E: and we 're recording those ,
E: uh there 's a network services and applications group here who 's agreed to have their meetings recorded ,
C: Great .
E: and we 're gonna start recording them .
E: They 're They meet on Tuesdays . We 're gonna start recording them next week .
E: So actually , we 're gonna h start having a a pretty significant chunk
E: and
E: so , you know , Adam 's sort of struggling with trying to get things to be less buggy , and come up quicker when they do crash and stuff things like that ,
E: now that uh  the things are starting to happen .
E: So right now , yeah , I th I 'd say the data is predominantly meeting meetings ,
E: but there are scattered other meetings in it and that that amount is gonna grow
E: uh so that the meeting meetings will probably ultimately E: i if we 're if we collect fifty or sixty hours , the meeting meetings it will probably be , you know , twenty or thirty percent of it ,
E: not not not eighty or ninety .
E: But .
C: So there 's probably there 's three to four a week ,
G: That 's what we 're aiming for .
C: that we 're aiming for .
E: Yeah .
C: And they 're each about an hour or something .
E: Yeah , yeah .
G: Although G: Yeah .
G: We 'll find out tomorrow whether we can really do this or not .
C: So E: Yeah and th the the other thing is I 'm not pos
C: OK .
E: I 'm sort of thinking as we 've been through this a few times , that I really don't know E: maybe you wanna do it once for the novelty ,
E: but I don't know if in general we wanna have meetings that we record from outside this group do the digits .
G: Right .
E: Because it 's just an added bunch of weird stuff .
C: Yeah .
E: And , you know , we we h we 're highly motivated .
E: Uh in fact , the morning group is really motivated
E: cuz they 're working on connected digits ,
E: so it 's G: Actually that 's something I wanted to ask ,
G: is I have a bunch of scripts to help with the transcription of the digits .
E: Yeah .
G: We don't have to hand - transcribe the digits because we 're reading them and I have those .
E: Yeah .
G: And so I have some scripts that let you very quickly extract the sections of each utterance .
C: Right .
G: But I haven't been ru I haven't been doing that .
G: Um , if I did that , is someone gonna be working on it ?
E: Uh , yeah , I I think
G: I mean , is it something of interest ?
E: definitely s so Absolutely .
E: Yeah , whoever we have working on the acoustics for the Meeting Recorder are gonna start with that .
G: OK .
G: I mean , I I 'm I 'm interested in it ,
G: I just don't have time to do it now .
F: I was these meetings I 'm sure someone thought of this , but these this uh reading of the numbers would be extremely helpful to do um adaptation .
G: So
G: Yep . Yep .
F: Um .
C: Actually I have o
G: I I would really like someone to do adaptation .
F: Mm - hmm .
G: So if we got someone interested in that , I think it would be great for Meeting Recorder .
E: Well E: I mean , one of the things I wanted to do , uh , that I I talked to to Don about , is one of the possible things he could do or m also , we could have someone else do it , is to do block echo cancellation ,
G: Since it 's the same people over and over .
F: Mm - hmm .
E: to try to get rid of some of the effects of the the the far - field effects .
F: Mm - hmm .
E: Um , I mean we have the party line has been that echo cancellation is not the right way to handle the situation
E: because people move around ,
E: and uh , if if it 's if it 's uh not a simple echo , like a cross - talk kind of echo , but it 's actually room acoustics , it 's it 's it 's you can't really do inversion ,
F: Mm - hmm .
E: and even echo cancellation is going to uh be something It may you Someone may be moving enough that you are not able to adapt quickly
E: and so the tack that we 've taken is more " lets come up with feature approaches and multi - stream approaches and so forth , that will be robust to it for the recognizer and not try to create a clean signal " .
F: Mm - hmm .
E: Uh , that 's the party line .
E: But it occurred to me a few months ago that uh party lines are always , you know , sort of dangerous .
E: It 's good  good to sort of test them , actually .
E: And so we haven't had anybody try to do a good serious job on echo cancellation and we should know how well that can do .
E: So that 's something I 'd like somebody to do at some point , just take these digits , take the far - field mike signal , and the close uh mike signal , and apply really good echo cancellation .
E: Um , there was a have been some nice talks recently by by Lucent on on their b
F: Hmm .
E: the block echo cancellation particularly appealed to me ,
E: uh you know , trying and change it sample by sample , but you have some reasonable sized blocks . <comment> And um , you know , th
A: W what is the um the artifact you try to you 're trying to get rid of when you do that ?
F: Ciao .
E: Uh so it 's it you have a a direct uh E: Uh , what 's the difference in If you were trying to construct a linear filter , that would
E: um F: I 'm signing off .
E: Yeah .
E: that would subtract off <comment> the um uh parts of the signal that were the aspects of the signal that were different between the close - talk and the distant .
E: You know , so so uh um I guess in most echo cancellation E: Yeah , so you Given that um E: Yeah , so you 're trying to So you 'd There 's a a distance between the close and the distant mikes so there 's a time delay there ,
E: and after the time delay , there 's these various reflections .
E: And if you figure out well what 's the E: there 's a a least squares algorithm that adjusts itself adjusts the weight so that you try to subtract essentially to subtract off uh different uh different reflections .
E: Right ? So let 's take the simple case where you just had you had some uh some delay in a satellite connection or something
E: and then there 's a there 's an echo .
E: It comes back . And you want to adjust this filter so that it will maximally reduce the effect of this echo .
A: So that would mean like if you were listening to the data that was recorded on one of those .
A: Uh , just the raw data ,
A: you would you might hear kind of an echo ?
A: And and then this noise cancellation would get
E: Well , I 'm I 'm I 'm saying That 's a simplified version of what 's really happening . <comment> What 's really happening is E: Well , when I 'm talking to you right now , you 're getting the direct sound from my speech ,
E: but you 're also getting , uh , the indirect sound that 's bounced around the room a number of times .
E: OK ?
E: So now , if you um try to r you To completely remove the effect of that is sort of impractical for a number of technical reasons ,
E: but I but E: not to try to completely remove it , that is , invert the the room response ,
E: but just to try to uh uh eliminate some of the the effect of some of the echos .
E: Um , a number of people have done this
E: so that , say , if you 're talking to a speakerphone , uh it makes it more like it would be , if you were talking right up to it .
E: So this is sort of the st the straight - forward approach .
E: You say I I I want to use this uh this item but I want to subtract off various kinds of echos .
E: So you construct a filter , and you have this this filtered version uh of the speech um gets uh uh gets subtracted off from the original speech .
E: Then you try to you try to minimize the energy in some sense .
E: And so um uh with some constraints .
A: Kind of a clean up thing , that E: It 's a clean up thing . Right .
A: OK .
E: So , echo cancelling is is , you know , commonly done in telephony ,
E: and and and it 's sort of the obvious thing to do in this situation if you if , you know , you 're gonna be talking some distance from a mike .
A: When uh , I would have meetings with the folks in Cambridge when I was at BBN over the phone , they had a um some kind of a special speaker phone
A: and when they would first connect me , it would come on and we 'd hear all this noise .
E: Yeah .
A: And then it was uh And then it would come on and it was very clear ,
A: you know .
E: Right . So it 's taking samples , it 's doing adaptation , it 's adjusting weights ,
E: and then it 's getting the sum .
E: So um , uh anyway that 's that 's kind of a reasonable thing that I 'd like to have somebody try E: somebody look And and the digits would be a reasonable thing to do that with .
E: I think that 'd be enough data plenty of data to do that with ,
E: and i for that sort of task you wouldn't care whether it was uh large vocabulary speech or anything .
E: Uh . Um
B: Is Brian Kingsbury 's work related to that ,
B: or is it a different type of reverberation ?
E: Brian 's <comment> Kingsbury 's work is an example of what we did f f from the opposite dogma .
E: Right ?
E: Which is what I was calling the " party line " , which is that uh doing that sort of thing is not really what we want .
E: We want something more flexible , uh i i where people might change their position ,
E: and there might be , you know E: There 's also um
E: oh yeah ,
E: noise .
E: So the echo cancellation does not really allow for noise .
E: It 's if you have a clean situation but you just have some delays ,
E: Then we 'll figure out the right the right set of weights for your taps for your filter in order to produce the effect of those those echos .
E: But um if there 's noise , then the very signal that it 's looking at is corrupted
E: so that it 's decision about what the right you know , right right uh delays are is , uh is right delayed signal is is is uh is incorrect .
E: And so , in a noisy situation , um , also in a in a situation that 's very reverberant <comment> with long reverberation times <comment> and really long delays , it 's it 's sort of typically impractical .