A: Mm - hmm .
F: um and he 's going t show me the scripts he has for um  running recognition on mee Meeting Recorder digits .
A: Mm - hmm .
F: Uh I also um  haven't got the code yet ,
F: I haven't asked Hynek for for the for his code yet .
F: Cuz I looked at uh Avendano 's thesis and I don't really understand what he 's doing yet
F: but it  it it sounded like um the channel normalization part um of his thesis um was done in a a bit of I don't know what the word is , a a bit of a rough way
F: um it sounded like he um
F: he he it it wasn't really fleshed out
F: and maybe he did something that was interesting for the test situation
F: but I I 'm not sure if it 's what I 'd wanna use
F: so I have to I have to read it more ,
F: I don't really understand what he 's doing yet .
A: OK .
D: It 's my
A: Yeah I haven't read it in a while so I 'm not gonna be too much help unless I read it again ,
D: I know this is mine here .
C: Oh yeah ?
A: so .
A: OK .
A: Um . The um A: so you , and then you 're also gonna be doing this echo cancelling between the the close mounted and the  and the the the what we 're calling a cheating experiment uh of sorts
F: Uh I I 'm ho
A: between the distant F: Right .
F: Well  or I 'm hoping I 'm hoping Espen will do it .
F: Um
A: Ah !
A: OK .
A: F um
F: u
A: Delegate .
A: That 's good .
A: It 's good to delegate .
F: I I think he 's at least planning to do it for the cl close - mike cross - talk
F: and so maybe I can just take whatever setup he has and use it .
A: Great .
A: Great .
A: Yeah actually um he should uh
A: I wonder who else is
A: I think maybe it 's Dan Ellis is going to be doing uh a different cancellation .
A: Um . One of the things that people working in the meeting task wanna get at is they would like to have cleaner close - miked recordings .
A: So uh this is especially true for the lapel
A: but even for the close close - miked uh cases
A: um we 'd like to be able to have um other sounds from other people and so forth removed from A: So when someone isn't speaking you 'd like the part where they 're not speaking to actually be A: So what they 're talking about doing is using ec uh echo cancellation - like techniques .
A: It 's not really echo but uh just um uh
A: taking the input from other mikes and using uh uh a uh  an adaptive filtering approach to remove the effect of that uh other speech .
A: So .
A: Um what was it ,
A: there was there was some some some point where eh uh Eric or somebody was was speaking and he had lots of silence in his channel
A: and I was saying something to somebody else uh which was in the background
A: and it was not A: it was recognizing my words , which were the background speech on the close  close mike .
F: Hmm .
B: Oh the What we talked about yesterday ?
A: Yes .
B: Yeah that was actually my B: I was wearing the I was wearing the lapel and you were sitting next to me ,
A: Oh you A: it was you I was
A: Yeah .
B: and I only said one thing but you were talking and it was picking up all your words .
A: Yeah .
A: Yeah .
A: So they would like clean channels .
A: Uh and for that mmm uh that purpose uh they 'd like to pull it out .
A: So I think  I think Dan Ellis or somebody who was working with him was going to uh work on that .
A: So .
A: OK .
A: Right ?
A: Um . And uh I don't know if we 've talked lately about the the plans you 're developing that we talked about this morning
A: uh I don't remember if we talked about that last week or not ,
A: but maybe just a quick reprise of of what we were saying this morning .
E: OK .
E: Um . <comment> So continuing to um extend
A: Uh .
B: What about the stuff that um Mirjam has been doing ?
B: And and S Shawn , yeah .
B: Oh .
B: So they 're training up nets to try to recognize these acoustic features ?
B: I see .
A: But that 's uh uh all that 's is a a certainly relevant <comment> uh study
A: and , you know , what are the features that they 're finding .
A: We have this problem with the overloading of the term " feature "
A: so
B: Yeah .
A: uh what are the variables ,
A: what we 're calling this one ,
A: what are the variables that they 're found finding useful
C: Hmm .
A: um
B: And their their targets are based on canonical mappings of phones to acoustic f features .
A: for A: Right .
A: And that 's certainly one thing to do and we 're gonna try and do something more f more fine than that
A: but uh
A: um
A: so
A: um
A: So I guess you know what , I was trying to remember some of the things we were saying ,
A: do you ha still have that ?
E: Oh yeah .
A: Yeah .
A: There 's those  that
A: uh
A: yeah , some of some of the issues we were talking about was in j just getting a good handle on on uh what " good features " are
A: and B: What does what did um Larry Saul use for it was the sonorant uh detector ,
B: right ?
B: How did he B: H how did he do that ?
B: Wh - what was his detector ?
B: Mm - hmm .
B: Mm - hmm .
B: Oh , OK .
B: Mm - hmm .
B: So how did he combine all these features ?
B: What what r mmm classifier did he
B: Hmm .
B: Oh right .
B: You were talking about that , yeah .
B: I see .
A: And the other thing you were talking about is is is where we get the targets from .
A: So I mean , there 's these issues of what are the what are the variables that you use
A: and do you combine them using the soft " AND - OR " or you do something , you know , more complicated
A: um and then the other thing was so where do you get the targets from ?
A: The initial thing is just the obvious that we 're discussing is starting up with phone labels from somewhere and then uh doing the transformation .
A: But then the other thing is to do something better
A: and eh w why don't you tell us again about this this database ?
A: This is the B: Hmm !
A: And then tell them to talk naturally ?
A: Yeah , yeah .
B: Pierced tongues and
B: Yeah .
B: You could just mount it to that and they wouldn't even notice .
B: Weld it .
B: Zzz .
A: Maybe you could go to these parlors and and you could , you know you know have have , you know , reduced rates if you  if you can do the measurements .
B: Yeah .
B: I
B: That 's right .
B: You could what you could do is you could sell little rings and stuff with embedded you know , transmitters in them and things
A: Yeah .
B: and
A: Yeah , be cool and help science .
B: Yeah .
A: OK .
B: Hmm !
B: There 's a bunch of data that l around ,
B: that people have done studies like that w way way back
B: right ?
B: I mean I can't remember where uh Wisconsin or someplace that used to have a big database of B: Yeah .
B: I remember there was this guy at A T - andT ,
B: Randolph ?
B: or r What was his name ?
B: Do you remember that guy ?
B: Um ,  researcher at A T - andT a while back that was studying , trying to do speech recognition from these kinds of features .
B: I can't remember what his name was .
B: Dang .
B: Now I 'll think of it .
A: Do you mean eh but you I mean Mar
B: That 's interesting .
C: Well he was the guy the guy that was using A: you mean when was was Mark Randolph there , or ?
B: Mark Randolph .
A: Yeah he 's he 's he 's at Motorola now .
B: Oh is he ?
B: Oh OK .
A: Yeah .
A: Yeah .
B: Yeah .
C: Is it the guy that was using the pattern of pressure on the tongue
C: or ?
B: I can't remember exactly what he was using , now .
B: But I know I just remember it had to do with you know uh positional parameters
C: What C: Yeah .
C: Mm - hmm .
B: and trying to m you know do speech recognition based on them .
A: Yeah .
A: So the only the only uh hesitation I had about it since , I mean I haven't see the data is it sounds like it 's it 's continuous variables and a bunch of them .
B: Hmm .
A: And so
A: I don't know how complicated it is to go from there A: What you really want are these binary labels , and just a few of them .
A: And maybe there 's a trivial mapping if you wanna do it
A: and it 's e but it A: I I I worry a little bit that this is a research project in itself ,
A: whereas um if you did something instead that like um having some manual annotation by uh you know , linguistics students ,
A: this would there 'd be a limited s set of things that you could do a as per our discussions with with John before
B: Mm - hmm .
A: but the things that you could do , like nasality and voicing and a couple other things you probably could do reasonably well .
B: Mm - hmm .
A: And then there would it would really be uh this uh uh binary variable .
A: Course then , that 's the other question is do you want binary variables .
A: So .
A: I mean the other thing you could do is boot trying to to uh get those binary variables
A: and take the continuous variables from uh the uh uh the data itself there ,
A: but I I 'm not sure