Professor A: but I 'm I 'm just wondering if um  on the list of things that there are to do , if there are things that we won't do because we 've got two groups doing the same thing .
PhD F: Mm - hmm .
Professor A: Um . That 's PhD F: Mm - hmm .
Professor A: Um . Just just asking . Uh . I mean , it 's PhD F: Yeah , well ,
PhD B: There also could be I mean . I can maybe see a reason f for both working on it too
PhD F: uh .
PhD B: if um you know , if if if you work on something else and and you 're waiting for them to give you spectral subtraction I mean it 's hard to know whether the effects that you get from the other experiments you do will carry over once you then bring in their spectral subtraction module . So it 's it 's almost like everything 's held up waiting for this one thing . I don't know if that 's true or not , but I could see how PhD F: Mmm .
Professor A: I don't know .
PhD B: Maybe that 's what you were thinking .
Professor A: I don't know . I mean , we still evidently have a latency reduction plan which which isn't quite what you 'd like it to be . That that seems like one prominent thing . And then uh weren't issues of of having a a second stream or something ? That was Was it There was this business that , you know , we we could use up the full forty - eight hundred bits , and PhD F: Yeah . But I think they ' I think we want to work on this . They also want to work on this , so . Uh . yeah . We we will try MSG , but um , yeah . And they are t I think they want to work on the second stream also , but more with some kind of multi - band or , well , what they call TRAP or generalized TRAP .
Professor A: Mm - hmm .
PhD F: Um . So .
Professor A: OK . Do you remember when the next meeting is supposed to be ? the next uh PhD F: It 's uh in June .
Professor A: In June . OK .
PhD F: Yeah .
Professor A: Yeah . Um . Yeah , the other thing is that you saw that that mail about uh the VAD V A Ds performing quite differently ? That that uh So um . This there was this experiment of uh " what if we just take the baseline ? "
PhD F: Mmm .
Professor A: set uh of features , just mel cepstra , and you inc incorporate the different V A And it looks like the the French VAD is actually uh better significantly better .
PhD B: Improves the baseline ?
Professor A: Yeah . Yeah .
PhD F: Yeah but I don't know which VAD they use . Uh . If the use the small VAD I th I think it 's on I think it 's easy to do better because it doesn't work at all . So . I I don't know which which one . It 's Pratibha that that did this experiment .
PhD D: Yeah .
PhD F: Um . We should ask which VAD she used .
PhD D: I don't @ @ . He Actually , I think that he say with the good VAD of from OGI and with the Alcatel VAD . And the experiment was sometime better , sometime worse .
PhD F: Yeah but I it 's uh I think you were talking about the other mail that used VAD on the reference features .
Professor A: Yes .
PhD F: Yeah .
Professor A: And on that one , uh the French one is was better .
PhD D: I don't remember .
Professor A: It was just better .
PhD D: Mm - hmm .
Professor A: I mean it was enough better that that it would uh account for a fair amount of the difference between our performance , actually .
PhD F: Mm - hmm .
PhD D: Mm - hmm .
Professor A: So . Uh . So if they have a better one , we should use it . I mean . You know ? it 's you can't work on everything .
PhD F: Yeah .
Professor A: Uh . Uh . Yeah .
PhD F: Yeah , so we should find out if it 's really better . I mean if it the compared to the small or the big network .
PhD D: Mm - hmm .
Professor A: Yeah .
PhD F: And perhaps we can easily improve if if we put like mean normalization before the before the VAD . Because  as as you 've mentioned .
Professor A: Yeah .
PhD F: Mmm .
Professor A: H Hynek will be back in town uh the week after next , back back in the country . So . And start start organizing uh more visits and connections and so forth ,
PhD F: Mm - hmm .
Professor A: and uh working towards June .
PhD F: Yeah .
PhD D: Also is Stephane was thinking that maybe it was useful to f to think about uh voiced - unvoiced PhD F: Mm - hmm .
PhD D: to work uh here in voiced - unvoiced detection .
PhD F: Yeah . Yeah .
PhD D: And we are looking  in the uh signal .
PhD F: Yeah , my feeling is that um actually when we look at all the proposals , ev everybody is still using some kind of spectral envelope
Professor A: Right .
PhD F: and um it 's Professor A: No use of pitch uh basically . Yeah .
PhD F: Yeah , well , not pitch , but to look at the um fine at the at the high re high resolution spectrum .
Professor A: Yeah . Well , it PhD F: So . We don't necessarily want to find the the pitch of the of the sound but uh Cuz I have a feeling that when we look when we look at the just at the envelope there is no way you can tell if it 's voiced and unvoiced , if there is some It 's it 's easy in clean speech because voiced sound are more low frequency and . So there would be more ,
Professor A: Yeah .
PhD F: uh there is the first formant , which is the larger and then voiced sound are more high frequencies cuz it 's frication and Professor A: Right .
PhD F: But , yeah . When you have noise there is no um  if if you have a low frequency noise it could be taken for for voiced speech and .
Professor A: Yeah , you can make these mistakes ,
PhD F: So .
Professor A: but but PhD B: Isn't there some other
PhD F: S
PhD B: uh d
PhD F: So I think that it it would be good Yeah , yeah , well , go go on .
PhD B: Uh , I was just gonna say isn't there  aren't aren't there lots of ideas for doing voice activity , or speech - nonspeech rather , {comment} um by looking at um , you know , uh I guess harmonics or looking across time Professor A: Well , I think he was talking about the voiced - unvoiced , though ,
PhD F: Mmm .
Professor A: right ? So , not the speech - nonspeech .
PhD B: Yeah . Well even with e
Professor A: Yeah .
PhD B: uh w ah you know , uh even with the voiced - non voiced - unvoiced
PhD F: Mmm .
PhD B: um I thought that you or somebody was talking about Professor A: Well . Uh yeah . B We should let him finish what he w he was gonna say ,
PhD F: So .
PhD B: OK .
Professor A: and PhD B: So go ahead .
PhD F: Um yeah , so yeah , I think if we try to develop a second stream well , there would be one stream that is the envelope and the second , it could be interesting to have that 's something that 's more related to the fine structure of the spectrum . And . Yeah , so I don't know . We were thinking about like using ideas from from Larry Saul , have a good voice detector , have a good , well , voiced - speech detector , that 's working on on the FFT and uh
Professor A: U
PhD F: Larry Saul could be an idea . We were are thinking about just kind of uh taking the spectrum and computing the variance of of the high resolution spectrum and things like this .
Professor A: So u s u OK . So So many tell you something about that . Uh we had a guy here some years ago who did some work on um making use of voicing information uh to help in reducing the noise .
PhD F: Yeah ?
Professor A: So what he was doing is basically y you  you do estimate the pitch .
PhD F: Mm - hmm .
Professor A: And um you from that you you estimate or you estimate fine harmonic structure , whichev ei either way , it 's more or less the same . But uh the thing is that um you then can get rid of things that are not i if there is strong harmonic structure , you can throw away stuff that 's that 's non - harmonic .
PhD F: Mm - hmm . Mm - hmm .
Professor A: And that that is another way of getting rid of part of the noise
PhD F: Yeah .
Professor A: So um that 's something that is sort of finer ,
PhD F: Yeah .
Professor A: brings in a little more information than just spectral subtraction . Um .
PhD F: Mm - hmm .
Professor A: And he had some I mean , he did that sort of in combination with RASTA . It was kind of like RASTA was taking care of convolutional stuff
PhD F: Mmm .
Professor A: and he was PhD F: Mm - hmm .
Professor A: and and got some some decent results doing that . So that that 's another another way . But yeah , there 's there 's PhD F: Yeah . Mmm .
Professor A: Right . There 's all these cues . We 've actually back when Chuck was here we did some voiced - unvoiced uh classification using a bunch of these ,
PhD F: But Professor A: and and uh works OK . Obviously it 's not perfect but um PhD F: Mm - hmm .
Professor A: But the thing is that you can't given the constraints of this task , we can't , in a very nice way , feed forward to the recognizer the information the probabilistic information that you might get about whether it 's voiced or unvoiced , where w we can't you know affect the  the uh distributions or anything .
PhD F: Mm - hmm .
Professor A: But we what we uh I guess we could Yeah .
PhD B: Didn't the head dude send around that message ? Yeah , I think you sent us all a copy of the message , where he was saying that I I 'm not sure , exactly , what the gist of what he was saying , but something having to do with the voice activity detector and that it will  that people shouldn't put their own in or something . It was gonna be a Professor A: That But OK . So that 's voice activity detector as opposed to voicing detector .
PhD F: They didn't .
Professor A: So we 're talking about something a little different .
PhD F: Mmm .
PhD B: Oh , I 'm sorry .
Professor A: Right ?
PhD B: I I missed that .
PhD F: Mmm .
Professor A: I guess what you could do , maybe this would be w useful , if if you have if you view the second stream , yeah , before you before you do KLT 's and so forth , if you do view it as probabilities , and if it 's an independent So , if it 's if it 's uh not so much envelope - based by fine - structure - based , uh looking at harmonicity or something like that , um if you get a probability from that information and then multiply it by you know , multiply by all the voiced outputs and all the unvoiced outputs , you know , then use that as the
PhD F: Mm - hmm .
Professor A: uh take the log of that or uh pre pre uh pre - nonlinearity ,
PhD F: Yeah . i if Professor A: uh and do the KLT on the on on that ,
PhD F: Yeah .
Professor A: then that would that would I guess be uh a reasonable use of independent information . So maybe that 's what you meant . And then that would be PhD F: Yeah , well , I was not thinking this yeah , this could be an yeah So you mean have some kind of probability for the v the voicing
Professor A: R Right . So you have a second neural net .
PhD F: and then use a tandem system
Professor A: It could be pretty small . Yeah . If you have a tandem system and then you have some kind of it can be pretty small net PhD F: Mm - hmm .
Professor A: we used we d did some of this stuff . Uh I I did , some years ago ,
PhD F: Yeah .
Professor A: and the and and you use  the thing is to use information primarily that 's different as you say , it 's more fine - structure - based than than envelope - based
PhD F: Mm - hmm .
Professor A: uh so then it you you you can pretty much guarantee it 's stuff that you 're not looking at very well with the other one , and uh then you only use for this one distinction .
PhD F: Alright .
Professor A: And and so now you 've got a probability of the cases , and you 've got uh the probability of the finer uh categories on the other side . You multiply them where appropriate and uh um
PhD F: I see , yeah . Mm - hmm .
Professor A: if they really are from independent information sources then they should have different kinds of errors
PhD F: Mm - hmm .
Professor A: and roughly independent errors , and it 's a good choice for PhD F: Mm - hmm . Mm - hmm . Yeah .
Professor A: Uh . Yeah , that 's a good idea .
PhD F: Yeah . Because , yeah , well , spectral subtraction is good and we could u we could use the fine structure to to have a better estimate of the noise but still there is this issue with spectral subtraction that it seems to increase the variance of of of
Professor A: Yeah .
PhD F: um Well it 's this musical noise which is annoying if you d you do some kind of on - line normalization after .
Professor A: Right .
PhD F: So . Um . Yeah . Well . Spectral subtraction and on - line normalization don't seem to to go together very well . I
Professor A: Or if you do a spectral subtraction do some spectral subtraction first and then do some on - line normalization then do some more spectral subtraction I mean , maybe maybe you can do it layers or something so it doesn't doesn't hurt too much or something .
PhD F: Ah , yeah .
Professor A: But it but uh , anyway I think I was sort of arguing against myself there by giving that example
PhD F: Yeah .
Professor A: uh I mean cuz I was already sort of suggesting that we should be careful about not spending too much time on exactly what they 're doing In fact if you get if you go into uh a uh harmonics - related thing it 's definitely going to be different than what they 're doing and uh uh
PhD F: Mm - hmm .
Professor A: should have some interesting properties in noise . Um . I know that when have people have done um sort of the obvious thing of taking uh your feature vector and adding in some variables which are pitch related or uh that it hasn't my impression it hasn't particularly helped . Uh . Has not .
PhD F: It it i has not ,
Professor A: Yeah .
PhD F: yeah .
Professor A: But I think uh that 's that 's a question for this uh you know extending the feature vector versus having different streams .
PhD F: Oh . Was it nois noisy condition ? the example that you you just
Professor A: And and it may not have been noisy conditions .
PhD F: Yeah .
Professor A: Yeah . I I don't remember the example but it was  it was on some DARPA data and some years ago and so it probably wasn't , actually
PhD F: Mm - hmm . Mm - hmm . Yeah . But we were thinking , we discussed with Barry about this , and perhaps thinking we were thinking about some kind of sheet cheating experiment where we would use TIMIT
Professor A: Uh - huh .
PhD F: and see if giving the d uh , this voicing bit would help in in terms of uh frame classification .
Professor A: Why don't you why don't you just do it with Aurora ?
PhD F: Mmm .
Professor A: Just any i in in each in each frame
PhD F: Yeah , but but B but we cannot do the cheating , this cheating thing .
Grad E: We 're Professor A: uh Grad E: We need labels .
Professor A: Why not ?
PhD F: Well . Cuz we don't have Well , for Italian perhaps we have , but we don't have this labeling for Aurora . We just have a labeling with word models
Professor A: I see .
PhD F: but not for phonemes .
PhD D: Not for foreigners .
Grad E: we don't have frame frame level transcriptions .
Professor A: Um .
PhD D: Right .
PhD F: Um . Yeah .
Professor A: But you could I mean you can you can align so that It 's not perfect , but if you if you know what was said and PhD B: But the problem is that their models are all word level models . So there 's no phone models that you get alignments for .
PhD F: Mm - hmm .
Professor A: Oh .
PhD B: You So you could find out where the word boundaries are but that 's about it .
Professor A: Yeah . I see .
Grad E: S But we could use uh the the noisy version that TIMIT , which you know , is similar to the the noises found in the TI - digits um portion of Aurora .
PhD F: Yeah . noise , yeah . Yeah , that 's right , yep . Mmm .
Professor A: Yeah .
PhD F: Well , I guess I guess we can we can say that it will help , but I don't know . If this voicing bit doesn't help , uh , I think we don't have to to work more about this because Professor A: Uh .
PhD F: Uh . It 's just to know if it how much i it will help
Professor A: Yeah .
PhD F: and to have an idea of how much we can gain .
Professor A: Right . I mean in experiments that we did a long time ago
PhD F: Mmm .
Professor A: and different ta it was probably Resource Management or something , um , I think you were getting something like still eight or nine percent error on the voicing , as I recall . And um , so um
Grad E: Another person 's voice .
Professor A: what that said is that , sort of , left to its own devices , like without the a strong language model and so forth , that you would  you would make significant number of errors just with your uh probabilistic machinery in deciding
PhD B: It also Professor A: one oh
PhD B: Yeah , the though I think uh there was one problem with that in that , you know , we used canonical mapping so our truth may not have really been true to the acoustics .
Professor A: Uh - huh .
Grad E: Hmm .
PhD B: So .
PhD F: Mmm .