A: So every mean every all all of the the parameters that you have , especially the variances , are going to be affected by the overall variance .
B: Oh , OK .
B: Uh - huh .
A: And so , in principle , you if you remove that source , then , you know , you can B: I see . OK .
B: So would the major effect is that you 're gonna get is by normalizing the means ,
B: but it may help A: That 's the first order but thing ,
B: First - order effects .
B: And it may help to do the variance .
A: but then the second order is is the variances
B: OK .
B: OK .
A: because , again , if you if you 're trying to distinguish between E and B
B: Mm - hmm .
A: if it just so happens that the E 's were a more you know , were recorded when when the energy was was was larger or something ,
B: Mm - hmm . Mm - hmm .
A: or the variation in it was larger , uh than with the B 's , then this will be give you some some bias .
A: So the  it 's removing these sources of variability in the data that have nothing to do with the linguistic component .
B: OK .
F: Mmm .
B: Gotcha . OK .
B: Sorry to interrupt .
F: Yep .
A: But the the uh but let me as ask ask you something .
F: And it and this A: i is if If you have a good voice activity detector , isn't isn't it gonna pull that out ?
F: Yeah . Sure .
F: If they are good .
F: Yeah .
F: Well what it it shows is that , yeah , perhaps a good voice activity detector is is good before on - line normalization
F: and that 's what uh we 've already observed .
F: But uh ,
F: yeah ,
F: voice activity detection is not  an easy thing neither .
B: But after you do this , after you do the variance normalization F: Mm - hmm .
B: I mean .
B: I don't know , it seems like this would be a lot easier than this signal to work with .
F: Yeah .
F: So . What I notice is that , while I prefer to look at the second figure than at the third one ,
F: well , because you clearly see where speech is .
B: Yeah .
A: Yeah .
F: But the problem is that on the speech portion , channel zero and channel one are more different than when you use variance normalization where channel zero and channel one become closer .
A: Right .
B: But for the purposes of finding the speech F: And F: Yeah , but here F: Yeah .
B: You 're more interested in the difference between the speech and the nonspeech ,
F: Yeah .
B: right ?
F: So I think ,
F: yeah .
F: For I th I think that it perhaps it shows that uh the parameters that the voice activity detector should use uh have to use should be different than the parameter that have to be used for speech recognition .
A: Yeah . So basically you want to reduce this effect .
F: Well , y
A: So you can do that by doing the voi voice activity detection .
A: You also could do it by spect uh spectral subtraction before the variance normalization ,
A: right ?
F: Yeah , but it 's not clear ,
F: yeah .
A: So uh F: We
F: So .
F: Well . It 's just to
A: Yeah .
F: the the number that at that are here are recognition experiments on Italian HM and MM with these two kinds of parameters .
F: And , well , it 's better with variance normalization .
A: Yeah .
A: Yeah .
A: So it does get better even though it looks ugly .
F: Uh A: OK .
A: but does this have the voice activity detection in it ?
F: Yeah .
A: OK .
F: Um .
E: OK .
A: So .
B: Where 's th
F: But the fact is that the voice activity detector doesn't work on channel one .
F: So .
F: Yeah .
A: Uh - huh .
B: Where at what stage is the voice activity detector applied ?
B: Is it applied here or a after the variance normalization ?
F: Hmm ?
A: Spectral subtraction , I guess .
F: It 's applied before variance normalization .
B: or F: So it 's a good thing ,
F: because I guess voice activity detection on this should could be worse .
B: Oh .
B: Yeah .
B: Is it applied all the way back here ?
F: It 's applied the
F: um on ,
F: yeah ,
F: something like this ,
F: yeah .
B: Maybe that 's why it doesn't work for channel one .
F: Perhaps , yeah .
A: Can I F: So we could perhaps do just mean normalization before VAD .
B: Mm - hmm .
A: Mm - hmm .
A: Can I ask a , I mean a sort of top - level question ,
A: which is um " if if most of what the OGI folk are working with is trying to integrate this other other uh spectral subtraction , why are we worrying about it ? "
F: Mm - hmm .
F: About ?
F: Spectral subtraction ?
A: Yeah .
F: It 's just uh Well it 's another F: They are trying to u to use the um  the Ericsson
F: and we 're trying to use something something else .
F: And .
F: Yeah , and also to understand what happens because
A: OK .
F: uh fff
F: Well . When we do spectral subtraction , actually , I think that this is the the two last figures .
A: Yeah .
F: Um . It seems that after spectral subtraction , speech is more emerging now uh than than before .
A: Mm - hmm .
B: Speech is more what ?
F: Well , the difference between the energy of the speech and the energy of the n spectral subtrac subtracted noise portion is is larger .
A: Mm - hmm .
F: Well , if you compare the first figure to this one F: Actually the scale is not the same ,
F: but if you look at the the numbers um you clearly see that the difference between the C - zero of the speech and C - zero of the noise portion is larger .
F: Uh but what happens is that after spectral subtraction , you also increase the variance of this of C - zero .
F: And so if you apply variance normalization on this , it completely sc screw everything .
A: Mm - hmm .
F: Well .
A: Mm - hmm .
F: Um . Uh . Yeah .
F: So yeah .
F: And what they did at OGI is just uh they don't use on - line normalization , for the moment , on spectral subtraction
F: and I think F: Yeah .
F: I think as soon as they will try on - line normalization there will be a problem .
F: So yeah , we 're working on the same thing but I think
F: uh
F: with different different system
F: and A: Right .
A: I mean , i the
A: Intellectually it 's interesting to work on things th uh one way or the other
F: Mm - hmm .
A: but I 'm I 'm just wondering if um  on the list of things that there are to do ,
A: if there are things that we won't do because we 've got two groups doing the same thing .
F: Mm - hmm .
A: Um .
F: Mm - hmm .
A: That 's A: Um .
A: Just just asking .
A: Uh . I mean , it 's F: Yeah , well ,
F: uh .
B: There also could be I mean . I can maybe see a reason f for both working on it too
B: if um you know , if if if you work on something else and and you 're waiting for them to give you spectral subtraction B: I mean it 's hard to know whether the effects that you get from the other experiments you do will carry over once you then bring in their spectral subtraction module .
B: So it 's it 's almost like everything 's held up waiting for this one thing .
B: I don't know if that 's true or not ,
B: but I could see how F: Mmm .
B: Maybe that 's what you were thinking .
A: I don't know .
A: I don't know . I mean , we still evidently have a latency reduction plan which which isn't quite what you 'd like it to be .
A: That that seems like one prominent thing .
A: And then uh weren't issues of of having a a second stream or something ?
A: That was A: Was it A: There was this business that , you know , we we could use up the full forty - eight hundred bits ,
A: and F: Yeah . But I think they '
F: I think we want to work on this .
F: They also want to work on this ,
F: so .
F: Uh . yeah .
F: We we will try MSG ,
F: but um ,
F: yeah .
F: And they are t
F: I think they want to work on the second stream also ,
F: but more with some kind of multi - band
F: or , well , what they call TRAP or generalized TRAP .
A: Mm - hmm .
F: Um . So .
A: OK .
A: Do you remember when the next meeting is supposed to be ?
F: It 's uh in June .
A: the next uh A: In June .
A: OK .
F: Yeah .
A: Yeah .
A: Um . Yeah , the other thing is that you saw that that mail about uh the VAD V A Ds performing quite differently ?
A: That that
A: uh
A: So um .
A: This there was this experiment of uh " what if we just take the baseline ? "
F: Mmm .
A: set uh of features ,
A: just mel cepstra ,
A: and you inc incorporate the different V A
A: And it looks like the the French VAD is actually uh better significantly better .
B: Improves the baseline ?
A: Yeah .
A: Yeah .
F: Yeah but I don't know which VAD they use .
F: Uh . If the use the small VAD
F: I th I think it 's on F: I think it 's easy to do better
F: because it doesn't work at all .
F: So .
F: I I don't know which which one .
F: It 's Pratibha that that did this experiment .
D: Yeah .
F: Um . We should ask which VAD she used .
D: I don't @ @ .
D: He Actually , I think that he say with the good VAD of from OGI
D: and with the Alcatel VAD .
D: And the experiment was sometime better , sometime worse .
F: Yeah but I it 's uh I think you were talking about the other mail that used VAD on the reference features .
A: Yes .
F: Yeah .
D: I don't remember .
A: And on that one , uh the French one is was better .
A: It was just better .
D: Mm - hmm .
A: I mean it was enough better that that it would uh account for a fair amount of the difference between our performance , actually .
F: Mm - hmm .
D: Mm - hmm .
A: So . Uh .
A: So if they have a better one , we should use it .
A: I mean .
A: You know ?
A: it 's A: you can't work on everything .
F: Yeah .
A: Uh . Uh . Yeah .
F: Yeah , so we should find out if it 's really better .
F: I mean if it F: the compared to the small or the big network .
D: Mm - hmm .
A: Yeah .
F: And perhaps we can easily improve if if we put like mean normalization before the before the VAD .
F: Because  as as you 've mentioned .
A: Yeah .
F: Mmm .
A: H Hynek will be back in town uh the week after next ,
A: back back in the country .
A: So .
A: And start start organizing uh more visits and connections and so forth ,
F: Mm - hmm .
A: and A: uh
A: working towards June .
F: Yeah .
F: Mm - hmm .
D: Also is Stephane was thinking that maybe it was useful to f to think about uh voiced - unvoiced D: to work uh here in voiced - unvoiced detection .
F: Yeah .
D: And we are looking  in the uh signal .
F: Yeah .
F: Yeah , my feeling is that um actually when we look at all the proposals , ev everybody is still using some kind of spectral envelope
F: and um it 's A: Right .
A: No use of pitch uh basically .
A: Yeah .
F: Yeah , well , not pitch ,
F: but to look at the um fine at the at the high re high resolution spectrum .
A: Yeah .
F: So . We don't necessarily want to find the the pitch of the of the sound
A: Well , it F: but
F: uh F: Cuz I have a feeling that when we look when we look at the just at the envelope there is no way you can tell if it 's voiced and unvoiced ,
F: if there is some F: It 's it 's easy in clean speech because voiced sound are more low frequency
F: and .
F: So there would be more ,
A: Yeah .
F: uh there is the first formant , which is the larger
F: and then voiced sound are more high frequencies
F: cuz it 's frication
F: and A: Right .
F: But ,
F: yeah .
F: When you have noise there is no
F: um  if if you have a low frequency noise it could be taken for for voiced speech
F: and .
A: Yeah , you can make these mistakes ,
F: So .
A: but but B: Isn't there some other
F: S
B: uh d
F: So I think that it it would be good F: Yeah , yeah ,
F: well , go go on .
B: Uh , I was just gonna say isn't there  aren't aren't there lots of ideas for doing voice activity , or speech - nonspeech rather , <comment> um by looking at um , you know , uh I guess harmonics
B: or looking across time A: Well , I think he was talking about the voiced - unvoiced , though ,
F: Mmm .
A: right ?
B: Yeah .
A: So , not the speech - nonspeech .
B: Well even with e
B: uh w ah
A: Yeah .
B: you know , uh even with the voiced - non voiced - unvoiced
F: Mmm .
B: um B: I thought that you or somebody was talking about A: Well .
F: So .
A: Uh yeah . B We should let him finish what he w he was gonna say ,
B: OK .
A: and B: So go ahead .
F: Um yeah , so yeah , I think if we try to develop a second stream
F: well , there would be one stream that is the envelope and the second , it could be interesting to have
F: that 's something that 's more related to the fine structure of the spectrum .
F: And .
F: Yeah , so I don't know .
F: We were thinking about like using ideas from from Larry Saul ,
F: have a good voice detector ,
F: have a good ,
F: well , voiced - speech detector , that 's working on on the FFT
F: and uh
F: Larry Saul could be an idea .
A: U
F: We were are thinking about just kind of uh taking the spectrum
F: and computing the variance of of the high resolution spectrum and things like this .
A: So u s u
A: OK .
A: So So many tell you something about that .
A: Uh we had a guy here some years ago who did some work on um making use of voicing information uh to help in reducing the noise .
F: Yeah ?
F: Mm - hmm .
A: So what he was doing is basically y you  you do estimate the pitch .
A: And
A: um
A: you from that you you estimate A: or you estimate fine harmonic structure ,
A: whichev
A: ei either way ,
A: it 's more or less the same .
A: But uh the thing is that um you then can get rid of things that are not A: i if there is strong harmonic structure , you can throw away stuff that 's that 's non - harmonic .
F: Mm - hmm .
F: Mm - hmm .
A: And that that is another way of getting rid of part of the noise
F: Yeah .
F: Yeah .
A: So um that 's something that is sort of finer ,
A: brings in a little more information than just spectral subtraction .
A: Um .
F: Mm - hmm .
A: And he had some I mean , he did that sort of in combination with RASTA .
A: It was kind of like RASTA was taking care of convolutional stuff
F: Mmm .
F: Mm - hmm .
A: and he was A: and and got some some decent results doing that .
A: So that that 's another another way .
F: Yeah .
A: But yeah , there 's there 's F: Mmm .
A: Right .
A: There 's all these cues .
A: We 've
F: But A: actually back when Chuck was here we did some voiced - unvoiced uh classification using a bunch of these ,
A: and and
A: uh
A: works OK .
A: Obviously it 's not perfect
A: but
F: Mm - hmm .
A: um A: But the thing is that you can't A: given the constraints of this task , we can't , in a very nice way , feed forward to the recognizer the information A: the probabilistic information that you might get about whether it 's voiced or unvoiced ,
F: Mm - hmm .
A: where w we can't you know affect the  the uh distributions or anything .
A: But we what we
A: uh A: I guess we could
A: Yeah .
B: Didn't the head dude send around that message ?
B: Yeah , I think you sent us all a copy of the message ,
B: where he was saying that B: I I 'm not sure , exactly , what the gist of what he was saying ,
B: but something having to do with the voice activity detector
B: and that it will  that people shouldn't put their own in or something .
B: It was gonna be a A: That But A: OK .
A: So that 's voice activity detector as opposed to voicing detector .
F: They didn't .
A: So we 're talking about something a little different .
F: Mmm .
B: Oh , I 'm sorry .
F: Mmm .
B: I I missed that .
A: Right ?
A: I guess what you could do , maybe
A: this would be w useful , if if you have if you view the second stream ,
A: yeah , before you before you do KLT 's and so forth ,
A: if you do view it as probabilities ,
A: and if it 's an independent A: So , if it 's if it 's uh not so much envelope - based by fine - structure - based ,
A: uh looking at harmonicity or something like that ,
A: um if you get a probability from that information
A: and then multiply it by you know , multiply by all the voiced outputs and all the unvoiced outputs ,
A: you know , then use that as the
F: Mm - hmm .
A: uh take the log of that
A: or uh pre pre uh pre - nonlinearity ,
F: Yeah .
F: i if A: uh and do the KLT on the on on that ,
F: Yeah .
A: then that would that would I guess be uh a reasonable use of independent information .
A: So maybe that 's what you meant .