PhD D: Also we were thinking to to , uh , apply the eh , spectral subtraction from Ericsson
PhD B: Yeah .
Professor C: Uh - huh .
PhD D: and to to change the contextual KLT for LDA .
PhD A: Change the what ?
PhD D: The contextual KLT .
PhD A: I 'm missing that last word . Context
Professor C: K KLT .
PhD A: KLT .
PhD D: KLT Grad E: Oh . KLT .
PhD A: Oh , KLT .
Professor C: Mm - hmm .
PhD A: Uh - huh .
PhD D: KLT , I 'm sorry . Uh , to change and use LDA discriminative .
PhD B: Yeah .
Professor C: Uh - huh .
PhD D: But I don't know .
Professor C: Uh ,
PhD A: What is the advantage of that ?
PhD D: Uh PhD B: Well , it 's that by the for the moment we have , uh , something that 's discriminant and nonlinear . And the other is linear but it 's not discriminant at all . Well , it 's it 's a linear transformation , that Uh Professor C: So at least just to understand maybe what the difference was between how much you were getting from just putting the frames together and how much you 're getting from the discriminative , what the nonlinearity does for you or doesn't do for you . Just to understand it a little better I guess .
PhD B: Mmm . Well uh yeah . Actually what we want to do , perhaps it 's to replace to to have something that 's discriminant but linear , also . And to see if it if it improves ov over over the non - discriminant linear transformation .
PhD A: Hmm .
PhD B: And if the neural net is better than this or , well . So .
Professor C: Yeah , well , that 's what I meant , is to see whether whether it having the neural net really buys you anything .
PhD B: Ye Mmm .
Professor C: Uh , I mean , it doe did look like it buys you something over just the KLT .
PhD B: Yeah .
Professor C: But maybe it 's just the discrimination and and maybe yeah , maybe the nonlinear discrimination isn't necessary .
PhD D: S maybe .
PhD B: Yeah . Mm - hmm .
Professor C: Could be .
PhD D: Maybe .
Professor C: Good good to know . But the other part you were saying was the spectral subtraction , so you just kind of , uh PhD B: Yeah .
Professor C: At what stage do you do that ? Do you you 're doing that , um ?
PhD B: So it would be on the um on on the mel frequency bands ,
PhD D: We was think
PhD B: so . Yeah , be before everything .
Professor C: OK ,
PhD D: Yeah ,
Professor C: so just do that on the mel f
PhD D: we no nnn We we was thinking to do before after VAD or
PhD B: Yeah ,
PhD D: Oh , {comment} we don't know exactly when it 's better .
PhD B: um PhD D: Before after VAD or Professor C: So so you know that that that the way that they 're PhD D: and then
PhD B: Um .
Professor C: uh , one thing that would be no good to find out about from this conference call is that what they were talking about , what they 're proposing doing , was having a third party , um , run a good VAD , and and determine boundaries .
PhD D: Yeah .
Professor C: And then given those boundaries , then have everybody do the recognition .
PhD D: Begin to work .
Professor C: The reason for that was that , um , uh if some one p one group put in the VAD and another didn't , uh , or one had a better VAD than the other since that they 're not viewing that as being part of the the task , and that any any manufacturer would put a bunch of effort into having some s kind of good speech - silence detection . It still wouldn't be perfect but I mean , e the argument was " let 's not have that be part of this test . " " Let 's let 's separate that out . " And so , uh , I guess they argued about that yesterday and , yeah , I 'm sorry , I don't don't know the answer but we should find out . I 'm sure we 'll find out soon what they , uh what they decided . So , uh Yeah , so there 's the question of the VAD but otherwise it 's it 's on the the , uh the mel fil filter bank , uh , energies I guess ?
PhD D: Mm - hmm .
PhD B: Mmm , yeah .
Professor C: You do doing the ?
PhD D: Mm - hmm .
Professor C: And you 're you 're subtracting in the in the in the I guess it 's power power domain , uh , or or magnitude domain . Probably power domain , right ?
PhD B: I guess it 's power domain , yeah .
Professor C: why
PhD B: I don't remember exactly .
Professor C: Yeah ,
PhD D: I don't remember .
PhD B: But yeah , so it 's before everything else ,
Professor C: yep .
PhD B: and Professor C: I mean , if you look at the theory , it 's it should be in the power domain but but , uh , I 've seen implementations where people do it in the magnitude domain
PhD B: Yeah .
Professor C: and PhD B: Mmm .
Professor C: I have asked people why and they shrug their shoulders and say , " oh , it works . " So .
PhD B: Yeah .
Professor C: Uh , and there 's this I guess there 's this mysterious I mean people who do this a lot I guess have developed little tricks of the trade . I mean , there 's there 's this , um you don't just subtract the the estimate of the noise spectrum . You subtract th that times PhD B: A little bit more and Yeah .
Professor C: Or or less , or PhD A: Really ?
PhD B: Yeah .
PhD A: Huh !
Professor C: Yeah .
PhD B: And generated this this ,
Professor C: Uh .
PhD B: um , so you have the estimation of the power spectra of the noise , and you multiply this by a factor which is depend dependent on the SNR . So . Well .
PhD D: Hmm , maybe .
PhD A: Hmm !
PhD B: When the speech lev when the signal level is more important , compared to this noise level , the coefficient is small , and around one . But when the power le the s signal level is uh small compared to the noise level , the coefficient is more important . And this reduce actually the music musical noise ,
PhD A: Oh !
PhD B: uh which is more important during silence portions ,
PhD A: Uh - huh .
PhD B: when the s the energy 's small .
PhD A: Hmm !
PhD B: So there are tricks like this but , mmm .
PhD A: Hmm !
Professor C: Yeah .
PhD B: Yeah .
Professor C: So .
PhD A: Is the estimate of the noise spectrum a running estimate ? Or PhD B: Yeah .
Professor C: Yeah .
PhD B: Yeah .
Professor C: Well , that 's I mean , that 's what differs from different different tasks and different s uh , spectral subtraction methods .
PhD A: Hmm !
Professor C: I mean , if if you have , uh , fair assurance that , uh , the noise is is quite stationary , then the smartest thing to do is use as much data as possible to estimate the noise , get a much better estimate , and subtract it off .
PhD A: Mm - hmm .
Professor C: But if it 's varying at all , which is gonna be the case for almost any real situation , you have to do it on - line , uh , with some forgetting factor or something .
PhD A: So do you is there some long window that extends into the past over which you calculate the average ?
Professor C: Well , there 's a lot of different ways of computing the noise spectrum . So one of the things that , uh , Hans - Guenter Hirsch did , uh and pas and other people actually , he 's he wasn't the only one I guess , was to , uh , take some period of of of speech and in each band , uh , develop a histogram . So , to get a decent histogram of these energies takes at least a few seconds really . But , uh I mean you can do it with a smaller amount but it 's pretty rough . And , um , in fact I think the NIST standard method of determining signal - to - noise ratio is based on this .
PhD A: A couple seconds ?
Professor C: So No , no , it 's based on this kind of method ,
PhD A: Hmm .
Professor C: this histogram method . So you have a histogram . Now , if you have signal and you have noise , you basically have these two bumps in the histogram , which you could approximate as two Gaussians .
PhD A: But wh don't they overlap sometimes ?
Professor C: Oh , yeah .
PhD A: OK .
Professor C: So you have a mixture of two Gaussians .
PhD A: Yeah .
Professor C: Right ? And you can use EM to figure out what it is . You know .
PhD A: Yeah .
Professor C: So so basically now you have this mixture of two Gaussians , you you n know what they are , and , uh I mean , sorry , you estimate what they are , and , uh , so this gives you what the signal is and what the noise e energy is in that band in the spectrum . And then you look over the whole thing and now you have a noise spectrum . So , uh , Hans - Guenter Hirsch and others have used that kind of method . And the other thing to do is which is sort of more trivial and obvious {comment} is to , uh , uh , determine through magical means that that , uh , there 's no speech in some period , and then see what the spectrum is .
PhD A: Mm - hmm .
Professor C: Uh , but , you know , it 's that that that 's tricky to do . It has mistakes . Uh , and if you 've got enough time , uh , this other method appears to be somewhat more reliable . Uh , a variant on that for just determining signal - to - noise ratio is to just , uh you can do a w a uh an iterative thing , EM - like thing , to determine means only . I guess it is EM still , but just just determine the means only . Don't worry about the variances .
PhD A: Mm - hmm .
Professor C: And then you just use those mean values as being the the , uh uh signal - to - noise ratio in that band .
PhD A: But what is the it seems like this kind of thing could add to the latency . I mean , depending on where the window was that you used to calculate the signal - to - noise ratio .
PhD B: Yeah , sure . But Mmm .
Professor C: Not necessarily . Cuz if you don't look into the future , right ?
PhD A: OK , well that I guess that was my question ,
Professor C: if you just yeah PhD A: yeah .
Professor C: I mean , if you just if you you , uh a at the beginning you have some PhD A: Guess .
Professor C: esti some guess and and , uh , uh PhD B: Yeah , but it Professor C: It 's an interesting question . I wonder how they did do it ?
PhD B: Actually , it 's a mmm If - if you want to have a good estimation on non - stationary noise you have to look in the in the future . I mean , if you take your window and build your histogram in this window , um , what you can expect is to have an estimation of th of the noise in in the middle of the window , not at the end . So Professor C: Well , yeah ,
PhD B: the but but people Professor C: but what does what what what does Alcatel do ?
PhD D: Mm - hmm .
Professor C: And and France Telecom .
PhD B: The They just look in the past . I guess it works because the noise are , uh pret uh , almost stationary
Professor C: Pretty stationary .
Grad E: Pretty stationary ,
PhD B: but , um Professor C: Well , the thing , e e e e
Grad E: yeah .
Professor C: Yeah , y I mean , you 're talking about non - stationary noise but I think that spectral subtraction is rarely is is not gonna work really well for for non - stationary noise ,
PhD B: Well , if y if you have a good estimation of the noise ,
Professor C: you know ?
PhD B: yeah , because well it it has to work .
Professor C: But it 's hard to PhD B: i
Professor C: but that 's hard to do .
PhD B: Yeah , that 's hard to do . Yeah .
Professor C: Yeah . So so I think that that what what is wh what 's more common is that you 're going to be helped with r slowly varying or stationary noise .
PhD B: But Mm - hmm .
Professor C: That 's what spectral subtraction will help with , practically speaking .
PhD B: Mm - hmm . Mm - hmm .
Professor C: If it varies a lot , to get a If if to get a good estimate you need a few seconds of speech , even if it 's centered , right ?
PhD B: Mm - hmm .
Professor C: if you need a few seconds to get a decent estimate but it 's changed a lot in a few seconds , then it , you know , i it 's kind of a problem .
PhD B: Yeah .
Professor C: I mean , imagine e five hertz is the middle of the of the speech modulation spectrum ,
PhD B: Mmm .
Professor C: right ? So imagine a jack hammer going at five hertz .
PhD B: Yeah , that 's Professor C: I mean , good good luck . So ,
PhD B: So in this case , yeah , sure , you cannot Professor C: Yeah .
PhD B: But I think y um , Hirsch does experiment with windows of like between five hundred milliseconds and one second . And well , five hundred wa was not so bad . I mean and he worked on non - stationary noises , like noise modulated with well , wi with amplitude modulations and things like that ,
PhD A: Were his , uh , windows centered around the PhD B: and But Um , yeah . Well , I think Yeah . Well , in in the paper he showed that actually the estimation of the noise is is delayed . Well , it 's there is you you have to center the window , yeah .
Professor C: Yeah .
PhD B: Mmm .
Professor C: No , I understand it 's better to do but I just think that that , uh , for real noises wh what what 's most likely to happen is that there 'll be some things that are relatively stationary
PhD B: Mmm .
Professor C: where you can use one or another spectral subtraction thing
PhD B: Yeah .
Professor C: and other things where it 's not so stationary and I mean , you can always pick something that that falls between your methods ,
PhD B: Hmm .
Professor C: uh , uh , but I don't know if , you know , if sinusoidally , uh , modul amplitude modulated noise is is sort of a big problem in in in practice .
PhD B: Yeah .
Professor C: I think that it 's uh PhD A: We could probably get a really good estimate of the noise if we just went to the noise files , and built the averages from them .
Professor C: Yeah . Well .
PhD B: What What do you mean ?
Professor C: Just cheat You 're saying , cheat .
PhD B: But if the if the noise is stationary perhaps you don't even need some kind of noise estimation algorithm .
Professor C: Yeah . Yeah .
PhD B: We just take th th th the beginning of the utterance and
Professor C: Oh , yeah , sure .
PhD B: I I know p I don't know if people tried this for Aurora .
PhD D: It 's the same .
PhD B: Well , everybody seems to use some kind of adaptive , well , scheme
Professor C: But but PhD D: Yeah .
PhD B: but ,
PhD D: A dictionary .
PhD B: is it very useful