Professor A: OK ? That 's what 's going on . Uh . What 's up with you ?
Grad B: Um our t I went to talk with uh Mike Jordan this this week
Professor A: Mm - hmm .
Grad B: um {nonvocalsound} and uh shared with him the ideas about um extending the Larry Saul work and um I asked him some questions about factorial H M so like later down the line when we 've come up with these these feature detectors , how do we  how do we uh you know , uh model the time series that that happens um  and and we talked a little bit about factorial H M Ms and how um when you 're doing inference or w when you 're doing recognition , there 's like simple Viterbi stuff that you can do for  for these H M and the uh  the great advantages that um a lot of times the factorial H M Ms don't um don't over - alert the problem there they have a limited number of parameters and they focus directly on  on uh the sub - problems at hand so you can imagine um five or so parallel um features um transitioning independently and then at the end you you uh couple these factorial H M Ms with uh  with uh undirected links um based on  based on some more data .
Professor A: Hmm .
Grad B: So he he seemed he seemed like really interested in  in um in this and said said this is this is something very do - able and can learn a lot and um yeah , I 've just been continue reading um about certain things .
Professor A: Mm - hmm .
Grad B: um thinking of maybe using um um m modulation spectrum stuff to um as features um also in the in the sub - bands
Professor A: Mm - hmm .
Grad B: because it seems like the modulation um spectrum tells you a lot about the intelligibility of of certain um words and stuff So , um . Yeah . Just that 's about it .
Professor A: OK .
Grad C: OK . And um so I 've been looking at Avendano 's work and um uh I 'll try to write up in my next stat status report a nice description of what he 's doing , but it 's it 's an approach to deal with reverberation or that the aspect of his work that I 'm interested in the idea is that um   normally an analysis frames are um too short to encompass reverberation effects um in full . You miss most of the reverberation tail in a ten millisecond window and so  you you 'd like it to be that um the reverberation responses um simply convolved um in , but it 's not really with these ten millisecond frames cuz you j But if you take , say , a two millisecond um window I 'm sorry a two second window then in a room like this , most of the reverberation response is included in the window and the then it um then things are l more linear . It is it is more like the reverberation response is simply c convolved and um  and you can use channel normalization techniques like uh in his thesis he 's assuming that the reverberation response is fixed . He just does um mean subtraction , which is like removing the DC component of the modulation spectrum and that 's supposed to d um deal uh deal pretty well with the um reverberation and um the neat thing is you can't take these two second frames and feed them to a speech recognizer um so he does this um method training trading the um the spectral resolution for time resolution and um come ca uh synthesizes a new representation which is with say ten second frames but a lower s um frequency resolution . So I don't really know the theory . I guess it 's these are called " time frequency representations " and h he 's making the the time sh um finer grained and the frequency resolution um less fine grained .
PhD E: Mm - hmm .
Grad C: s so I 'm I guess my first stab actually in continuing his work is to um re - implement this this thing which um changes the time and frequency resolutions cuz he doesn't have code for me . So that that 'll take some reading about the theory . I don't really know the theory .
PhD E: Mm - hmm .
Grad C: Oh , and um , another f first step is um , so the the way I want to extend his work is make it able to deal with a time varying reverberation response um and um we don't really know how fast the um the reverberation response is varying the Meeting Recorder data um so um we we have this um block least squares um imp echo canceller implementation and um I want to try finding the the response , say , between a near mike and the table mike for someone using the echo canceller and looking at the echo canceller taps and then see how fast that varies from block to block .
PhD E: Mm - hmm .
Grad C: That should give an idea of how fast the reverberation response is changing .
PhD E: Mm - hmm .
Professor A: OK . Um . I think we 're sort of done .
PhD E: Yeah .
Professor A: So let 's read our digits and go home .
Grad C: Um . S so um y you do I think you read some of the the zeros as O 's and some as zeros .
Professor A: Yeah .
Grad C: Is there a particular way we 're supposed to read them ?
PhD E: There are only zeros here . Well .
Professor A: No . " O " " O " " O " " O " " O " " O " and " zero " are two ways that we say that digit .
PhD E: Eee . Yeah .
Professor A: So it 's Grad B: Ha !
PhD E: But Professor A: so it 's i
PhD E: Perhaps in the sheets there should be another sign for the if we want to the the guy to say " O " or
Professor A: No . I mean . I think people will do what they say .
PhD E: It 's Professor A: It 's OK .
PhD E: Yeah .
Professor A: I mean in digit recognition we 've done before , you have you have two pronunciations for that value , " O " and " zero " .
Grad C: Alright .
PhD E: OK .
Grad C: OK .
PhD E: But it 's perhaps more difficult for the people to prepare the database then , if because here you only have zeros
Professor A: No , they just write PhD E: and and people pronounce " O " or zero Professor A: they they write down OH . or they write down ZERO a and they and they each have their own pronunciation .
PhD E: Yeah but if the sh the sheet was prepared with a different sign for the " O " .
Professor A: But people wouldn't know what that wa I mean there is no convention for it .
PhD E: OK . Yeah .
Professor A: See . I mean , you 'd have to tell them " OK when we write this , say it tha " ,
PhD E: OK .
Professor A: you know , and you just They just want people to read the digits as you ordinarily would
PhD E: Mm - hmm . Yeah .
Professor A: and and people say it different ways .
PhD E: Yep .
Grad C: OK . Is this a change from the last batch of of um forms ? Because in the last batch it was spelled out which one you should read .
PhD E: Yeah , it was orthographic , so .
Professor A: Yes . That 's right . It was it was spelled out , and they decided they wanted to get at more the way people would really say things .
Grad C: Oh . OK .
Professor A: That 's also why they 're they 're bunched together in these different groups . So so it 's Grad C: OK .
Professor A: Yeah . So it 's it 's Everything 's fine .
Grad C: OK .
Professor A: OK . Actually , let me just s since since you brought it up , I was just it was hard not to be self - conscious about that when it after we since we just discussed it . But I realized that that um when I 'm talking on the phone , certainly , and and saying these numbers , I almost always say zero . And uh cuz because uh i it 's two syllables . It 's it 's more likely they 'll understand what I said . So that that that 's the habit I 'm in , but some people say " O " and Grad B: Yeah I normally say " O " cuz it 's easier to say .
Professor A: Yeah it 's shorter . Yeah . So it 's So . So uh .
Grad B: " O "
Professor A: Now , don't think about it .
Grad B: Oh , no !
Professor A: OK . We 're done .

Summary: Grad B shared some of his progress on how the modulation spectrum could speak to the intelligibility of different linguistic units. Grad C was working on reverberation and how a model may deal with varying reverberation response. The discussion ended with different pronunciation, "O" and "zero" for the digit zero.