Professor A: Yeah . Anybo - anybody in the in this group do doing anything for Eurospeech ?
PhD F: S
Professor A: Or , is that what is that PhD F: Yeah we are  We are trying to to do something with the Meeting Recorder digits ,
Professor A: Right .
PhD F: and But yeah . Yeah . And the good thing is that there is this first deadline ,
Professor A: Yeah .
PhD F: and , well , some people from OGI are working on a paper for this , but there is also the um special session about th Aurora which is  uh which has an extended deadline . So . The deadline is in May .
Professor A: For uh  Oh , for Eurospeech ?
PhD F: For th Yeah .
Professor A: Oh !
PhD F: So f only for the experiments on Aurora . So it it 's good ,
Professor A: Oh , a special dispensation .
PhD F: yeah .
Professor A: That 's great .
PhD B: Mm - hmm . Where is Eurospeech this year ?
PhD F: It 's in Denmark .
Professor A: Aalborg Aalborg uh
PhD B: Oh .
Professor A: So the deadline When 's the deadline ? When 's the deadline ?
PhD F: Hmm ? I think it 's the thirteenth of May .
Professor A: That 's great ! It 's great . So we should definitely get something in for that .
PhD F: Yeah .
Professor A: But on meeting digits , maybe there 's Maybe .
PhD F: Yeah .
Professor A: Maybe .
PhD F: So it would be for the first deadline .
Professor A: Yeah .
PhD F: Nnn .
Professor A: Yeah . So , I mean , I I think that you could certainly start looking at at the issue uh but but uh I think it 's probably , on s from what Stephane is saying , it 's it 's unlikely to get sort of active participation from the two sides until after they 've PhD B: Well I could at least Well , I 'm going to be out next week but I could try to look into like this uh CVS over the web . That seems to be a very popular way of people distributing changes and over , you know , multiple sites and things
Professor A: Mm - hmm .
PhD B: so maybe if I can figure out how do that easily and then pass the information on to everybody so that it 's you know , as easy to do as possible and and people don't it won't interfere with {comment} their regular work , then maybe that would be good . And I think we could use it for other things around here too . So .
Professor A: Good .
Grad C: That 's cool . And if you 're interested in using CVS , I 've set it up here ,
PhD B: Oh great .
Grad C: so .
PhD B: OK .
Grad C: um j
PhD B: I used it a long time ago but it 's been a while so maybe I can ask you some questions .
Grad C: Oh . So . I 'll be away tomorrow and Monday but I 'll be back on Tuesday or Wednesday .
PhD B: OK .
Professor A: Yeah . Dave , the other thing , actually , is is this business about this wave form . Maybe you and I can talk a little bit at some point about coming up with a better uh demonstration of the effects of reverberation for our web page , cuz uh  the uh um I mean , actually the the uh It made a good good audio demonstration because when we could play that clip the the the really obvious difference is that you can hear two voices and   in the second one and only hear PhD B: Maybe we could just like , talk into a cup .
Professor A: Yeah .
PhD B: Some good reverb .
Professor A: No , I mean , it sound it sounds pretty reverberant , but I mean you can't when you play it back in a room with a you know a big room , nobody can hear that difference really .
Grad C: Yeah .
Professor A: They hear that it 's lower amplitude and they hear there 's a second voice ,
Grad C: Uh - huh .
Professor A: um but uh that actually that makes for a perfectly good demo because that 's a real obvious thing , that you hear two voices .
PhD B: But not of reverberation .
Professor A: Yeah .
Grad C: A boom .
Professor A: Well that that that 's OK . But for the the visual , just , you know , I 'd like to have uh uh , you know , the spectrogram again ,
Grad C: Yeah .
Professor A: because you 're you 're you 're visual uh abilities as a human being are so good you can pick out you know , you you look at the good one , you look at the cru the screwed up one , and and you can see the features in it without trying to @ @ PhD B: I noticed that in the pictures .
Professor A: yeah .
PhD B: I thought " hey , you know th " I My initial thought was " this is not too bad ! "
Professor A: Right . But you have to you know , if you look at it closely , you see " well , here 's a place where this one has a big formant uh uh formant maj major formants here are  are moving quite a bit . " And then you look in the other one and they look practically flat .
PhD B: Mm - hmm .
Professor A: So I mean you could that 's why I was thinking , in a section like that , you could take a look look at just that part of the spectrogram and you could say " Oh yeah . This this really distorted it quite a bit . "
PhD B: Yeah . The main thing that struck me in looking at those two spectrograms was the difference in the high frequencies . It looked like for the one that was farther away , you know , it really everything was attenuated
Professor A: Right .
PhD B: and I mean that was the main visual thing that I noticed .
Professor A: Right . But it 's it 's uh So . Yeah . So there are clearly are spectral effects . Since you 're getting all this indirect energy , then a lot of it does have have uh reduced high frequencies . But um the other thing is the temporal courses of things really are changed , and  and uh we want to show that , in some obvious way . The reason I put the wave forms in there was because uh they they do look quite different . Uh . And so I thought " Oh , this is good . " but I  I just uh After after uh they were put in there I didn't really look at them anymore , cuz I just they were different . So I want something that has a is a more interesting explanation for why they 're different . Um .
Grad C: Oh . So maybe we can just substitute one of these wave forms and um then do some kind of zoom in on the spectrogram on an interesting area .
Professor A: Something like that . Yeah .
Grad C: Uh - huh .
Professor A: The other thing that we had in there that I didn't like was that um the most obvious characteristic of the difference uh when you listen to it is that there 's a second voice , and the the the the the uh cuts that we have there actually don't correspond to the full wave form . It 's just the first I think there was something where he was having some trouble getting so much in , or . I I forget the reason behind it . But it it 's um  it 's the first six seconds or something of it and it 's in the seventh or eighth second or something where @ @ the second voice comes in . So we we would like to actually see the voice coming in , too , I think , since that 's the most obvious thing when you listen to it .
Grad C: Mm - hmm .
Professor A: So . Um .
PhD F: Uh , yeah . Yeah . I brought some I don't know if  some figures here . Well . I start we started to work on spectral subtraction . And um the preliminary results were very bad .
Professor A: Uh - huh .
PhD F: So the thing that we did is just to add spectral subtraction before this , the Wall uh process , which contains LDA on - line normalization . And it hurts uh a lot .
Professor A: Uh - huh .
PhD F: And so we started to look at at um things like this , which is , well , it 's Yeah . So you have the C - zero parameters for one uh Italian utterance .
PhD D: You can @ @ .
PhD F: And I plotted this for two channels . Channel zero is the close mic microphone , and channel one is the distant microphone . And it 's perfectly synchronized , so . And the sentence contain only one word , which is " Due " And it can't clearly be seen . Where where is it ?
Professor A: Uh - huh .
PhD F: Where is the word ?
PhD B: This is this is ,
Grad E: Hmm .
PhD B: oh , a plot of C - zero ,
PhD F: So .
PhD B: the energy .
PhD F: This is a plot of C - zero , uh when we don't use spectral substraction , and when there is no on - line normalization .
Professor A: Mm - hmm .
PhD F: So . There is just some filtering with the LDA and and some downsampling , upsampling .
PhD B: C - zero is the close talking ? PhD F: So .
PhD B: uh the close channel ?
PhD F: Yeah . Yeah .
PhD B: and s channel one is the PhD F: Yeah . So C - zero is very clean , actually .
PhD B: Yeah .
PhD F: Uh then when we apply mean normalization it looks like the second figure , though it is not . Which is good . Well , the noise part is around zero
Professor A: Mm - hmm .
PhD F: and   And then the third figure is what happens when we apply mean normalization and variance normalization . So . What we can clearly see is that on the speech portion the two channel come becomes very close , but also what happens on the noisy portion is that the variance of the noise is Professor A: Mm - hmm .
PhD B: This is still being a plot of C - zero ? OK .
PhD F: Yeah . This is still C - zero .
PhD B: Can I ask um what does variance normalization do ? w What is the effect of that ?
Professor A: Normalizes the variance .
PhD F: So it it Yeah .
PhD B: I mean
PhD F: It normalized th the standard deviation .
PhD B: y Yeah .
PhD F: So it PhD B: No , I understand that ,
PhD F: You you get an estimate of the standard deviation .
PhD B: but I mean PhD F: That 's
PhD B: No .
PhD F: um PhD B: No , I understand what it is , but I mean , what does it what 's what is
PhD F: Yeah but .
PhD B: uh Professor A: What 's the rationale ?
PhD B: We Yeah . Yeah . Why why do it ?
PhD F: Uh .
Professor A: Well , I mean , because everything uh If you have a system based on Gaussians , everything is based on means and variances .
PhD B: Yeah .
Professor A: So if there 's an overall reason You know , it 's like uh if you were doing uh image processing and in some of the pictures you were looking at , uh there was a lot of light uh and and in some , there was low light ,
PhD B: Mm - hmm .
Professor A: you know , you would want to adjust for that in order to compare things .
PhD B: Mm - hmm .
Professor A: And the variance is just sort of like the next moment , you know ? So uh what if um one set of pictures was taken uh so that throughout the course it was went through daylight and night uh um um ten times , another time it went thr I mean i is , you know , how how much  how much vari
PhD B: Oh , OK .
Professor A: Or no . I guess a better example would be how much of the light was coming in from outside rather than artificial light . So if it was a lot  if more was coming from outside , then there 'd be the bigger effect of the of the of the change in the 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 .
PhD B: Oh , OK . Uh - huh .
Professor A: And so , in principle , you if you remove that source , then , you know , you can PhD B: I see . OK . So would the major effect is that you 're gonna get is by normalizing the means ,
Professor A: That 's the first order but thing ,
PhD B: but it may help First - order effects .
Professor A: but then the second order is is the variances
PhD B: And it may help to do the variance . OK .
Professor A: because , again , if you if you 're trying to distinguish between E and B
PhD B: OK .
Professor 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 ,
PhD B: Mm - hmm . Mm - hmm . Mm - hmm .
Professor A: or the variation in it was larger , uh than with the B 's , then this will be give you some some bias .
PhD B :
Professor A: So the  it 's removing these sources of variability in the data that have nothing to do with the linguistic component .
PhD B: OK .
PhD F: Mmm .
PhD B: Gotcha . OK . Sorry to interrupt .
Professor A: But the the uh but let me as ask ask you something .
PhD F: Yep . And it and this Professor A: i is if If you have a good voice activity detector , isn't isn't it gonna pull that out ?
PhD F: Yeah . Sure . If they are good . Yeah . Well what it it shows is that , yeah , perhaps a good voice activity detector is is good before on - line normalization and that 's what uh we 've already observed . But uh , yeah , voice activity detection is not  an easy thing neither .
PhD B: But after you do this , after you do the variance normalization I mean .
PhD F: Mm - hmm .
PhD B: I don't know , it seems like this would be a lot easier than this signal to work with .
PhD F: Yeah . So . What I notice is that , while I prefer to look at the second figure than at the third one , well , because you clearly see where speech is .
Professor A: Yeah .
PhD B: Yeah .
PhD 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 .
Professor A: Right .
PhD B: But for the purposes of finding the speech PhD F: And Yeah , but here PhD B: You 're more interested in the difference between the speech and the nonspeech ,
PhD F: Yeah .
PhD B: right ?
PhD F: Yeah . So I think , yeah . 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 .
Professor A: Yeah . So basically you want to reduce this effect .
PhD F: Well , y
Professor A: So you can do that by doing the voi voice activity detection . You also could do it by spect uh spectral subtraction before the variance normalization , right ?
PhD F: Yeah , but it 's not clear , yeah .
Professor A: So uh PhD F: We So . Well . It 's just to
Professor A: Yeah .
PhD F: the the number that at that are here are recognition experiments on Italian HM and MM with these two kinds of parameters . And , well , it 's better with variance normalization .
Professor A: Yeah . Yeah . So it does get better even though it looks ugly .
PhD F: Uh Professor A: OK . but does this have the voice activity detection in it ?
PhD F: Yeah .
Professor A: OK .
PhD F: Um .
Professor A: So .
Grad E: OK .
PhD B: Where 's th
PhD F: But the fact is that the voice activity detector doesn't work on channel one . So . Yeah .
Professor A: Uh - huh .
PhD B: Where at what stage is the voice activity detector applied ? Is it applied here or a after the variance normalization ?
PhD F: Hmm ?
Professor A: Spectral subtraction , I guess .
PhD B: or PhD F: It 's applied before variance normalization . So it 's a good thing ,
PhD B: Oh .
PhD F: because I guess voice activity detection on this should could be worse .
PhD B: Yeah . Is it applied all the way back here ?
PhD F: It 's applied the um on , yeah , something like this ,
PhD B: Maybe that 's why it doesn't work for channel one .
PhD F: yeah . Perhaps , yeah .
Professor A: Can I PhD F: So we could perhaps do just mean normalization before VAD .
PhD B: Mm - hmm .
Professor A: Mm - hmm . Can I ask a , I mean a sort of top - level question , 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 ? "
PhD F: Mm - hmm . About ? Spectral subtraction ?
Professor A: Yeah .
PhD F: It 's just uh Well it 's another They are trying to u to use the um  the Ericsson and we 're trying to use something something else . And . Yeah , and also to understand what happens because
Professor A: OK .
PhD F: uh fff Well . When we do spectral subtraction , actually , I think that this is the the two last figures .
Professor A: Yeah .
PhD F: Um . It seems that after spectral subtraction , speech is more emerging now uh than than before .
Professor A: Mm - hmm .
PhD B: Speech is more what ?
PhD F: Well , the difference between the energy of the speech and the energy of the n spectral subtrac subtracted noise portion is is larger .
Professor A: Mm - hmm .
PhD F: Well , if you compare the first figure to this one Actually the scale is not the same , 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 . Uh but what happens is that after spectral subtraction , you also increase the variance of this of C - zero .
Professor A: Mm - hmm .
PhD F: And so if you apply variance normalization on this , it completely sc screw everything . Well .
Professor A: Mm - hmm .
PhD F: Um . Uh . Yeah . So yeah . And what they did at OGI is just uh they don't use on - line normalization , for the moment , on spectral subtraction and I think Yeah . I think as soon as they will try on - line normalization there will be a problem . So yeah , we 're working on the same thing but I think uh with different different system and Professor A: Right . I mean , i the Intellectually it 's interesting to work on things th uh one way or the other
PhD F: Mm - hmm .