C: Eh C: Ah !
D: Which category do you put that in ?
E: Yeah , that 's right . That 's my question .
C: Yeah .
C: Yeah , he here I I put eh speech from eh , from , eh , one speaker without , eh , eh , any any any events more .
E: Oh !
D: Right , so where do you put speech from one speaker that does have a nonspeech event at the same time ?
D: Which catege which category ?
E: Like a c
C: Where ? Where What is the class ?
C: No . By the moment , no .
E: Oh .
B: Yeah , yeah , that 's what he was saying before .
E: So you don't i i it 's not in that D: Oh , so you not not marked .
C: For for the by the @ @ no , @ @ because I I I I want to limit the the nnn , the the study .
D: OK .
D: Got it .
D: Fine . So so B: Yeah , so that 's what he was saying before , is that he excluded those .
A: So you 're not using all of the data .
C: The All I Exactly .
B: Yeah .
D: Yeah .
C: Yeah , you mean E: So you 're ignoring overlapping events unless they 're speech with speech .
C: Yeah , be
D: Yeah , that 's fine .
C: Yeah .
E: OK .
C: " Why ?
C: Why ?
C: What 's the reason ? "
C: because i it 's the first study .
D: Oh , no no , it 's a perfectly sensible way to go .
C: the first
E: We 're just
D: We just wondered trying to understand what what you were doing .
C: Yeah .
E: Yeah .
E: Yeah cuz you 've talked about other overlapping events in the past .
D: OK .
C: Yeah .
E: So , this is this is a subset .
C: Yeah .
C: In the in the future , the the idea is to to extend the class ,
A: Is is C: to consider all the all the information , you you mentioned before
D: Yeah . Yeah , I I don't think we were asking for that .
E: OK .
D: We were jus just trying to understand E: Yeah .
C: but eh , the the first idea Because eh , I don't know what hap what will happen <comment> with the study .
E: Yeah , we just wanted to know what the category was here .
D: Yeah .
B: Right .
D: Sure .
C: Yeah .
A: Is your silence category pure silence , or ?
A: What if there was a door - slam or something ?
C: i it 's pure C: No , no , it 's pure silence .
A: Pure silence .
C: It 's the control set .
A: OK .
C: OK ?
C: It 's the control set .
D: What you C: It 's pure si pure silence <comment> with the with the machine on the on the roof .
D: Well D: w I I think what you m I think what you mean is that it 's nonspeech segments that don't have impulsive noises .
B: With the fan .
C: Yeah .
D: Right ?
D: Cuz you 're calling what you 're calling " event " is somebody coughing or clicking , or rustling paper , or hitting something , which are impulsive noises .
C: Yeah .
D: But steady - state noises are part of the background .
C: Yeah .
D: Which , are being , included in that .
D: Right ?
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 .
D: Yeah .
E: So it 's like a signal - noise situation .
E: Yeah .
D: Well D: Yeah .
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 .
E: Well , steady state .
D: Right , it 's I mean , it 's " Background " might be might be a better word than " silence " .
C: Yeah .
D: It 's just sort of that the the background acoustic C: Yeah .
D: Yeah .
C: Yeah .
B: Right . So Fine . Go on .
C: Is is is only D: Yeah .
C: OK .
E: Well , we needed to get the categories , yeah .
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 C: Sorry .
C: And eh I am going to prepare a test bed , eh , well , eh , a a set of feature structure eh , eh , models .
B: Right .
C: And my idea is
B: " Tone " , whatever .
C: so so on because I have a pitch extractor yet .
D: Right .
B: Mm - hmm .
C: I have to to test , but eh I A: You have your own ?
C: Yeah , yeah , yeah .
A: Oh !
C: I ha I have prepare .
C: Is a modified version of of of a pitch tracker , eh , from , eh , Standar - eh Stanford University C: in Stanford ?
C: No .
C: From , eh , em , Cambridge University .
A: Oh !
A: What 's it written in ?
C: Eh , em , I I I don't remember what is the the name of the of the author ,
C: 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 .
A: Ah .
D: Mm - hmm . Mm - hmm .
C: And And I have to because , in general the pitch tracker , doesn't work <comment> very well and B: Bad .
B: Right .
B: But , you know , as a feature , it might be OK .
C: Yeah . Yeah .
B: So , we don't know .
C: 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 .
C: 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
B: Supervised clustering .
B: Mm - hmm .
C: This is the the eh , first idea
E: Mm - hmm .
C: and the second is try to eh , to use some ideas eh , similar to the linear discriminant analysis .
B: Mm - hmm .
C: Eh ?
C: 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 .
B: Mm - hmm .
B: What sort of classifier ar ?
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 .
C: Is Yeah ? W with a model , is is only to cluster using a eh , @ @ or a similarity .
B: Unimodal ?
E: Mm - hmm .
B: So is it just one cluster per C: A another possibility it to use eh a netw netw a neural network .
B: Right .
C: But eh what 's the p What is my idea ?
C: What 's the problem I I I I see in in in  if you you use the the neural network ?
C: 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 ,
B: Right , you can't analyse it .
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 .
D: Well , actually , you can do sensitivity analyses which show you what the importance of the different parce pieces of the input are .
C: Yeah .
D: It 's hard to w w what you It 's hard to tell on a neural net is what 's going on internally .
C: Yeah .
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 .
C: Yeah .
C: Yeah .
D: Um , but B: Well , using something simpler first I think is probably fine .
D: Well , this isn't tru if if if you really wonder what different if if