Professor C: Uh , So What else is going on ?
PhD D: Mmm . You Yeah . We are playing we are also playing , trying to put other spectral subtraction mmm , in the code . Um , it would be a very simple spectral subtraction , on the um , mel energies which I already tested but without the um frame dropping actually , and I think it 's important to have frame dropping if you use spectral subtraction .
PhD F: Is it is spectral subtraction typically done on the after the mel , uh , scaling or is it done on the FFT bins ?
PhD D: Um ,
PhD F: Does it matter , or ?
PhD D: I d I don't know . Well , it 's both both uh , cases can i
PhD F: Oh .
PhD D: Yeah . So - some of the proposal , uh , we 're doing this on the bin on the FFT bins ,
PhD F: Hmm .
PhD D: others on the um , mel energies . You can do both , but I cannot tell you what 's which one might be better or I PhD F: Hmm .
PhD A: I guess if you want to reconstruct the speech , it may be a good idea to do it on FFT bins .
PhD D: I don't know . Yeah , but
PhD F: Mmm .
PhD A: But for speech recognition , it may not . I mean it may not be very different if you do it on mel warped or whether you do it on FFT . So you 're going to do a linear weighting anyway after that .
PhD F: I see .
PhD A: Well Yeah ?
PhD F: Hmm .
PhD A: So , it may not be really a big different .
PhD D: Well , it gives something different , but I don't know what are the , pros and cons of both .
PhD A: It I Uh - huh .
Professor C: Hmm .
PhD A: So
Professor C: OK .
PhD A: The other thing is like when you 're putting in a speech enhancement technique , uh , is it like one stage speech enhancement ? Because everybody seems to have a mod two stages of speech enhancement in all the proposals , which is really giving them some improvement .
PhD D: Yeah .
PhD B: Mm - hmm .
PhD D: Mm - hmm .
PhD A: I mean they just do the same thing again once more .
Professor C: Mm - hmm .
PhD A: And So , there 's something that is good about doing it I mean , to cleaning it up once more .
PhD D: Yeah , it might be .
PhD A: Yeah ,
PhD D: Yeah .
PhD A: so we can PhD D: So maybe in my implementation I should also try to inspire me from this kind of thing
PhD A: Yeah . That 's what
Professor C: Well , the other thing would be to combine what you 're doing .
PhD D: and Yeah .
Professor C: I mean maybe one or one or the other of the things that you 're doing would benefit from the other happening first .
PhD A: That 's wh Yeah . So ,
Professor C: Right , so he 's doing a signal subspace thing , maybe it would work better if you 'd already done some simple spectral subtraction , or maybe vi maybe the other way around ,
PhD D: Yeah , mm - hmm .
PhD A: Yeah .
Professor C: you know ?
PhD A: So I 've been thinking about combining the Wiener filtering with signal subspace ,
PhD D: Mm - hmm .
PhD A: I mean just to see all some some such permutation combination to see whether it really helps or not .
PhD D: Mm - hmm . Mm - hmm . Mm - hmm . Yeah . Yeah .
Professor C: How is it I I guess I 'm ignorant about this , how does I mean , since Wiener filter also assumes that you 're that you 're adding together the two signals , how is how is that differ from signal subspace ?
PhD A: The signal subspace ? The Professor C: Yeah .
PhD A: The signal subspace approach has actually an in - built Wiener filtering in it .
Professor C: Oh , OK .
PhD A: Yeah . It is like a KL transform followed by a Wiener filter . Is the signal is is a signal substrate .
Professor C: Oh , oh , OK so the difference is the KL .
PhD A: So , the the different the c the the advantage of combining two things is mainly coming from the signal subspace approach doesn't work very well if the SNR is very bad . It 's it works very poorly with the poor SNR conditions , and in colored noise .
Professor C: I see . So essentially you could do simple spectral subtraction , followed by a KL transform , followed by a
PhD A: Wiener filtering . It 's a it 's a cascade of two s
Professor C: Wiener filter . Yeah , in general , you don't that 's right you don't wanna othorg orthogonalize if the things are noisy . Actually . Um , that was something that uh , Herve and I were talking about with um , the multi - band stuff , that if you 're converting things to from uh , bands , groups of bands into cepstral coef you know , local sort of local cepstral coefficients that it 's not that great to do it if it 's noisy .
PhD A: Mm - hmm . OK . Yeah . So .
Professor C: Uh , so .
PhD A: So that that 's one reason maybe we could combine s some something to improve SNR a little bit , first stage ,
Professor C: Yeah .
PhD A: and then do a something in the second stage which could take it further .
PhD D: What was your point about about colored noise there ?
PhD A: Oh , the colored noise uh PhD D: Yeah .
PhD A: the colored noise the the v the signal subspace approach has I mean , it it actually depends on inverting the matrices . So it it ac the covariance matrix of the noise . So if if it is not positive definite ,
PhD D: Mm - hmm .
PhD A: I mean it has a it 's It doesn't behave very well if it is not positive definite ak It works very well with white noise because we know for sure that it has a positive definite .
Professor C: So you should do spectral subtraction and then add noise .
PhD A: So the way they get around is like they do an inverse filtering , first of the colo colored noise
Professor C: Yeah .
PhD A: and then make the noise white ,
Professor C: Yeah .
PhD A: and then finally when you reconstruct the speech back , you do this filtering again .
PhD D: Yeah , right .
Professor C: I was only half kidding . I mean if you sort of you do the s spectral subtraction , that also gets rid PhD A: Yeah .
PhD D: Yeah .
PhD A: Yeah .
Professor C: and then you then then add a little bit l noise noise addition I mean , that sort of what J JRASTA does , in a way .
PhD A: Yeah .
Professor C: If you look at what JRASTA doing essentially i i it 's equivalent to sort of adding a little adding a little noise ,
PhD A: Huh ? Uh - huh .
PhD D: Uh - huh .
Professor C: in order to get rid of the effects of noise .
PhD A: So .
Professor C: OK .
PhD D: Yeah . Uh , yeah . So there is this . And maybe we well we find some people so that uh , agree to maybe work with us , and they have implementation of VTS techniques so it 's um , Vector Taylor Series that are used to mmm , uh f to model the transformation between clean cepstra and noisy cepstra . So . Well , if you take the standard model of channel plus noise , uh , it 's it 's a nonlinear eh uh , transformation in the cepstral domain .
Professor C: Mm - hmm . Yes .
PhD D: And uh , there is a way to approximate this using uh , first - order or second - order Taylor Series and it can be used for uh , getting rid of the noise and the channel effect .
Professor C: Who is doing this ?
PhD D: Uh w working in the cepstral domain ? So there is one guy in Grenada ,
PhD B: Yeah , in Grenada one of my friend .
PhD D: and another in uh , Lucent that I met at ICASSP .
Professor C: Who 's the guy in Grenada ?
PhD D: uh ,
PhD B: Uh , Jose Carlos Segura .
Professor C: I don't know him .
PhD A: This VTS has been proposed by CMU ?
PhD D: Mm - hmm .
PhD A: Is it is it the CMU ? Yeah , yeah , OK .
PhD B: Yeah , yeah , yeah . Originally the idea was from CMU .
PhD A: From C .
PhD D: Mm - hmm . Yeah .
Professor C: Uh - huh .
PhD D: Well , it 's again a different thing  that could be tried . Um ,
Professor C: Uh - huh .
PhD D: Mmm , yeah .
Professor C: Yeah , so at any rate , you 're looking general , uh , standing back from it , looking at ways to combine one form or another of uh , noise removal , uh , with with these other things we have ,
PhD D: Mm - hmm .
Professor C: uh , looks like a worthy thing to to do here .
PhD D: Uh , yeah . But , yeah . But for sure there 's required to that requires to re - check everything else , and re - optimize the other things
Professor C: Oh yeah .
PhD D: and , for sure the on - line normalization may be the LDA filter . Um ,
Professor C: Well one of the seems like one of the things to go through next week when Hari 's here ,
PhD D: I Professor C: cuz Hari 'll have his own ideas too or I guess not next week ,
PhD D: Uh - huh .
Professor C: week and a half , uh , will be sort of go through these alternatives , what we 've seen so far , and come up with some game plans . Um . You know . So , I mean one way would he Here are some alternate visions . I mean one would be , you look at a few things very quickly , you pick on something that looks like it 's promising and then everybody works really hard on the same different aspects of the same thing . Another thing would be to have t to to pick two pol two plausible things , and and you know , have t sort of two working things for a while until we figure out what 's better ,
PhD D: Mm - hmm .
Professor C: and then , you know , uh , but , w um , uh , he 'll have some ideas on that too .
PhD A: The other thing is to , uh Most of the speech enhancement techniques have reported results on small vocabulary tasks . But we we going to address this Wall Street Journal in our next stage , which is also going to be a noisy task so s very few people have reported something on using some continuous speech at all . So , there are some I mean , I was looking at some literature on speech enhancement applied to large vocabulary tasks and spectral subtraction doesn't seems to be the thing to do for large vocabulary tasks . And it 's Always people have shown improvement with Wiener filtering and maybe subspace approach over spectral subtraction everywhere . But if we if we have to use simple spectral subtraction , we may have to do some optimization to make it work @ @ .
Professor C: So they 're making there Somebody 's generating Wall Street Journal with additive artificially added noise or something ?
PhD A: Yeah , yeah .
Professor C: Sort of a sort of like what they did with TI - digits , and ?
PhD A: Yeah . Yeah .
Professor C: Yeah , OK .
PhD A: I m I guess Guenter Hirsch is in charge of that . Guenter Hirsch and TI .
Professor C: OK .
PhD A: Maybe Roger r Roger , maybe in charge of .
Professor C: And then they 're they 're uh , uh , generating HTK scripts to PhD A: Yeah . Yeah , I don't know . There are they have there is no I don't know if they are converging on HTK or are using some Mississippi State ,
Professor C: Mis - Mississippi State maybe ,
PhD A: yeah . I 'm not sure about that .
Professor C: yeah . Yeah , so that 'll be a little little task in itself .
PhD A: Yeah .
Professor C: Um , well we 've Yeah , it 's true for the additive noise , y artificially added noise we 've always used small vocabulary too . But for n there 's been noisy speech this larv large vocabulary that we 've worked with in Broadcast News . So we we did the Broadcast News evaluation
PhD A: Mm - hmm .
Professor C: and some of the focus conditions were noisy and and PhD A: It had additive n
Professor C: But we but we didn't do spectral subtraction . We were doing our funny stuff , right ? We were doing multi multi uh , multi - stream and and so forth .
PhD A: Yeah .
Professor C: But it , you know , we di stuff we did helped . I mean it , did something .
PhD A: OK .
Professor C: So . Um , now we have this um , meeting data . You know , like the stuff we 're {comment} recording right now ,
PhD A: Yeah . Yeah .
Professor C: and and uh , that we have uh , for the uh , the quote - unquote noisy data there is just noisy and reverberant actually . It 's the far field mike . And uh , we have uh , the digits that we do at the end of these things . And that 's what most o again , most of our work has been done with that , with with uh , connected digits .
PhD A: Uh - huh .
Professor C: Um , but uh , we have recognition now with some of the continuous speech , large vocabulary continuous speech , using Switchboard uh , Switchboard recognizer ,
PhD A: Yeah . OK .
Professor C: uh , no training , from this , just just plain using the Switchboard .
PhD A: Oh . You just take the Switchboard trained ? Yeah ,
Professor C: That 's that 's what we 're doing ,
PhD A: yeah .
Professor C: yeah . Now there are some adaptation though ,
PhD A: OK . Yeah . That 's cool .
Professor C: that that uh , Andreas has been playing with ,
PhD A: OK .
Professor C: but we 're hop uh , actually uh , Dave and I were just talking earlier today about maybe at some point not that distant future , trying some of the techniques that we 've talked about on , uh , some of the large vocabulary data . Um , I mean , I guess no one had done yet done test one on the distant mike using uh , the SRI recognizer and , uh ,
PhD F: I don't not that I know of .
Professor C: Yeah , cuz everybody 's scared .
PhD A: Yeah .
Professor C: You 'll see a little smoke coming up from the the CPU or something trying to trying to do it ,
PhD F: That 's right
Professor C: but uh , yeah . But , you 're right that that that 's a real good point , that uh , we we don't know yeah , uh , I mean , what if any of these ta I guess that 's why they 're pushing that in the uh in the evaluation .
PhD A: Yeah .