Postdoc E: I mean , it 's not to say I mean , I certainly think it 's extremely val {comment} And we we humans n n depend on you know , these these binaural cues .
PhD C: Yeah , yeah .
Professor D: But it 's almost but it 's almost a  I think what you 're talking about i there 's two things .
Postdoc E: But .
Grad B: Must do . {comment} Yeah .
Professor D: There 's a sensitivity issue , and then there 's a pathological error uh issue . So th the one where someone is just right directly in line is sort of a pathological error .
Postdoc E: Yes . Yeah .
PhD C: Yeah .
Professor D: If someone just happens to be sitting right there then we won't get good information from it .
Postdoc E: OK . and i and if there So it And if it 's the two of you guys on the same side Professor D: Uh , if they 're if they 're close , it 's just a question of the sensitivity .
Grad B: Yep .
Professor D: So if the sensitivity is good enough and we just we just don't have enough , uh , experience with it to know how Postdoc E: Yeah . OK . Yeah yeah , OK . Yeah .
Grad B: But PhD C: Yeah .
Postdoc E: Oh I 'm not I 'm not trying to argue against using it , by any means . I just wanted to point out that that weakness , that it 's topo topologically impossible to get it perfect for everybody .
Professor D: Yeah . Mm - hmm .
Grad B: And I think Dan is still working on it . So . He actually he wrote me about it a little bit , so .
Postdoc E: Great . No , I don't mean to discourage that at all .
Professor D: I mean , the other thing you can do uh , if I mean , i We 're assuming that it would be a big deal just to get somebody convince somebody to put two microphones in the PDA . But if you h put a third in , you could put in the other axis . And then you know then you 're sort of Yeah , then then you pretty much could cover PhD A: Once you got two Postdoc E: Interesting .
PhD C: Yeah .
PhD A: Well what about just doing it from these mikes ?
Postdoc E: Interesting .
PhD A: You know ?
PhD C: Yeah .
Grad B: Yep .
PhD C: It will be more interesting to study the PZM because the the the separation I I think Professor D: Uh @ @ {comment} But - but that 's I mean , we can we 'll be all of this is there for us to study .
Grad B: Then they 're much broader . Yeah , we can do whatever we want .
PhD C: Yeah .
Professor D: But  but but the thing is , uh , one of the at least one of the things I was hoping to get at with this is what can we do with what we think would be the normal situation if some people get together and one of them has a PDA .
Grad B: Whatever you 're interested in .
PhD C: Yeah .
PhD A: That 's what I was asking about , what are the constraints ?
PhD C: Yeah . Yeah . Yeah .
Professor D: Right . Yeah .
PhD C: Yeah .
Professor D: Well , that 's that 's the constraint of one question that I think both Adam and I were were were interested in .
Grad B: Well PhD A: Mm - hmm .
Grad B: Yep .
PhD A: Mm - hmm .
PhD C: Yeah .
Professor D: Uh , but you know if you can instrument a room , this is really minor league compared with what some people are doing , right ? Some people at at uh , yeah , at Brown and and and and at uh um and at Cape ,
Grad B: Big micro @ @ arrays .
PhD C: Yeah .
PhD A: Didn't they have something at Cape ?
Professor D: they both have these , you know , big arrays on the wall . And you know , if you could do that , you 've got microphones all over the place
Grad B: Very finely .
Professor D: uh , you know p tens of microphones , and and uh PhD A: Oh ! I saw a demo .
PhD C: Oh , right , oh , yeah .
Professor D: And if you do that then you can really get very nice uh kind of selectivity PhD A: Yeah .
Grad B: Oh , I saw one that was like a hundred microphones , a ten by ten array .
Professor D: Yeah . Yeah .
PhD A: And you could In a noisy room , they could have all kinds of noises and you can zoom right in on somebody .
PhD C: Hundred .
Grad B: And they had very precision .
PhD C: Yeah . Yeah .
Grad B: Right .
PhD C: Very complex , uh Yeah .
Professor D: Ye - Pretty much . Yeah .
Grad B: It was all in software and they and you could pick out an individual beam and listen to it .
PhD A: That is cool .
Professor D: Yeah .
Grad B: It was yeah , it was interesting .
PhD C: Yeah .
Professor D: But , the reason why I haven't focused on that as the fir my first concern is because um , I 'm interested in what happens for people , random people out in some random place where they 're p having an impromptu discussion . And you can't just always go , " well , let 's go to this heavily instrumented room that we spent tens of thousands of dollars to se to set up " .
PhD C: Yeah .
PhD A: No , what you need to do is you 'd have a little fabric thing that you unroll and hang on a wall .
PhD C: Yeah .
PhD A: It has all these mikes and it has a plug - in jack to the PDA .
Postdoc E: Interesting .
Grad B: But I think Professor D: The other thing actually , that gets at this a little bit of something else I 'd like to do , is what happens if you have two P D
Grad B: Yep .
PhD C: Yeah .
Professor D: and they communicate with each other ? And then You know , they 're in random positions , the likelihood that I mean , basically there wouldn't be any l likely to be any kind of nulls , if you even had two . If you had three or four it 's Yeah .
PhD A: Ooo !