G: Yeah .
B: So .
G: Um , on the recognition side , actually , um , I think the main person doing that is , uh , Harriet from Harriet Nock from , uh formerly of Cambridge .
C: Yeah .
G: Um , and apparently they get reasonable results
G: except in some portions they get very high insertion rates G: even higher than we get with with the , um  like even , you know , on lapel mikes with , uh , background speech .
G: And so they were trying to track that down ,
G: and G: It could be that our recognizer 's actually doing relatively well
G: because it has a reject model for , you know , mismatched speech , essentially ,
G: or for unspecified speech .
G: And , um , so C: Oh !
G: so , uh , they 'll I sent them our recognition output
G: so they can sort of do a line - by - line comparison
G: and see if if they , um  you know , their insertions correspond to our rejects and stuff like that .
G: So , um , we 'll see what that , uh , leads to .
G: Um .
G: Yeah . Other than that , uh ,
G: we 're Well , that 's pretty m pretty much it as far as meetings are concerned .
C: Yeah , I mean we 're doing , uh C: So I should mention , Dave and I have sort of been pushing through thinking in terms of an ASRU paper .
C: But I 'm still G: Mm - hmm .
C: I think early next week we 'll decide whether we 'll  we 'll do one or not .
C: Um . But because , you know , the results are are still confusing enough that  that , um ,
C: we we gotta be convinced we understand what 's going on .
C: But it 's starting to look like it it 's almost the opposite of the usual th situation where , um , you get some really nice result with , uh  with digits , say ,
C: and then you go to a large corpus and the result goes away .
C: Um , we had what looked like a good result in digits but th but then when we went to more training data that kind of went away .
G: Mm - hmm .
C: So , uh , the digits result right now is kind of equivocal .
G: Mm - hmm .
C: Um , but the conversational speech one , while not huge , is in sort of Hub - five or Meeting kind of terms ,
C: actually it 's it 's it 's not bad .
C: You know ?
C: So  So it 's it 's you know , it 's like ,
C: i i w
G: Right .
C: after he did some adjustments , more like a percent or so that that i th absolute , that it goes  goes down
F: Yeah . It 's a P H
C: and G: But is it still true that basically the s speaker adaptation , um , sort of negates much of the advantage ?
C: Sometimes . Yeah .
C: No , I 'm talking about with the adaptation .
G: With it .
C: Without the adapt without the adaptation is a much bigger effect .
G: OK .
C: So it so it 's it 's very reasonable to think of as an alternative way of getting this kind o kind of improvement .
G: Mm - hmm .
C: But I think what w ultimately what we have to face with it is that , um C: I mean , he gets incredibly good results if you artificially reverberate clean you know , close close - miked data .
F: Right .
C: So I think the thing we really have to face is that our model for what 's going on is is wrong
F: Is it wrong ?
G: Right .
C: or incomplete ,
C: and that in this situation in particular there 's a lot of noise .
G: Right .
C: And noise plus reverberation is not at all the same as just reverberation .
C: So , um , uh , there 's that one thing that @ @ <comment> been thinking of doing , is there 's a whole lot of work that 's been going on in noise suppression in the in the Aurora , uh , team .
C: And so , uh , they 've got some software
C: and I 'm think of of just having us integrate that in ,
C: uh , cuz it F: Just combine it see .
C: Yeah . Cuz it just I mean , it just subtracts it out .
C: In fact in fact you can run it in sort of enhancement mode where you get speech out
C: and and and , uh , then just run it through the rest of your recognizer .
G: Mm - hmm .
C: So So , uh um .
G: Yeah .
F: Neat .
C: So we 're working on that .
G: OK .
G: There 's some work , um , that G: Do you remember this paper or poster <comment> by , um , some CMU folks at , uh , HLT ?
G: They were talking about G: and and they s they referred to an upcoming ICASSP paper , I think , at the time .
G: Some - someone in Karlsruhe , I think , um , worked on , uh , es you know , estimating noise from the silent regions
G: and then , uh , doing some explicit G: I don't know if it 's something akin to parallel model combination or something like that to to , uh ,
C: I mean , people have done that for a long time .
C: Right ?
G: Right . Anyway , uh , so I I I couldn't judge whether this was original or not ,
G: but but it seemed like they got pretty good results on their meeting data .
G: So we might want to look into that .
C: Yeah , I don I don't remember the paper .
G: Right .
C: But I mean , that th that 's certainly one of the common techniques is to do that .
G: Right .
C: Uh , and in all th the stuff that our team is doing they certainly look at regions that you think are nonspeech in order to get noise statistics .
G: Mm - hmm .
C: But , uh ,
G: Right .
C: I mean , there 's a host of thing different things you could do .
C: In the Aurora thing , we have we have a little bit of a handicap in that they they don't let you , uh , adjust the tis <comment> the statistical models .
C: So y you have to you have to do everything at the feature level .
G: Mm - hmm .
C: Um . But , um , um C: but there 's , uh , you know , Wiener filtering and spectral subtraction
C: which are , uh , sort of i can be looked at as m minor modifications of one another .
C: Um .
C: And they they 've now built up a nice piece of software that does C: u um C: has this sort of general framework for a for it
C: so that you can adjust it to sort of an any any any one of a dozen ways that people have done this kind of thing
C: and
G: Mm - hmm .
C: they found a particular set of parameters they liked this week .
C: And we 're running with that for a while .
C: But .
G: OK .
C: Anything else ?
B: So . Well , I 'll just really briefly , um B: So , I 've been working with Don and also  and and Andreas on the this paper on prosody ,
B: and what we 're trying to do is predict where people at B: given all information up to whatever point in time you 're considering , try to predict if that 's a good location for someone else to jump in .
B: So , that 's the idea of that paper .
B: And that 's the same task that Mari , a um , a student named Dustin , and also Sarah who was at this last meeting will be looking into doing from the language modeling side .
B: Maybe some kind of clu you know , clustered class N - gram or something .
C: Uh - huh .
B: Um . And also we 're we 're using a couple of the labellers who are doing emotion to help us , um , finalize some transcripts for this .
B: So these undergra undergrad students are really helping us out a lot ,
H: Yeah .
B: under Don 's supervision , actually .
B: So that 's good .
H: Right .
B: So these offices are being , uh , very busy B: Don 's and the one across from him .
B: And then also , um , working with Jeremy on Communicator emotion labelling .
B: And most of that up to a few weeks ago was just getting the people labelling these utterances to a computer for emotion
B: and that 's going pretty well now .
C: What kind of emotions do you label ?
B: Um .
B: Well , we 're mostly looking for places where the person 's frustrated with the system .
C: So , there 's just two categories , frustrated and not ,
C: or ? No .
B: There there are actually that 's how we started it .
C: Yeah .
B: And we had different levels .
B: Now we have things like " amuse " .
B: Like , " oh , you finally got it " .
B: And  um , and " disappointed - tired " . Like , " yeah , no , that 's wrong again " .
B: Which isn't really mad ,
B: but , you know .
C: Yeah .
B: So th they gave me a lot of feedback . The labellers , after doing this ,
C: Uh - huh .
B: told me what they wanted as categories .
B: So . <comment> And then there 's jokes .
B: And C: Yeah .
B: Yeah , right .
B: Um .
B: There 's not a lot of enough frustration for us to really go through quickly ,
B: cuz we have to label every meeting
B: and also things like repeats for the same information , and so forth .
B: And I 've been coordinating a little bit with Katrin at , uh , UW who was doing user correction work that she presented at the at the meeting .
B: So there 's some overlap there in what you m
B: there 's a correlation between corrections and annoyance or frustration .
C: Yeah .
B: Um , but Jeremy 's been starting to really work on that project now from the acoustic side .
B: So we have all these waveforms ,
B: and he 's been with Morgan 's help and , uh , Dan Ellis 's , I think , looking into spectral tilt a bit ,
B: so he can talk about that .