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