B: But a thousand events in twelve minutes , that 's D: Yeah , but D: Yeah .
C: But that can include taps .
E: Uh . Yeah .
D: But B: Well , but a thousand taps in eight minutes is a l in twelve minutes is a lot .
D: General .
C: Actually D: I I con I consider I consider acoustic events eh , the silent too .
B: Silent .
G: Silence starting or silence ending D: Yeah , silent , ground
D: to bec to detect eh because I consider acoustic event all the things are not eh speech .
C: Oh , OK .
E: Mm - hmm .
A: Oh .
D: In ge in in in a general point of view .
C: Oh .
E: OK , so how many of those thousand were silence ?
C: Alright .
F: Not speech not speech or too much speech .
D: in the per
D: Too much speech .
E: Right .
E: So how many of those thousand were silence , silent sections ?
D: Yeah .
D: Uh silent ,
D: I I I I don't I I haven't the D: eh I I would like to to do a stylistic study
E: Yeah .
D: and give you eh with the report eh from eh the the study from the the the session one session .
E: Yeah .
E: Yeah .
D: And I I found that eh another thing .
D: When eh eh I w I  I was eh look at eh nnn , the difference speech file ,
D: um , for example , eh if eh we use the ehm the mixed file , to to transcribe , the the events and the words , I I saw that eh the eh speech signal , collected by the eh this kind of mike eh of this kind of mike , eh are different from the eh mixed signal eh , we eh collected by headphone .
G: Yep .
G: Right .
D: And It 's right .
E: Yeah .
D: But the problem is the following .
D: The the the D: I I I knew that eh the signal eh , eh would be different ,
D: but eh the the problem is eh , eh we eh detected eh difference events in the speech file eh collected by by that mike uh qui compared with the mixed file .
D: And so if when you transcribe eh only eh using the nnn the mixed file , it 's possible eh if you use the transcription to evaluate a different system ,
D: it 's possible you eh in the eh
D: i and you use the eh speech file collected by the eh fet mike , to eh to nnn to do the experiments with the the system ,
E: Mm - hmm .
G: Right .
D: its possible to evaluate eh , eh or to consider eh acoustic events that which you marked eh in the mixed file , but eh they don't appear in the eh speech signal eh collected by the by the mike .
G: Right .
G: The the reason that I generated the mixed file was for IBM to do word level transcription , not speech event transcription .
D: Yeah .
D: Yeah . Oh , it 's a good idea .
D: It 's a good idea I think .
G: So I agree that if someone wants to do speech event transcription , that the mixed signals here D: Yeah .
G: I mean , if I 'm tapping on the table , you it 's not gonna show up on any of the mikes , but it 's gonna show up rather loudly in the PZM .
D: Yeah .
D: Yeah .
D: Yeah .
D: So and I I  I say eh that eh , eh , or this eh only because eh I c I I  in my opinion , it 's necessary to eh to eh to put the transcription on the speech file , collected by the objective signal .
G: So .
D: I mean the the the signal collected by the eh , the real mike in the future , in the prototype to to eh correct the initial eh segmentation eh with the eh real speech
E: Mm - hmm .
E: The the the far - field ,
E: yeah .
D: you have to to analyze D: you have to to process .
D: Because I I found a difference .
E: Yeah , well , just I mean , just in that that one s ten second , or whatever it was , example that Adam had that that we we passed on to others a few months ago ,
E: there was that business where I g I guess it was Adam and Jane were talking at the same time
E: and and uh , in the close - talking mikes you couldn't hear the overlap , and in the distant mike you could .
E: So yeah , it 's clear that if you wanna study if you wanna find all the places where there were overlap , it 's probably better to use a distant mike .
F: That 's good .
E: On the other hand , there 's other phenomena that are going on at the same time for which it might be useful to look at the close - talking mikes ,
D: Yeah .
E: so it 's C: But why can't you use the combination of the close - talking mikes , time aligned ?
G: If you use the combination of the close - talking mikes , you would hear Jane interrupting me ,
G: but you wouldn't hear the paper rustling .
G: And so if you 're interested in C: I I mean if you 're interested in speakers overlapping other speakers and not the other kinds of nonspeech , that 's not a problem ,
E: Some <comment> of it 's masking masked .
D: Yeah .
A: Were you interrupting him or was he interrupting you ?
E: Right .
G: Right .
C: right ?
G: Although the other issue is that the mixed close - talking mikes D: Yeah .
G: I mean , I 'm doing weird normalizations and things like that .
C: But it 's known .
D: Yeah .
G: Yep .
C: I mean , the normalization you do is over the whole conversation
G: Right .
C: isn't it ,
C: over the whole meeting .
G: Yep .
C: So if you wanted to study people overlapping people , that 's not a problem .
D: I I I think eh
D: I saw the nnn the eh
D: but eh I eh I have eh any results .
D: I I I saw the the speech file collected by eh the fet mike ,
D: and eh eh signal eh to eh to noise eh relation is eh low .
E: Mm - hmm .
D: It 's low .
D: It 's very low . You would comp if we compare it with eh the headphone .
G: Yep .
D: And
D: I I found that nnn that eh , ehm , pr probably ,
G: Did Did you
D: I 'm not sure eh by the moment ,
D: but it 's it 's probably that eh a lot of eh , eh
D: for example , in the overlapping zone , on eh in in several eh parts of the files where you you can find eh , eh eh , smooth eh eh speech eh from eh one eh eh talker in the in the meeting ,
E: Mm - hmm .
E: Mm - hmm .
D: it 's probably in in that eh in in those files you you can not find you can not process
D: because eh it 's confused with with noise .
E: Mm - hmm .
D: And there are a lot of
D: I think .
D: But I have to study with more detail .
D: But eh my idea is to to process only nnn , this eh nnn , this kind of s of eh speech .
D: Because I think it 's more realistic .
D: I 'm not sure it 's a good idea , but eh G: Well , it 's more realistic but it 'll it 'll be a lot harder .
E: No i
E: Well , it 'd be hard , but on the other hand as you point out , if your if i if if your concern is to get uh the overlapping people people 's speech , you will you will get that somewhat better .
D: Yeah .
D: Mm - hmm . Yeah .
E: Um , Are you making any use E: uh you were you were working with th the data that had already been transcribed .
E: Does it uh D: With By Jane .
E: Yes .
D: Yeah .
E: Now um did you make any use of that ?
E: See I was wondering cuz we st we have these ten hours of other stuff that is not yet transcribed .
D: Yeah .
D: Yeah .
E: Do you D: The the transcription by Jane , t eh i eh , I I I want to use to to nnn , eh to put D: i i it 's a reference for me .
D: But eh the transcription eh for example , I I don't I I 'm not interested in the in the in the words , transcription words , eh transcribed eh eh in eh follow in the  in the in the speech file , but eh eh Jane eh for example eh put a mark eh at the beginning eh of each eh talker ,
D: in the in the meeting ,
D: um eh she she nnn includes information about the zone where eh there are eh there is an overlapping zone .
E: Mm - hmm .
D: But eh there isn't any any mark , time temporal mark , to to c eh to mmm  e - heh , to label <comment> the beginning and the end of the of the
E: OK .
E: Right , so she is D: ta I 'm I I I think eh we need this information to
E: Right .
E: So the twelve you you it took you twelve hours of course this included maybe some some time where you were learning about what what you wanted to do ,