A: Uh F: Are y are you saying LDA ?
A: Yeah .
F: LDA .
F: OK .
A: Yeah .
A: So ,
A: just just the LDA filters .
A: I just plug in I just take the cepstral coefficients coming from their system and then plug in LDA on top of that .
F: Mm - hmm .
A: But the LDA filter that I used was different from what we submitted in the proposal .
A: What I did was I took the LDA filter 's design using clean speech ,
A: uh , mainly because the speech is already cleaned up after the enhancement
A: so , instead of using this , uh , narrow narrow band LDA filter that we submitted uh , I got new filters .
A: So
A: that seems to be giving uh , improving over their uh , system .
A: Slightly .
A: But , not very significantly .
A: And uh , that was
A: uh , showing any improvement over final by plugging in an LDA .
A: And uh ,
A: so then after after that I I added uh , on - line normalization also on top of that .
A: And that there there also I n I found that I have to make some changes to their time constant that I used
A: because th it has a a mean and variance update time constant
A: and which is not suitable for the enhanced speech , and whatever we try it on with proposal - one .
A: But um ,
A: I didn't I didn't play with that time constant a lot ,
A: I just t g
A: I just found that I have to reduce the value A: I mean , I have to increase the time constant , or reduce the value of the update value .
A: That 's all I found
A: So I have to .
A: Uh ,
A: Yeah .
A: And uh ,
A: uh , the other other thing what I tried was , I just um , uh , took the baseline and then ran it with the endpoint inf uh th information ,
A: just the Aurora baseline ,
A: to see that how much the baseline itself improves
A: by just supplying the information of the I mean the w speech and nonspeech .
A: And uh ,
A: I found that the baseline itself improves by twenty - two percent by just giving the wuh .
C: Uh , can you back up a second ,
C: I I I missed something ,
C: uh ,
C: I guess my mind wandered .
C: Ad - ad When you added the on - line normalization and so forth , uh , uh things got better again ?
A: Yeah .
C: or is it ?
A: No .
A: No .
C: Did it not ?
A: No , things didn't get better with the same time constant that we used .
C: No , no .
C: With a different time constant .
A: With the different time constant I found that A: I mean , I didn't get an improvement over not using on - line normalization ,
C: Oh .
C: No you didn't ,
A: because I I found that I would have change the value of the update factor .
C: OK .
A: But I didn't play it with play play quite a bit to make it better than .
C: Yeah .
C: OK .
A: So , it 's still not A: I mean , the on - line normalization didn't give me any improvement .
C: OK .
A: And uh ,
C: OK .
A: so ,
A: oh yeah
A: So I just stopped there with the uh , speech enhancement .
A: The the other thing what I tried was the adding the uh , endpoint information to the baseline
A: and that itself gives like twenty - two percent
A: because the the second the new phase is going to be with the endpointed speech .
A: And just to get a feel of how much the baseline itself is going to change by adding this endpoint information , I just , uh , use C: Hmm .
F: So people won't even have to worry about , uh , doing speech - nonspeech then .
A: Yeah
A: that 's , that 's what the feeling is like .
F: Mmm .
A: They 're going to give the endpoint information .
F: I see .
C: G I guess the issue is that people do that anyway ,
C: everybody does that ,
C: and they wanted to see , given that you 're doing that , what what are the best features that you should use .
A: Yeah .
F: Yeah ,
F: I see .
A: So ,
C: I mean clearly they 're interact .
C: So I don't know that I entirely agree with it .
F: Yeah .
C: But but it might be uh In some ways it might be better t to rather than giving the endpoints , to have a standard that everybody uses and then interacts with .
F: Mm - hmm .
C: But , you know . It 's it 's still someth reasonable .
F: So , are people supposed to assume that there is uh F: Are are people not supposed to use any speech outside of those endpoints ?
A: Uh F: Or can you then use speech outside of it for estimating background noise and things ?
A: No .
A: No .
A: That i I A: Yeah .
A: Yeah , yeah ,
A: exactly .
A: I guess that is that is where the consensus is .
A: Like y you will you will You 'll be given the information about the beginning and the end of speech
A: but the whole speech is available to you .
F: OK .
A: So .
C: So it should make the spectral subtraction style things work even better ,
C: because you don't have the mistakes in it .
A: Yeah .
C: Yeah ?
C: OK .
A: Yeah .
A: So A: So that that The baseline itself A: I mean , it improves by twenty - two percent .
A: I found that in s one of the SpeechDat - Car cases , that like , the Spanish one improves by just fifty percent by just putting the endpoint .
F: Wow .
A: w
A: I mean you don't need any further speech enhancement with fifty .
A: So , uh ,
F: So the baseline itself improves by fifty percent .
A: Yeah , by fifty percent .
C: Yeah .
C: So it 's g it 's gonna be harder to beat that actually .
F: Wow .
F: Yeah .
A: Yeah ,
C: But but A: so A: so that is when uh , the the qualification criteria was reduced from fifty percent to something like twenty - five percent for well - matched .
A: And I think they have they have actually changed their qualification c criteria now .
A: And uh ,
A: Yeah ,
A: I guess after that , I just went home f I just had a vacation fo for four weeks .
C: OK .
A: Uh .
C: No , that 's that 's that 's a good good update .
A: Ye
A: Yeah ,
A: and I I came back and I started working on uh , some other speech enhancement algorithm .
A: I mean , so I from the submission what I found that people have tried spectral subtraction and Wiener filtering . These are the main uh , approaches where people have tried ,
C: Yeah .
A: so just to just to fill the space with some f few more speech enhancement algorithms to see whether it improves a lot , I I 've been working on this uh , signal subspace approach for speech enhancement
A: where you take the noisy signal and then decomposing the signal s and the noise subspace
A: and then try to estimate the clean speech from the signal plus noise subspace .
C: Mm - hmm .
A: And A: So , I 've been actually running some s
A: So far I 've been trying it only on Matlab .
A: I have to to to test whether it works first or not
C: Yeah .
A: and then I 'll p port it to C
A: and I 'll update it with the repository once I find it it giving any some positive result .
A: So , yeah .
C: S
C: So you s you So you said one thing I want to jump on for a second .
C: So so now you 're you 're getting tuned into the repository thing that he has here
A: Yeah .
C: and so we we 'll have a single place where the stuff is .
A: Yep .
A: Yeah .
C: Cool .
C: Um ,
C: so maybe uh , just briefly , you could remind us about the related experiments .
C: Cuz you did some stuff that you talked about last week ,
C: I guess ?
D: Mm - hmm .
C: Um , where you were also combining something C: both of you I guess were both combining something from the uh , French Telecom system with the u uh D: Right .
C: I I don't know whether it was system one or system two ,
C: or ?
D: Mm - hmm .
D: It was system one .
D: So
C: OK .
D: we D: The main thing that we did is just to take the spectral subtraction from the France Telecom ,
D: which provide us some speech samples that are uh , with noise removed .
C: So I let me let me just stop you there .
C: So then , one distinction is that uh , you were taking the actual France Telecom features and then applying something to A: Uh , no
A: there is a slight different .
A: Uh
A: I mean , which are extracted at the handset
C: Yeah .
A: because they had another back - end blind equalization C: Yeah .
C: But that 's what I mean .
A: Yeah .
A: Yeah .
C: But u u
C: Sorry ,
C: yeah , I 'm not being I 'm not being clear .
A: Yeah .
A: Yeah .
C: What I meant was you had something like cepstra or something , right ?
A: Yeah , yeah , yeah , yeah .
C: And so one difference is that , I guess you were taking spectra .
A: The speech .
B: Yeah .
D: Yeah .
D: But I guess it 's the s exactly the same thing
D: because on the heads uh , handset they just applied this Wiener filter and then compute cepstral features ,
D: right ?
A: Yeah ,
D: or ?
A: the cepstral f The difference is like There may be a slight difference in the way A: because they use exactly the baseline system for converting the cepstrum once you have the speech .
D: Right .
A: I mean , if we are using our own code for th I mean that that could be the only difference .
A: I mean , there is no other difference .
D: Mm - hmm .
A: Yeah .
C: But you got some sort of different result .
C: So I 'm trying to understand it .
C: But uh ,
D: Yeah , well I think we should uh , have a table with all the result
C: I th
D: because I don't know I uh , I don't exactly know what are your results ?
D: But ,
A: OK .
A: OK .
D: Mmm .
D: Yeah , but so we did this ,
D: and another difference I guess is that we just applied uh , proposal - one system after this
D: without well , with our modification to reduce the delay of the the LDA filters ,
D: and
A: Uh - huh .
D: Well there are slight modifications ,
B: And the filter D: but it was the full proposal - one .
D: In your case , if you tried just putting LDA , then maybe on - line normalization ?
A: Only LDA .
A: Yeah .
A: Af - I after that I added on - line normalization ,
A: yeah .
D: Mm - hmm .
D: So we just tried directly to to just , keep the system as it was
D: and ,
D: um ,
D: when we plug the spectral subtraction it improves uh , signif significantly .
D: Um ,
D: but , what seems clear also is that we have to retune the time constants of the on - line normalization .
A: Yeah , yeah .
A: Yeah .
D: Because if we keep the value that was submitted uh , it doesn't help at all .
D: You can remove on - line normalization , or put it , it doesn't change anything .
D: Uh , uh , as long as you have the spectral subtraction .
D: But , you can still find some kind of optimum somewhere ,
D: and we don't know where exactly
D: but ,
A: Yeah .
D: uh .
A: Yeah , I assume .
C: So it sounds like you should look at some tables of results or something
D: Right .
C: and see where i where the  where they were different and what we can learn from it .
D: Yeah .
A: Yeah .
D: Mm - hmm .
D: Mm - hmm .
A: without any change .
D: Yeah .
B: But it 's A: OK .
D: Well ,
B: It 's the new .
D: with with with changes ,
A: with
B: The new .
D: because we change it the system to have A: Oh yeah ,
A: I mean the the new LDA filters .
B: The new .
A: I mean D: Yeah .
A: OK .
D: LDA filters .
D: There are other things that we finally were shown to improve also
D: like , the sixty - four hertz cut - off .
B: Mm - hmm .
A: Mm - hmm .
D: w Uh , it doesn't seem to hurt on TI - digits , finally .
A: OK .
D: Maybe because of other changes .
A: OK .
D: Um ,
D: well there are some minor changes ,
D: yeah .
A: Mm - hmm .
D: And , right now if we look at the results , it 's , um , always better than it seems always better than France Telecom for mismatch and high - mismatch .
D: And it 's still slightly worse for well - matched .
D: Um ,
B: But
D: but this is not significant .
D: But , the problem is that it 's not significant ,
D: but if you put this in the , mmm , uh , spreadsheet , it 's still worse .
D: Even with very minor D: uh ,
D: even if it 's only slightly worse for well - matched .
C: Mm - hmm .
D: And significantly better for HM .
D: Uh ,
D: but , well .
D: I don't think it 's importa important
D: because when they will change their metric ,
D: uh ,
D: uh , mainly because of uh , when you p you plug the um , frame dropping in the baseline system , it will improve a lot HM , and MM ,
A: Yeah .
D: so ,
D: um , I guess what will happen D: I don't know what will happen .
D: But , the different contribution , I think , for the different test set will be more even .
A: Because the your improvement on HM and MM will also go down significantly in the spreadsheet
A: so .
A: But the the well - matched may still D: Mm - hmm .
A: I mean the well - matched may be the one which is least affected by adding the endpoint information .
C: Right .
A: Yeah .
A: So the A: the MM D: Mm - hmm .
A: MM and HM are going to be v hugely affected by it .
D: Yeah ,
D: so um ,
A: Yeah .
D: yeah .
A: Yeah .
A: But they d the everything I mean is like ,
A: but there
A: that 's how they reduce why they reduce the qualification to twenty - five percent or some something on .
D: Mm - hmm .
C: But are they changing the weighting ?
A: Uh , no ,
A: I guess they are going ahead with the same weighting .
D: Yeah .
A: Yeah .
A: So there 's nothing on C: I don't understand that .
C: I guess I I haven't been part of the discussion ,
A: Yeah .
C: so , um ,
C: it seems to me that the well - matched condition is gonna be unusual ,
C: in this case .
A: Usual .
C: Unusual .
A: Uh - huh .
C: Because , um , you don't actually have good matches ordinarily for what any @ @ particular person 's car is like ,
C: or
A: Mmm .
C: uh ,
A: Mmm .
C: It seems like something like the middle one is is more natural .
A: Hmm .
C: So I don't know why the well - matched is
A: Right .
C: uh D: Mm - hmm .
A: Yeah , but actually the well well the well - matched
A: um ,
A: uh ,
A: I mean the the well - matched condition is not like , uh , the one in TI - digits
A: where uh , you have all the training , uh , conditions exactly like replicated in the testing condition also .
A: It 's like , this is not calibrated by SNR or something .
A: The well - matched has also some some mismatch in that which is other than the C: The well wa matched has mismatch ?
A: has has also some slight mismatches ,
A: unlike the TI - digits where it 's like prefectly matched
F: Perfect to match .
C: Yeah .
A: because it 's artificially added noise .
C: Yeah .
A: But this is natural recording .
C: So remind me of what well - matched meant ?
C: You 've told me many times .
A: The the well - matched is like A: the the well - matched is defined like it 's seventy percent of the whole database is used for training and thirty percent for testing .
D: Yeah .
D: Well , so it means that if the database is large enough , it 's matched .
A: It 's it 's D: Because it
A: OK ,
A: it 's C: Yeah .
D: in each set you have a range of conditions D: Well C: Right .
C: So , I mean ,
C: yeah , unless they deliberately chose it to be different , which they didn't because they want it to be well - matched ,
C: it is pretty much C: You know , so it 's so it 's sort of saying if you F: It 's it 's not guaranteed though .
A: Yeah .
C: Uh , it 's not guaranteed .
A: Yeah .
C: Right .
D: Mm - hmm .
C: Right .
A: Yeah because the m the main major reason for the m
A: the main mismatch is coming from the amount of noise and the silence frames and all those present in the database actually .
C: Again , if you have enough if you have enough A: No
A: yeah , yeah .
A: Yeah .
C: So it 's sort of i i it 's sort of saying OK ,
C: so you much as you train your dictation machine for talking into your computer , um , you you have a car ,
C: and so you drive it around a bunch and and record noise conditions , or something ,
C: and then I don't think that 's very realistic ,
C: I mean I th
A: Mm - hmm .
C: I I you know ,
C: so I C: I I you know ,
C: I guess they 're saying that if you were a company that was selling the stuff commercially , that you would have a bunch of people driving around in a bunch of cars ,
C: and and you would have something that was roughly similar
C: and maybe that 's the argument ,
C: but I 'm not sure I buy it ,
C: so .
A: Yeah , yeah , yeah .
C: Uh ,
C: So
C: What else is going on ?
D: Mmm .
D: You
D: Yeah .
D: We are playing we are also playing , trying to put other spectral subtraction mmm , in the code .
D: Um ,
D: it would be a very simple spectral subtraction , on the um , mel energies
D: which I already tested but without the um frame dropping actually ,
D: and I think it 's important to have frame dropping if you use spectral subtraction .
F: Is it is spectral subtraction typically done on the after the mel , uh , scaling
F: or is it done on the FFT bins ?
D: Um ,
F: Does it matter ,
F: or ?
D: I d
D: I don't know .
D: Well , it 's both both uh , cases can i
F: Oh .
D: Yeah .
D: So - some of the proposal , uh , we 're doing this on the bin on the FFT bins ,
F: Hmm .
D: others on the um , mel energies .
D: You can do both ,
D: but I cannot tell you what 's which one might be better
D: or F: Hmm .
D: I A: I guess if you want to reconstruct the speech , it may be a good idea to do it on FFT bins .
D: I don't know .
F: Mmm .
D: Yeah ,
D: but
A: But for speech recognition , it may not .
A: I mean it may not be very different if you do it on mel warped or whether you do it on FFT .
F: I see .
A: So you 're going to do a linear weighting anyway after that .
A: Well A: Yeah ?
F: Hmm .
A: So , it may not be really a big different .
D: Well , it gives something different ,
D: but I don't know what are the , pros and cons of both .
A: It
A: I Uh - huh .
C: Hmm .
A: So
C: OK .
A: The other thing is like when you 're putting in a speech enhancement technique ,
A: uh ,
A: is it like one stage speech enhancement ?
A: Because everybody seems to have a mod two stages of speech enhancement in all the proposals ,
A: which is really giving them some improvement .
D: Yeah .
B: Mm - hmm .
D: Mm - hmm .
A: I mean they just do the same thing again once more .
C: Mm - hmm .
A: And So , there 's something that is good about doing it A: I mean , to cleaning it up once more .
D: Yeah , it might be .
D: Yeah .
A: Yeah ,
A: so we can D: So maybe in my implementation I should also try to inspire me from this kind of thing
A: Yeah .
A: That 's what
D: and Yeah .
C: Well , the other thing would be to combine what you 're doing .
C: I mean maybe one or one or the other of the things that you 're doing would benefit from the other happening first .
A: That 's wh
A: Yeah .
A: So ,
C: Right ,
C: so he 's doing a signal subspace thing ,
C: maybe it would work better if you 'd already done some simple spectral subtraction ,
C: or maybe vi maybe the other way around ,
D: Yeah ,
D: mm - hmm .
A: Yeah .
C: you know ?
D: Mm - hmm .
A: So I 've been thinking about combining the Wiener filtering with signal subspace ,
A: I mean just to see all some some such permutation combination to see whether it really helps or not .
D: Mm - hmm .
D: Mm - hmm .
D: Mm - hmm .
D: Yeah .
D: Yeah .
C: How is it C: I I guess I 'm ignorant about this ,
C: how does C: 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 ?
A: The signal subspace ?
C: Yeah .
A: The A: The signal subspace approach has actually an in - built Wiener filtering in it .
C: Oh , OK .
A: Yeah .
A: It is like a KL transform followed by a Wiener filter .
A: Is the signal is is a signal substrate .
C: Oh , oh , OK
C: so the difference is the KL .