A: So , the the different A: 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 .
A: It 's C: I see .
A: it works very poorly with the poor SNR conditions , and in colored noise .
C: So essentially you could do simple spectral subtraction , followed by a KL transform , followed by a
A: Wiener filtering .
C: Wiener filter .
A: It 's a it 's a cascade of two s
C: Yeah , in general , you don't C: that 's right
C: you don't wanna othorg orthogonalize if the things are noisy .
C: Actually .
C: Um , that was something that uh , Herve and I were talking about with um , the multi - band stuff ,
C: 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 .
A: Mm - hmm .
A: OK .
A: Yeah .
C: Uh ,
C: so .
A: So .
A: So that that 's one reason maybe we could combine
A: s some something to improve SNR a little bit , first stage ,
C: Yeah .
A: and then do a something in the second stage
A: which could take it further .
D: What was your point about about colored noise there ?
A: Oh , the colored noise
D: Yeah .
A: uh A: the colored noise the the v the signal subspace approach has I mean , it it actually depends on inverting the matrices .
A: So it it ac
A: the covariance matrix of the noise .
D: Mm - hmm .
A: So if if it is not positive definite ,
A: I mean it has a it 's A: It doesn't behave very well if it is not positive definite
A: ak
A: It works very well with white noise because we know for sure that it has a positive definite .
C: So you should do spectral subtraction and then add noise .
A: So the way they get around is like they do an inverse filtering ,
A: first of the colo colored noise
C: Yeah .
A: and then make the noise white ,
C: Yeah .
A: and then finally when you reconstruct the speech back , you do this filtering again .
D: Yeah , right .
C: I was only half kidding .
C: I mean if you sort of you do the s spectral subtraction , that also gets rid D: Yeah .
A: Yeah .
A: Yeah .
C: and then you then then add a little bit l noise noise addition C: I mean , that sort of what J JRASTA does , in a way .
C: If you look at what JRASTA doing essentially i i it 's equivalent to sort of adding a little adding a little noise ,
A: Yeah .
A: Huh ?
A: Uh - huh .
D: Uh - huh .
C: in order to get rid of the effects of noise .
A: So .
D: Yeah .
C: OK .
D: Uh , yeah .
D: So there is this .
D: And
D: maybe we D: well we find some people so that uh , agree to maybe work with us ,
D: and they have implementation of VTS techniques
D: so it 's um , Vector Taylor Series that are used to mmm , uh f to model the transformation between clean cepstra and noisy cepstra .
D: So . Well ,
D: if you take the standard model of channel plus noise , uh , it 's it 's a nonlinear eh uh , transformation in the cepstral domain .
C: Mm - hmm .
C: Yes .
D: And
D: uh , there is a way to approximate this using uh , first - order or second - order Taylor Series
D: and
D: it can be used for uh , getting rid of the noise and the channel effect .
C: Who is doing this ?
D: Uh w working in the cepstral domain ?
D: So there is one guy in Grenada ,
B: Yeah , in Grenada
D: and another in uh , Lucent that I met at ICASSP .
B: one of my friend .
D: uh ,
C: Who 's the guy in Grenada ?
B: Uh , Jose Carlos Segura .
C: I don't know him .
A: This VTS has been proposed by CMU ?
D: Mm - hmm .
A: Is it is it the CMU ?
B: Yeah , yeah , yeah .
A: Yeah , yeah , OK .
B: Originally the idea was from CMU .
D: Mm - hmm .
A: From C .
D: Yeah .
C: Uh - huh .
D: Well , it 's again a different thing  that could be tried .
D: Um ,
C: Uh - huh .
D: Mmm , yeah .
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 ,
D: Mm - hmm .
C: uh , looks like a worthy thing to to do here .
D: Uh , yeah .
D: But , yeah .
D: But for sure there 's required to that requires to re - check everything else , and re - optimize the other things
C: Oh yeah .
D: and ,
D: for sure the on - line normalization may be the LDA filter .
D: Um ,
C: Well one of the seems like one of the things to go through next week when Hari 's here ,
D: I C: cuz Hari 'll have his own ideas too D: Uh - huh .
C: or I guess not next week ,
C: week and a half ,
C: uh , will be sort of go through these alternatives ,
C: what we 've seen so far ,
C: and come up with some game plans .
C: Um .
C: You know .
C: So , I mean one way would C: he Here are some alternate visions .
C: I mean one would be , you look at a few things very quickly ,
C: you pick on something that looks like it 's promising
C: and then everybody works really hard on the same different aspects of the same thing .
C: Another thing would be to have t to to pick two pol two plausible things ,
C: and and you know , have t sort of two working things for a while until we figure out what 's better ,
D: Mm - hmm .
C: and then ,
C: you know ,
C: uh ,
C: but , w um ,
C: uh , he 'll have some ideas on that too .
A: The other thing is to , uh Most of the speech enhancement techniques have reported results on small vocabulary tasks .
A: But we we going to address this Wall Street Journal in our next stage ,
A: which is also going to be a noisy task
A: so s very few people have reported something on using some continuous speech at all .
A: So , there are some A: I mean , I was looking at some literature on speech enhancement applied to large vocabulary tasks
A: and
A: spectral subtraction doesn't seems to be the thing to do for large vocabulary tasks .
A: And it 's A: Always people have shown improvement with Wiener filtering and maybe subspace approach over spectral subtraction everywhere .
A: But if we if we have to use simple spectral subtraction , we may have to do some optimization to make it work @ @ .
C: So they 're making C: there Somebody 's generating Wall Street Journal with additive artificially added noise or something ?
A: Yeah , yeah .
C: Sort of a sort of like what they did with TI - digits , and ?
A: Yeah .
C: Yeah , OK .
A: Yeah .
A: I m I guess Guenter Hirsch is in charge of that .
A: Guenter Hirsch and TI .
C: OK .
A: Maybe Roger A: r Roger , maybe in charge of .
C: And then they 're they 're uh , uh , generating HTK scripts to A: Yeah .
A: Yeah , I don't know .
A: There are they have there is no A: I don't know if they are converging on HTK or are using some Mississippi State ,
C: Mis - Mississippi State maybe ,
A: yeah .
C: yeah .
A: I 'm not sure about that .
C: Yeah , so that 'll be a little little task in itself .
A: Yeah .
C: Um ,
C: well we 've C: Yeah , it 's true for the additive noise , y artificially added noise we 've always used small vocabulary too .
C: But for n there 's been noisy speech this larv large vocabulary that we 've worked with in Broadcast News .
C: So we we did the Broadcast News evaluation
C: and some of the focus conditions were noisy
A: Mm - hmm .
C: and and A: It had additive n
C: But we but we didn't do spectral subtraction .
C: We were doing our funny stuff , right ?
C: We were doing multi multi uh , multi - stream and and so forth .
A: Yeah .
C: But it , you know , we di stuff we did helped .
C: I mean it , did something .
C: So .
A: OK .
C: Um ,
C: now we have this um , meeting data .
C: You know , like the stuff we 're <comment> recording right now ,
A: Yeah .
A: Yeah .
C: and C: and uh ,
C: that we have uh , for the uh , the quote - unquote noisy data there is just noisy and reverberant actually .
C: It 's the far field mike .
C: And uh , we have uh , the digits that we do at the end of these things .
C: And that 's what most o
C: again , most of our work has been done with that ,
C: with with uh , connected digits .
A: Uh - huh .
C: Um ,
C: but uh , we have recognition now with some of the continuous speech ,
C: large vocabulary continuous speech , using Switchboard uh , Switchboard recognizer ,
A: Yeah .
A: OK .
C: uh ,
C: no training , from this ,
C: just just plain using the Switchboard .
A: Oh . You just take the Switchboard trained ?
C: That 's that 's what we 're doing ,
A: Yeah ,
C: yeah .
A: yeah .
C: Now there are some adaptation though ,
A: OK .
C: that that uh , Andreas has been playing with ,
A: Yeah .
A: That 's cool .
A: OK .
C: but we 're hop
C: 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 .
C: Um ,
C: I mean , I guess no one had done yet done test one on the distant mike using uh , the SRI recognizer
C: and , uh ,
F: I don't