Professor A: it has has many people  who sound in many different ways . So . Um . I mean . OK . That was that topic . What else we got ?
PhD E: Um .
Professor A: Did we end up giving up on on , any Eurospeech submissions ,
PhD E: But Professor A: or ? I know Thilo and Dan Ellis are are submitting something , but uh .
PhD E: Yeah . I  I guess e the only thing with these the Meeting Recorder and , well , So , I think , yeah I think we basically gave up .
Professor A: Um . Now , actually for the for the Aur - uh
PhD E: But Professor A: we do have stuff for Aurora , right ? Because because we have ano an extra month or something .
PhD E: Yeah . Yeah . Yeah . So . Yeah , for sure we will do something for the special session .
Professor A: Yeah . Well , that 's fine . So th so so we have a couple a couple little things on Meeting Recorder
PhD E: Yeah . Mm - hmm .
Professor A: and we have  We don't we don't have to flood it with papers . We 're not trying to prove anything to anybody . so . That 's fine . Um . Anything else ?
PhD E: Yeah . Well . So . Perhaps the point is that we 've been working on is , yeah , we have put the um the good VAD in the system and it really makes a huge difference . Um . So , yeah . I think , yeah , this is perhaps one of the reason why our system was not  not the best , because with the new VAD , it 's very the results are similar to the France Telecom results and perhaps even better sometimes .
Professor A: Hmm .
Grad B: Huh .
PhD E: Um . So there is this point . Uh . The problem is that it 's very big and  we still have to think how to where to put it and  um ,
Professor A: Mm - hmm .
PhD E: because it it well , this VAD uh either some delay and we if we put it on the server side , it doesn't work , because on the server side features you already have LDA applied from the f from the terminal side and so you accumulate the delay so the VAD should be before the LDA which means perhaps on the terminal side and then smaller and
Professor A: So wha where did this good VAD come from ?
PhD E: So . It 's um from OGI . So it 's the network trained it 's the network with the huge amounts on hidden of hidden units , and um nine input frames compared to the VAD that was in the proposal which has a very small amount of hidden units and fewer inputs .
Professor A: This is the one they had originally ?
PhD E: Yeah .
Professor A: Oh . Yeah , but they had to get rid of it because of the space , didn't they ?
PhD E: Yeah . So . Yeah . But the abso assumption is that we will be able to make a VAD that 's small and that works fine . And . So we can Professor A: Well . So that 's a problem . Yeah .
PhD E: Yeah but nnn .
Professor A: But the other thing is uh to use a different VAD entirely . I mean , uh i if if there 's a if if I I don't know what the thinking was amongst the the the the ETSI folk but um if everybody agreed sure let 's use this VAD and take that out of there PhD E: Mm - hmm . Mm - hmm . They just want , apparently they don't want to fix the VAD because they think there is some interaction between feature extraction and and VAD or frame dropping But they still want to just to give some um requirement for this VAD because it 's it will not be part of they don't want it to be part of the standard .
Professor A: OK .
PhD E: So . So it must be at least uh somewhat fixed but not completely . So there just will be some requirements that are still not uh not yet uh ready I think .
Professor A: Determined . I see . But I was thinking that that uh s " Sure , there may be some interaction ,
PhD E: Nnn .
Professor A: but I don't think we need to be stuck on using our or OGI 's VAD . We could use somebody else 's if it 's smaller or PhD E: Yeah .
Professor A: You know , as long as it did the job .
PhD E: Mm - hmm .
Professor A: So that 's good .
PhD E: Uh . So there is this thing . There is um Yeah . Uh I designed a new a new filter because when I designed other filters with shorter delay from the LDA filters , there was one filter with fif sixty millisecond delay and the other with ten milliseconds
Professor A: Right .
PhD E: and uh Hynek suggested that both could have sixty - five sixty - s I think it 's sixty - five .
Professor A: Yeah .
PhD E: Yeah . Both should have sixty - five because Professor A: You didn't gain anything , right ?
PhD E: Yeah . And . So I did that and uh it 's running . So , let 's see what will happen . Uh but the filter is of course closer to the reference filter .
Professor A: Mm - hmm .
PhD E: Mmm . Um . Yeah . I think Professor A: So that means logically , in principle , it should be better . So probably it 'll be worse .
PhD E: Yeah
Professor A: Or in the basic perverse nature uh of reality . Yeah . OK .
PhD E: Yeah . Sure .
Grad C: Yeah .
Professor A: OK .
PhD E: Yeah , and then we 've started to work with this of um voiced - unvoiced stuff .
Professor A: Mm - hmm .
PhD E: And next week I think we will perhaps try to have um a new system with uh uh MSG stream also see what what happens . So , something that 's similar to the proposal too , but with MSG stream .
Professor A: Mm - hmm . Mm - hmm .
PhD E: Mmm .
Professor A: OK .
PhD D: No , I w I begin to play with Matlab and to found some parameter robust for voiced - unvoiced decision . But only to play . 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 .
Professor A: Uh - huh .
PhD D: And , well , is more or less robust . Is good for clean speech . Is quite good for noisy speech .
Professor A: Huh ? Mm - hmm .
PhD D: but um we must to have bigger statistic with TIMIT ,
Professor A: Mm - hmm .
PhD D: and is not ready yet to use on ,
Professor A: Yeah .
PhD D: well , I don't know .
Professor A: Yeah .
PhD E: Yeah . So , basically we wa want to look at something like the ex the ex excitation signal and Professor A: Right .
PhD D: Mm - hmm .
PhD E: which are the variance of it and PhD D: I have here . I have here for one signal , for one frame .
PhD E: Mmm .
Professor A: Yeah . Uh - huh .
PhD D: The the mix of the two , noise and unnoise , and the signal is this . Clean , and this noise .
Professor A: Uh .
PhD D: These are the two the mixed , the big signal is for clean .
Professor A: Well , I 'm s uh There 's None of these axes are labeled , so I don't know what this What 's this axis ?
PhD D: Uh this is uh this axis is nnn , " frame " .
Professor A: Frame .
PhD D: Mm - hmm .
Professor A: And what 's th what this ?
PhD D: Uh , this is uh energy , log - energy of the spectrum . Of the this is the variance , the difference {nonvocalsound} between the spectrum of the signal and FFT of each frame of the signal and this mouth spectrum of time after the f may fit for the two ,
Professor A: For this one . For the noi
PhD D: this big , to here , they are to signal . This is for clean and this is for noise .
Professor A: Oh . There 's two things on the same graph .
PhD D: Yeah . I don't know . I I think that I have d another graph , but I 'm not sure .
Professor A: So w which is clean and which is noise ?
PhD E: Yeah . I think the lower one is noise .
PhD D: The lower is noise and the height is clean .
Professor A: OK . So it 's harder to distinguish
PhD D: It 's height .
Professor A: but it but it g
PhD E: Yeah .
Professor A: with noise of course but but PhD D: Oh . I must to have .
Professor A: Uh .
PhD D: Pity , but I don't have two different
Professor A: And presumably when there 's a a PhD E: So this should the the the t voiced portions .
Professor A: Uh - huh .
PhD D: Yeah , it is the height is voiced portion .
PhD E: The p the peaks should be voiced portion .
PhD D: And this is the noise portion .
Professor A: Uh - huh .
PhD D: And this is more or less like this . But I meant to have see @ @ two two the picture .
Professor A: Yeah . Yeah .
PhD D: This is , for example , for one frame .
Professor A: Yeah
PhD D: the the spectrum of the signal . And this is the small version of the spectrum after ML mel filter bank .
Professor A: Yeah . And this is the difference ?
PhD D: And this is I don't know . This is not the different . This is trying to obtain with LPC model the spectrum but using Matlab without going factor and s
Professor A: No pre - emphasis ? Yeah .
PhD D: Not pre - emphasis . Nothing .
Professor A: Yeah so it 's doesn't do too well there .
PhD D: And the I think that this is good . This is quite similar . this is  this is another frame . ho how I obtained the envelope , {nonvocalsound} this envelope , with the mel filter bank .
Professor A: Right . So now I wonder I mean , do you want to I know you want to get at something orthogonal from what you get with the smooth spectrum Um . But if you were to really try and get a voiced - unvoiced , do you do you want to totally ignore that ? I mean , do you do you I mean , clearly a a very big very big cues for voiced - unvoiced come from uh spectral slope and so on , right ?
PhD E: Mm - hmm .
Professor A: Um .
PhD E: Yeah . Well , this would be this would be perhaps an additional parameter ,
Professor A: Yeah .
PhD E: simply isn't Professor A: I see .
PhD E: Yeah .
PhD D: Yeah because when did noise clear {nonvocalsound} in these section is clear
PhD E: Uh .
Professor A: Mm - hmm .
PhD D: if s @ @ {nonvocalsound} val value is indicative that is a voice frame and it 's low values
Professor A: Yeah . Yeah . Well , you probably want I mean , certainly if you want to do good voiced - unvoiced detection , you need a few features . Each each feature is by itself not enough . But , you know , people look at at slope and uh first auto - correlation coefficient , divided by power .
PhD E: Mmm .
Professor A: Or or uh um there 's uh I guess we prob probably don't have enough computation to do a simple pitch detector or something ? I mean with a pitch detector you could have a  have a an estimate of of what the PhD E: Mmm .
Professor A: Uh . Or maybe you could you just do it going through the P FFT 's figuring out some um probable um harmonic structure . Right . And and uh .
PhD E: Mmm .
PhD D: you have read up and you have a paper , the paper that you s give me yesterday . they say that yesterday they are some {nonvocalsound} problem
PhD E: Oh , yeah . But Yeah , but it 's not it 's , yeah , it 's it 's another problem .
PhD D: and the Is another problem .
PhD E: Yeah Um . Yeah , there is th this fact actually . If you look at this um spectrum ,
Professor A: Yeah .
PhD E: What 's this again ? Is it the mel - filters ?
PhD D: Yeah like this . Of kind like this .
PhD E: Yeah . OK . So the envelope here is the output of the mel - filters
Professor A: Mm - hmm .
PhD E: and what we clearly see is that in some cases , and it clearly appears here , and the the harmonics are resolved by the f Well , there are still appear after mel - filtering ,
Professor A: Mm - hmm .
PhD E: and it happens for high pitched voice because the width of the lower frequency mel - filters is sometimes even smaller than the pitch .
Professor A: Yeah .
PhD E: It 's around one hundred , one hundred and fifty hertz Nnn .
Professor A: Right .
PhD E: And so what happens is that this uh , add additional variability to this envelope and  um
Professor A: Yeah .
PhD E: so we were thinking to modify the mel - spectrum to have something that that 's smoother on low frequencies .
Professor A: That 's as as a separate thing .
PhD E: i
Professor A: Yeah .
PhD E: Yeah . This is a separate thing .
Professor A: Separate thing ?
PhD D: Yeah .
Professor A: Yeah .
PhD E: And .
Professor A: Yeah . Maybe so . Um . Yeah . So , what Yeah . What I was talking about was just , starting with the FFT you could you could uh do a very rough thing to estimate estimate uh pitch .
PhD E: Yeah . Mm - hmm .
Professor 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 .
PhD E: Mm - hmm .
Professor A: Uh . It 's uh a variant on what you 're s what you 're doing . The I mean , the the the mel does give a smooth thing . But as you say it 's not that smooth here . 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 and and our first kilohertz , even .
PhD E: Mm - hmm .
Professor A: And um if was uh noisy , the proportion that it would go down would be if it was if it was unvoiced or something .
PhD E: Mm - hmm .
Professor A: So you oughta be able to pick out voiced segments . At least it should be another another cue . So . Anyway .
PhD E: Mm - hmm .