PhD A: All set ? Do you want to go , Stephane ?
PhD C: Um . Yeah . Maybe , concerning these d still , these meeting digits . I 'm more interested in trying to figure out what 's still the difference between the SRI system and the Aurora system . And Um . Yeah . So , I think I will maybe train , like , gender - dependent models , because this is also one big difference between the two systems . Um , the other differences were the fact that maybe the acoustic models of the SRI are more SRI system are more complex . But , uh , Chuck , you did some experiments with this and
PhD A: It didn't seem to help in the HTK system .
PhD C: it was hard t to to have some exper some improvement with this . Um .
Professor B: Well , it sounds like they also have he he 's saying they have all these , uh , uh , different kinds of adaptation .
PhD C: Mm - hmm .
Professor B: You know , they have channel adaptation . They have speaker adaptation .
PhD C: Yeah . Right .
PhD A: Well , there 's also the normalization .
Professor B: Yeah . Yeah .
PhD C: Yeah .
Grad F: Yeah .
PhD A: Like they do , um I 'm not sure how they would do it when they 're working with the digits ,
PhD C: The vocal tr
PhD A: but , like , in the Switchboard data , there 's , um conversation - side normalization for the non - C - zero components ,
PhD C: Yeah . Yeah . This is another difference . Their normalization works like on on the utterance levels .
PhD A: Mm - hmm .
PhD C: But we have to do it We have a system that does it on - line .
PhD A: Right .
PhD C: So , it might be it might be better with it might be worse if the channel is constant ,
PhD A: Yeah .
PhD C: or Nnn .
PhD G: And the acoustic models are like - k triphone models or or is it the whole word ?
PhD C: SRI it 's it 's tr
Grad F: SRI .
PhD G: Yeah .
PhD C: Yeah . I guess it 's triphones .
PhD G: It 's triphone .
Professor B: I think it 's probably more than that .
PhD C: Huh .
Professor B: I mean , so they they have I I thin think they use these , uh , uh , genone things . So there 's there 's these kind of , uh , uh , pooled models and and they can go out to all sorts of dependencies .
PhD G: Oh . It 's like the tied state .
Professor B: So .
PhD A: Mm - hmm .
Professor B: They have tied states and I think I I I don't real I 'm talk I 'm just guessing here . But I think I think they they don't just have triphones .
PhD G: OK .
Professor B: I think they have a range of of , uh , dependencies .
PhD C: Mm - hmm .
PhD G: Mm - hmm .
PhD C: Mm - hmm .
Grad F: Hmm .
PhD C: And Yeah . Well . Um . Well , the first thing I that I want to do is just maybe these gender things . Uh . And maybe see with Andreas if Well , I I don't know how much it helps , what 's the model .
PhD A: So so the n stuff on the numbers you got , the two point seven , is that using the same training data that the SRI system used and got one point two ?
PhD C: That 's right . So it 's the clean TI - digits training set .
PhD A: So exact same training data ?
PhD C: Right .
PhD A: OK .
PhD C: Mm - hmm . I guess you used the clean training set .
Grad E: Right .
PhD C: Mm - hmm .
Grad E: For with the SRI system PhD C: Well .
Grad E: You know , the the Aurora baseline is set up with these , um  this version of the clean training set that 's been filtered with this G - seven - one - two filter , and , um , to train the SRI system on digits S - Andreas used the original TI - digits , um , under U doctor - speech data TI - digits , which don't have this filter . But I don't think there 's any other difference .
PhD C: Mm - hmm . Mm - hmm . Yeah .
Professor B: So is that ? Uh , are are these results comparable ? So you you were getting with the , uh , Aurora baseline something like two point four percent on clean TI - digits , when , uh , training the SRI system with clean TR digits {comment} TI - digits . Right ? And Grad E: Um . Uh - huh .
Professor B: Yeah . And , so , is your two point seven comparable , where you 're , uh , uh , using , uh , the submitted system ?
PhD C: Yeah . I think so .
Professor B: OK .
PhD C: Yeah .
Professor B: So it 's about the same ,
PhD C: Mm - hmm .
Professor B: maybe a little worse .
Grad E: W w it was one one point two
PhD C: Ye
Grad E: with the SRI system ,
Professor B: I 'm sorry .
PhD C: Yeah .
Grad E: I PhD C: The complete SRI system is one point two .
Professor B: You you were HTK .
PhD C: Yeah .
Professor B: Right ? OK . That 's right . So PhD C: Mm - hmm .
Professor B: OK , so the comparable number then , uh for what you were talking about then , since it was HTK , would be the um , two point f
PhD C: It was four point something . Right ? The HTK system with , uh , b
Grad E: D d
Professor B: Oh , right , right , right , right .
PhD C: MFCC features Grad E: Do you mean the b ? The baseline Aurora - two system , trained on TI - digits , tested on Meeting Recorder near , I think we saw in it today , and it was about six point six percent .
Professor B: Right . Right , right , right .
PhD C: Oh .
Professor B: OK . Alright . So He 's doing some different things .
PhD C: So Yeah . The only difference is the features , right now , between this and Professor B: Yes . OK , good . So they are helping .
PhD C: Mm - hmm .
Professor B: That 's good to hear . Yeah .
PhD C: They are helping . Yeah . Um . Yeah . And another thing I I maybe would like to do is to just test the SRI system that 's trained on Macrophone test it on , uh , the noisy TI - digits ,
Professor B: Yeah .
PhD C: cuz I 'm still wondering where this improvement comes from . When you train on Macrophone , it seems better on meeting digits . But I wonder if it 's just because maybe Macrophone is acoustically closer to the meeting digits than than TI - digit is , which is TI - digits are very clean recorded digits
Professor B: Mm - hmm .
PhD C: and PhD A: You know , it would also be interesting to see , uh to do the regular Aurora test ,
PhD C: Uh , f s
PhD A: um , but use the SRI system instead of HTK .
PhD C: That 's Yeah . That 's what I wanted , just , uh Yeah . So , just using the SRI system , test it on and test it on Aurora TI - digits . Right .
PhD A: Why not the full Aurora , uh , test ?
PhD C: Um . Yeah . There is this problem of multilinguality yet .
PhD A: Mm - hmm .
PhD C: So we don't Professor B: You 'd have to train the SRI system with with all the different languages .
PhD C: i i
PhD A: Right .
PhD C: We would have to train on PhD A: Yeah . That 's what I mean .
PhD C: Yeah .
PhD A: So , like , comple
Professor B: It 'd be a lot of work . That 's the only thing .
PhD C: Yeah .
PhD A: Mmm .
PhD C: It 's PhD A: Well , I mean ,
PhD C: Mmm .
PhD A: uh , uh , I guess the work would be into getting the the files in the right formats , or something . Right ? I mean PhD C: Mm - hmm .
PhD A: Because when you train up the Aurora system , you 're , uh you 're also training on all the data .
PhD C: That 's right .
PhD A: I mean , it 's PhD C: Yeah . Yeah . I see . Oh , so , OK . Right . I see what you mean .
Professor B: That 's true , but I think that also when we 've had these meetings week after week , oftentimes people have not done the full arrange of things
PhD A: Mm - hmm .
Professor B: because on on whatever it is they 're trying , because it 's a lot of work , even just with the HTK .
PhD A: Mm - hmm .
Professor B: So , it 's it 's a good idea , but it seems like it makes sense to do some pruning
PhD A: Mm - hmm .
Professor B: first with a a test or two that makes sense for you ,
PhD A: Yeah .
Professor B: and then take the likely candidates and go further .
PhD A: Yeah .
PhD C: Mm - hmm . Yeah . But , just testing on TI - digits would already give us some information about what 's going on . And mm - hmm . Uh , yeah . OK . Uh , the next thing is this this VAD problem that , um , um So , I 'm just talking about the the curves that I I sent  I sent you so , whi that shows that when the SNR decrease , uh , the current VAD approach doesn't drop much frames for some particular noises , uh , which might be then noises that are closer to speech , uh , acoustically .
Professor B: I i Just to clarify something for me . I They were supp Supposedly , in the next evaluation , they 're going to be supplying us with boundaries .
PhD C: Mm - hmm .
Professor B: So does any of this matter ? I mean , other than our interest in it . Uh PhD C: Uh Well . First of all , the boundaries might be , uh like we would have t two hundred milliseconds or before and after speech . Uh . So removing more than that might still make a difference in the results .
Professor B: Do we ? I mean , is there some reason that we think that 's the case ?
PhD C: And No . Because we don't didn't looked that much at that .
Professor B: Yeah .
PhD C: But , still , I think it 's an interesting problem .
Professor B: Oh , yeah .
PhD C: And Um . Yeah .
Professor B: But maybe we 'll get some insight on that when when , uh , the gang gets back from Crete . Because there 's lots of interesting problems , of course .
PhD C: Mm - hmm .
Professor B: And then the thing is if if they really are going to have some means of giving us fairly tight , uh , boundaries , then that won't be so much the issue .
PhD C: Yeah , yeah . Mm - hmm . Mm - hmm .
Professor B: Um But I don't know .
PhD G: Because w we were wondering whether that VAD is going to be , like , a realistic one or is it going to be some manual segmentation . And then , like , if if that VAD is going to be a realistic one , then we can actually use their markers to shift the point around , I mean , the way we want
Professor B: Mm - hmm .
PhD G: to find a I mean , rather than keeping the twenty frames , we can actually move the marker to a point which we find more suitable for us .
Professor B: Right .
PhD G: But if that is going to be something like a manual , uh , segmenter , then we can't use that information anymore ,
PhD C: Mm - hmm .
PhD G: because that 's not going to be the one that is used in the final evaluation .
Professor B: Right .
PhD G: So . We don't know what is the type of   VAD which they 're going to provide .
Professor B: Yeah .
PhD C: Yeah . And actually there 's Yeah . There 's an uh , I think it 's still for even for the evaluation , uh , it might still be interesting to work on this because the boundaries apparently that they would provide is just , um , starting of speech and end of speech uh , at the utterance level . And Um .
PhD G: With some some gap .
PhD C: So PhD G: I mean , with some pauses in the center , provided they meet that whatever the hang - over time which they are talking .
PhD C: Yeah . But when you have like , uh , five or six frames , both PhD G: Yeah . Then the they will just fill fill it up .
PhD C: it it with PhD G: I mean , th Yeah .
PhD C: Yeah .
Professor B: So if you could get at some of that , uh PhD C: So Professor B: although that 'd be hard .
PhD C: Yeah . It might be useful for , like , noise estimation , and a lot of other things that we want to work on .
Professor B: But but Yeah .
PhD G: Yeah .
Professor B: Right . OK .
PhD C: But Mmm . Yeah . So I did I just started to test putting together two VAD which was was not much work actually . Um , I im re - implemented a VAD that 's very close to the , um , energy - based VAD that , uh , the other Aurora guys use . Um . So , which is just putting a threshold on the noise energy ,
Professor B: Mm - hmm .
PhD C: and , detect detecting the first group of four frames that have a energy that 's above this threshold , and , uh , from this point , uh , tagging the frames there as speech . So it removes the first silent portion portion of each utterance . And it really removes it , um , still o on the noises where our MLP VAD doesn't work a lot .
Professor B: Mmm .
PhD C: Uh ,
Professor B: Cuz I would have thought that having some kind of spectral information ,
PhD C: and Professor B: uh uh , you know , in the old days people would use energy and zero crossings , for instance uh , would give you some better performance . Right ? Cuz you might have low - energy fricatives or or , uh stop consonants , or something like that .
PhD C: Mm - hmm .
Professor B: Uh .
PhD C: Yeah . So , your point is will be to u use whatever Professor B: Oh , that if you d if you use purely energy and don't look at anything spectral , then you don't have a good way of distinguishing between low - energy speech components and nonspeech . And , um ,
PhD C: Mm - hmm .
Professor B: just as a gross generalization , most nonsp many nonspeech noises have a low - pass kind of characteristic , some sort of slope . And and most , um , low - energy speech components that are unvoiced have a a high - pass kind of characteristic PhD C: Mm - hmm .
Professor B: an upward slope . So having some kind of a PhD C: Yeah .
Professor B: uh , you know , at the beginning of a of a of an S sound for instance , just starting in , it might be pretty low - energy ,
PhD C: Mm - hmm .
Professor B: but it will tend to have this high - frequency component . Whereas , a a lot of rumble , and background noises , and so forth will be predominantly low - frequency . Uh , you know , by itself it 's not enough to tell you , but it plus energy is sort of PhD C: Yeah .
Professor B: it plus energy plus timing information is sort of PhD C: Mm - hmm .
Professor B: I mean , if you look up in Rabiner and Schafer from like twenty - five years ago or something , that 's sort of what they were using then .
PhD C: Mm - hmm .
Professor B: So it 's it 's not a PhD C: Mm - hmm .
Grad F: Hmm .
PhD C: So , yeah . It it might be that what I did is so , removes like low , um , uh low - energy , uh , speech frames . Because the way I do it is I just I just combine the two decisions so , the one from the MLP and the one from the energy - based with the with the and operator . So , I only keep the frames where the two agree that it 's speech . So if the energy - based dropped dropped low - energy speech , mmm , they they are they are lost . Mmm .
Professor B: Mm - hmm .
PhD C: But s still , the way it 's done right now it it helps on on the noises where it seems to help on the noises where our VAD was not very good .
Professor B: Well , I guess I mean , one could imagine combining them in different ways . But but , I guess what you 're saying is that the the MLP - based one has the spectral information . So .
PhD C: Yeah . But Yeah . But the way it 's combined wi is maybe done Well , yeah .
Professor B: Well , you can imagine PhD C: The way I use a an a " AND " operator is So , it I , uh Professor B: Is ?
PhD C: The frames that are dropped by the energy - based system are are , uh , dropped , even if the , um , MLP decides to keep them .
Professor B: Right . Right . And that might not be optimal ,
PhD C: But , yeah .
Professor B: but PhD C: Mm - hmm .
PhD A: No
Professor B: but I mean , I guess in principle what you 'd want to do is have a  uh , a probability estimated by each one and and put them together .
PhD C: Yeah . Mmm . M Yeah .
PhD A: Something that that I 've used in the past is , um when just looking at the energy , is to look at the derivative . And you make your decision when the derivative is increasing for so many frames . Then you say that 's beginning of speech .
PhD C: Uh - huh .
PhD A: But , I 'm I 'm trying to remember if that requires that you keep some amount of speech in a buffer . I guess it depends on how you do it . But I mean , that 's that 's been a useful thing .
Professor B: Yeah .
PhD C: Mm - hmm .
Grad F: Mm - hmm .
PhD G: Yeah . Well , every everywhere has a delay associated with it . I mean , you still have to k always keep a buffer ,
PhD A: Mm - hmm .
PhD G: then only make a decision because you still need to smooth the decision further .
PhD A: Right . Right .
PhD G: So that 's always there .
PhD A: Yeah . OK .
PhD C: Well , actually if I don't maybe don't want to work too much of on it right now . I just wanted to to see if it 's  what I observed was the re was caused by this this VAD problem .
Professor B: Mm - hmm .
PhD C: And it seems to be the case . Um . Uh , the second thing is the this spectral subtraction . Um . Um , which I 've just started yesterday to launch a bunch of , uh , {nonvocalsound} twenty - five experiments , uh , with different , uh , values for the parameters that are used . So , it 's the Makhoul - type spectral subtraction which use an over - estimation factor . So , we substr I subtract more ,  um , {nonvocalsound} noise than the noise spectra that is estimated on the noise portion of the s uh , the utterances . So I tried several , uh , over - estimation factors . And after subtraction , I also add a constant noise , and I also try different , uh , noise , uh , values and we 'll see what happen .
Professor B: Hmm . OK .
PhD C: Mm - hmm . Mm - hmm . But st still when we look at the , um Well , it depends on the parameters that you use , but for moderate over - estimation factors and moderate noise level that you add , you st have a lot of musical noise . Um . On the other hand , when you subtract more and when you add more noise , you get rid of this musical noise but maybe you distort a lot of speech . So . Well . Mmm . Well , it until now , it doesn't seem to help . But We 'll see . So the next thing , maybe I what I will try to to do is just to try to smooth mmm , the , um to smooth the d the result of the subtraction , to get rid of the musical noise , using some kind of filter , or PhD G: Can smooth the SNR estimate , also .
PhD C: Yeah . Right . Mmm .
PhD G: Your filter is a function of SNR . Hmm ?
PhD C: Yeah . So , to get something that 's would be closer to what you tried to do with Wiener filtering .
PhD G: Yeah .
PhD C: And Mm - hmm . Yeah .
PhD G: Actually , it 's , uh Uh . I don't know , it 's go ahead .
PhD C: It PhD G: And it 's PhD C: Maybe you can PhD G: go ahead .
PhD C: I think it 's That 's it for me .