E: So , something that 's similar to the proposal too , but with MSG stream .
A: Mm - hmm .
A: Mm - hmm .
E: Mmm .
A: OK .
D: No , I w I begin to play with Matlab and to found some parameter robust for voiced - unvoiced decision .
D: But only to play .
D: And we  they we found that maybe w is a classical parameter , the sq the variance between the um FFT of the signal and the small spectrum of time we after the um mel filter bank .
A: Uh - huh .
D: And , well , is more or less robust .
D: Is good for clean speech .
D: Is quite good for noisy speech .
A: Huh ?
A: Mm - hmm .
D: but um we must to have bigger statistic with TIMIT ,
A: Mm - hmm .
D: and is not ready yet to use on ,
A: Yeah .
D: well , I don't know .
A: Yeah .
E: Yeah .
E: So , basically we wa want to look at something like the ex the ex excitation signal and A: Right .
D: Mm - hmm .
E: which are the variance of it and D: I have here .
D: I have here for one signal , for one frame .
E: Mmm .
A: Yeah .
D: The the mix of the two , noise and unnoise , and the signal is this .
A: Uh - huh .
D: Clean , and this noise .
A: Uh .
D: These are the two D: the mixed , the big signal is for clean .
A: Well , I 'm s uh A: There 's A: None of these axes are labeled ,
A: so I don't know what this A: What 's this axis ?
D: Uh this is uh this axis is nnn , " frame " .
A: Frame .
D: Mm - hmm .
A: And what 's th what this ?
D: Uh , this is uh energy , log - energy of the spectrum .
D: Of the
D: this is the variance ,
D: the difference <nonvocalsound> between the spectrum of the signal and FFT of each frame of the signal
D: and this mouth spectrum of time after the f may fit for the two ,
A: For this one .
A: For the noi
D: this big , to here , they are to signal .
D: This is for clean and this is for noise .
A: Oh .
A: There 's two things on the same graph .
D: Yeah .
D: I don't know .
D: I I think that I have d another graph ,
D: but I 'm not sure .
E: Yeah .
A: So w which is clean and which is noise ?
E: I think the lower one is noise .
D: The lower is noise and the height is clean .
A: OK .
A: So it 's harder to distinguish
D: It 's height .
A: but it but it g
E: Yeah .
A: with noise of course
A: but but D: Oh . I must to have .
A: Uh .
D: Pity , but I don't have two different
A: And presumably when there 's a a E: So this should the the the t voiced portions .
A: Uh - huh .
D: Yeah , it is the height is voiced portion .
E: The p the peaks should be voiced portion .
D: And this is the noise portion .
A: Uh - huh .
D: And this is more or less like this .
D: But I meant to have see @ @ two two the picture .
A: Yeah .
A: Yeah .
D: This is , for example , for one frame .
A: Yeah
D: the the spectrum of the signal .
D: And this is the small version of the spectrum after ML mel filter bank .
A: Yeah .
A: And this is the difference ?
D: And this is
D: I don't know .
D: This is not the different .
D: This is trying to obtain with LPC model the spectrum
D: but using Matlab without going factor and s
A: No pre - emphasis ?
A: Yeah .
D: Not pre - emphasis .
D: Nothing .
A: Yeah so it 's D: And the A: doesn't do too well there .
D: I think that this is good .
D: This is quite similar .
D: this is  this is another frame .
D: ho how I obtained the envelope , <nonvocalsound> this envelope , with the mel filter bank .
A: Right .
A: So now I wonder A: I mean , do you want to A: I know you want to get at something orthogonal from what you get with the smooth spectrum
A: Um . But if you were to really try and get a voiced - unvoiced , do you do you want to totally ignore that ?
A: I mean , do you do you A: I mean , clearly a a very big very big cues for voiced - unvoiced come from uh spectral slope and so on ,
E: Mm - hmm .
A: right ?
A: Um .
E: Yeah .
E: Well , this would be this would be perhaps an additional parameter ,
E: simply isn't A: Yeah .
A: I see .
E: Yeah .
D: Yeah because when did noise clear <nonvocalsound> in these section is clear
E: Uh .
A: Mm - hmm .
D: if s @ @ <nonvocalsound> val value is indicative that is a voice frame and it 's low values
A: Yeah .
A: Yeah .
A: Well , you probably want A: I mean , certainly if you want to do good voiced - unvoiced detection , you need a few features .
A: Each each feature is by itself not enough .
A: But , you know , people look at at slope and uh first auto - correlation coefficient , divided by power .
E: Mmm .
A: Or or uh
A: um
A: there 's uh A: I guess we prob probably don't have enough computation to do a simple pitch detector or something ?
A: I mean with a pitch detector you could have a  have a an estimate of of what the E: Mmm .
A: Uh .
A: Or maybe you could you just do it going through the P FFT 's figuring out some um probable um harmonic structure .
A: Right .
E: Mmm .
A: And and uh .
D: you have read up and you have a paper , the paper that you s give me yesterday .
E: Oh , yeah .
D: they say that yesterday they are some <nonvocalsound> problem
E: But E: Yeah , but it 's not E: it 's ,
E: yeah , it 's it 's another problem .
D: and the E: Yeah
D: Is another problem .
E: Um .
E: Yeah , there is th this fact actually .
E: If you look at this um spectrum ,
A: Yeah .
E: What 's this again ?
E: Is it the mel - filters ?
D: Yeah like this .
E: Yeah .
D: Of kind like this .
E: OK .
E: So the envelope here is the output of the mel - filters
A: Mm - hmm .
E: and what we clearly see is that in some cases ,
E: and it clearly appears here ,
E: and the the harmonics are resolved by the f
E: Well , there are still appear after mel - filtering ,
A: Mm - hmm .
E: and it happens for high pitched voice because the width of the lower frequency mel - filters is sometimes even smaller than the pitch .
A: Yeah .
E: It 's around one hundred , one hundred and fifty hertz Nnn .
A: Right .
E: And so what happens is that this uh , add additional variability to this envelope
E: and  um
A: Yeah .
E: so we were thinking to modify the mel - spectrum to have something that that 's smoother on low frequencies .
A: That 's as as a separate thing .
E: i
A: Yeah .
E: Yeah .
E: This is a separate thing .
D: Yeah .
A: Separate thing ?
A: Yeah .
E: And .
A: Yeah .
A: Maybe so .
A: Um .
A: Yeah .
A: So , what A: Yeah .
A: What I was talking about was just , starting with the FFT
A: you could you could uh do a very rough thing to estimate estimate uh pitch .
E: Yeah .
E: Mm - hmm .
A: And uh uh , given you know , given that , uh you could uh uh come up with some kind of estimate of how much of the low frequency energy was was explained by  by uh uh those harmonics .
E: Mm - hmm .
A: Uh .
A: It 's uh a variant on what you 're s what you 're doing .
A: The I mean , the the the mel does give a smooth thing .
A: But as you say it 's not that smooth here .
A: And and so if you  if you just you know subtracted off uh your guess of the harmonics then something like this would end up with quite a bit lower energy in the first fifteen hundred hertz or so
A: and and our first kilohertz , even .
E: Mm - hmm .
A: And um if was uh noisy , the proportion that it would go down would be
E: Mm - hmm .
A: if it was if it was unvoiced or something .
A: So you oughta be able to pick out voiced segments .
A: At least it should be another another cue .
E: Mm - hmm .
A: So . Anyway .
A: OK ?
A: That 's what 's going on .
A: Uh .
A: What 's up with you ?
B: Um our t I went to talk with uh Mike Jordan this this week
A: Mm - hmm .
B: um <nonvocalsound> and uh shared with him the ideas about um extending the Larry Saul work
B: and um I asked him some questions about factorial H M
B: 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
B: 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
B: 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
B: there they have a limited number of parameters and they focus directly on  on uh the sub - problems at hand
B: so you can imagine um five or so parallel um features um transitioning independently
B: 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 .
A: Hmm .
B: So he he seemed he seemed like really interested in  in um in this
B: and said said this is this is something very do - able and can learn a lot
B: and um
B: yeah , I 've just been continue reading um about certain things .
A: Mm - hmm .
B: um
B: thinking of maybe using um um m modulation spectrum stuff to um as features um also in the in the sub - bands
A: Mm - hmm .
B: because it seems like the modulation um spectrum tells you a lot about the intelligibility of of certain um words and stuff
B: So , um .
B: Yeah .
B: Just
B: that 's about it .
A: OK .
C: OK .
C: And
C: um
C: so I 've been looking at Avendano 's work
C: and um
C: uh I 'll try to write up in my next stat status report a nice description of what he 's doing ,
C: but it 's it 's an approach to deal with reverberation or that the aspect of his work that I 'm interested in
C: the idea is that um   normally an analysis frames are um too short to encompass reverberation effects um in full .
C: You miss most of the reverberation tail in a ten millisecond window
C: and so  you you 'd like it to be that um the reverberation responses um simply convolved um in ,
C: but it 's not really with these ten millisecond frames
C: cuz you j
C: But if you take , say , a two millisecond um window C: I 'm sorry
C: a two second window
C: then in a room like this , most of the reverberation response is included in the window
C: and the then it um then things are l more linear .
C: It is it is more like the reverberation response is simply c convolved
C: and um  and you can use channel normalization techniques like
C: uh in his thesis he 's assuming that the reverberation response is fixed .
C: He just does um mean subtraction ,
C: which is like removing the DC component of the modulation spectrum
C: and that 's supposed to d um deal uh deal pretty well with the um reverberation
C: and um the neat thing is you can't take these two second frames and feed them to a speech recognizer
C: 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 .
C: So I don't really know the theory .
C: I guess it 's these are called " time frequency representations "
C: and h he 's making the the time sh um finer grained and the frequency resolution um less fine grained .
E: Mm - hmm .
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
C: cuz he doesn't have code for me .
C: So that that 'll take some reading about the theory .
C: I don't really know the theory .
E: Mm - hmm .
C: Oh , and um , another f first step is
C: um ,
C: so the the way I want to extend his work is make it able to deal with a time varying reverberation response
C: um and um
C: we don't really know how fast the um the reverberation response is varying the Meeting Recorder data
C: um so um we we have this um block least squares um imp echo canceller implementation
C: 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
C: and looking at the echo canceller taps and then see how fast that varies from block to block .
E: Mm - hmm .
C: That should give an idea of how fast the reverberation response is changing .
E: Mm - hmm .
A: OK .
A: Um . I think we 're sort of done .
E: Yeah .
A: So let 's read our digits and go home .
C: Um .
C: S so um
C: y you do I think you read some of the the zeros as O 's and some as zeros .
A: Yeah .
C: Is there a particular way we 're supposed to read them ?
E: There are only zeros here .
E: Well .
A: No . " O " " O " " O "
A: " O " " O " " O " and " zero " are two ways that we say that digit .
E: Eee .
E: Yeah .
A: So it 's E: But B: Ha !
A: so it 's i
E: Perhaps in the sheets there should be another sign for the E: if we want to the the guy to say " O "
E: or
A: No .
A: I mean . I think people will do what they say .
E: It 's E: Yeah .
A: It 's OK .
E: OK .
C: Alright .
A: I mean in digit recognition we 've done before , you have you have two pronunciations for that value , " O " and " zero " .
C: OK .
E: But it 's perhaps more difficult for the people to prepare the database then , if E: because here you only have zeros
A: No , they just write E: and and people pronounce " O " or zero A: they they write down OH .
A: or they write down ZERO
A: a and they and they each have their own pronunciation .
E: Yeah but if the sh the sheet was prepared with a different sign for the " O " .
A: But people wouldn't know what that wa
A: I mean there is no convention for it .
E: OK .
E: Yeah .
E: OK .
A: See . I mean , you 'd have to tell them " OK when we write this , say it tha " ,
A: you know ,
A: and you just They just want people to read the digits as you ordinarily would
E: Mm - hmm .
E: Yeah .
E: Yep .
A: and and people say it different ways .
C: OK .
C: Is this a change from the last batch of of um forms ?
C: Because in the last batch it was spelled out which one you should read .
E: Yeah , it was orthographic ,
E: so .
A: Yes .
A: That 's right .
A: It was it was spelled out , and they decided they wanted to get at more the way people would really say things .
C: Oh . OK .
A: That 's also why they 're they 're bunched together in these different groups .
C: OK .
A: So so it 's A: Yeah .
A: So it 's it 's A: Everything 's fine .
C: OK .
A: OK .
A: 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 .
A: But I realized that that um when I 'm talking on the phone , certainly , and and saying these numbers , I almost always say zero .
A: And uh cuz because uh i it 's two syllables .
A: It 's it 's more likely they 'll understand what I said .
A: So that that that 's the habit I 'm in ,
A: but some people say " O " and B: Yeah I normally say " O " cuz it 's easier to say .
A: Yeah it 's shorter .
A: Yeah .
A: So it 's So . So uh .
B: " O "
A: Now , don't think about it .
B: Oh , no !
A: OK .
A: We 're done .