B: I get it .
A: Um A: So , yeah .
A: Uh .
A: Yeah , but it 's a good thing anyway to have shorter delay .
A: Then we tried , um , to do something like proposal - two
A: but having , um , e using also MSG features .
A: So there is this KLT part , which use just the standard features ,
B: Mm - hmm .
B: Right .
A: and then two neura two neural networks .
B: Mm - hmm .
A: Mmm ,
A: and it doesn't seem to help .
A: Um , however , we just have one result ,
A: which is the Italian mismatch ,
A: so .
A: Uh .
A: We have to wait for that to fill the whole table ,
A: but B: OK .
B: There was a start of some effort on something related to voicing or something .
B: Is that ?
A: Yeah .
A: Um , yeah .
A: So basically we try to ,  uh , find good features that could be used for voicing detection ,
A: uh , but it 's still , uh on the ,
A: um F: Oh , well , I have the picture .
A: t
A: we w basically we are still playing with Matlab to  to look at at what happened ,
C: What sorts of F: Yeah .
A: and C: what sorts of features are you looking at ?
F: We have some A: So we would be looking at , um , the variance of the spectrum of the excitation ,
F: uh , um , this , this , and this .
A: something like this ,
A: which is should be high for voiced sounds .
A: Uh ,
C: Wait a minute .
C: I what does that mean ?
A: we C: The variance of the spectrum of excitation .
A: Yeah .
A: So the A: So basically the spectrum of the excitation for a purely periodic sig signal shou sh
B: OK .
B: Yeah , w
B: what yo what you 're calling the excitation , as I recall , is you 're subtracting the the , um the mel mel  mel filter , uh , spectrum from the FFT spectrum .
A: e
A: That 's right .
A: Yeah .
B: Right .
A: So F: Mm - hmm .
A: Yeah .
A: So we have the mel f filter bank ,
A: we have the FFT ,
A: so we just B: So it 's it 's not really an excitation ,
B: but it 's something that hopefully tells you something about the excitation .
A: No .
A: Yeah , that 's right .
B: Yeah ,
B: yeah .
A: Um A: Yeah .
F: We have here some histogram ,
F: but they have a lot of overlap .
A: E yeah ,
A: but it 's it 's still A: Yeah .
A: So ,
A: well ,
A: for unvoiced portion we have something tha that has a mean around O point three ,
A: and for voiced portion the mean is O point fifty - nine .
A: But the variance seem quite high .
C: How do you know ?
A: So A: Mmm .
C: How did you get your voiced and unvoiced truth data ?
A: We used , uh , TIMIT
A: and we used canonical mappings between the phones
F: Yeah .
F: We , uh , use TIMIT on this ,
A: and
F: for A: th Yeah .
F: But if we look at it in one sentence , it apparently it 's good ,
F: I think .
A: Yeah ,
A: but A: Yeah .
A: Uh , so it 's noisy TIMIT .
A: That 's right .
E: It 's noisy TIMIT .
F: Yeah .
A: Yeah .
A: It seems quite robust to noise ,
A: so when we take we draw its parameters across time for a clean sentence and then nois the same noisy sentence , it 's very close .
B: Mm - hmm .
A: Yeah . So there are there is this .
A: There could be also the , um  something like the maximum of the auto - correlation function
A: or C: Is this a a s a trained system ?
A: which C: Or is it a system where you just pick some thresholds ?
C: Ho - how does it work ?
A: Right now we just are trying to find some features .
C: Mm - hmm .
A: And ,
A: uh A: Yeah . Hopefully , I think what we want to have is to put these features in s some kind of ,
A: um A: well , to to obtain a statistical model on these features
A: and to or just to use a neural network
A: and hopefully these features w would help C: Because it seems like what you said about the mean of the the voiced and the unvoiced <comment> that seemed pretty encouraging .
A: Mm - hmm .
B: Well , yeah , except the variance was big .
C: Right ?
A: Yeah .
A: Except the variance is quite high .
B: Right ?
C: Well , y
C: Well , y I I don't know that I would trust that so much
A: Yeah .
C: because you 're doing these canonical mappings from TIMIT labellings .
C: Right ?
A: Uh - huh .
C: So ,
C: really that 's sort of a cartoon picture about what 's voiced and unvoiced .
C: So that could be giving you a lot of variance .
C: I mean ,
A: Yeah .
C: i it it may be that that you 're finding something good
C: and that the variance is sort of artificial because of how you 're getting your truth .
A: Mm - hmm .
B: Yeah .
B: But another way of looking at it might be that B: I mean , what w we we are coming up with feature sets after all .
B: So another way of looking at it is that um , the mel cepstru mel spectrum , mel cepstrum , any of these variants , um , give you the smooth spectrum .
B: It 's the spectral envelope .
B: By going back to the FFT , you 're getting something that is more like the raw data .
B: So the question is , what characterization B: and you 're playing around with this B: another way of looking at it is what characterization of the difference between the raw data and this smooth version is something that you 're missing that could help ?
B: So , I mean , looking at different statistical measures of that difference ,
B: coming up with some things and just trying them out
B: and seeing if you add them onto the feature vector does that make things better or worse in noise ,
B: where you 're really just i i
B: the way I 'm looking at it is not so much you 're trying to f find the best the world 's best voiced - unvoiced , uh , uh , classifier ,
C: Mm - hmm .
A: Mmm .
B: but it 's more that , you know , uh , uh , try some different statistical characterizations of that difference back to the raw data
C: Right .
B: and and
C: Right .
B: m maybe there 's something there that the system can use .
A: Yeah .
A: Yeah , but ther more obvious is that A: Yeah .
A: The the more obvious is that that A: well , using the th the FFT , um , you just it gives you just information about if it 's voiced or not voiced , ma mainly , I mean .
A: But So ,
B: Yeah .
A: this is why we we started to look by having sort of voiced phonemes
B: Well , that 's the rea
B: w w what I 'm arguing is that 's
B: Yeah .
B: I mean , uh , what I 'm arguing is that that that 's givi you gives you your intuition .
A: and A: Mm - hmm .
B: But in in reality , it 's you know , there 's all of this this overlap and so forth ,
E: Oh , sorry .
B: and But what I 'm saying is that may be OK ,
B: because what you 're really getting is not actually voiced versus unvoiced ,
B: both for the fac the reason of the overlap and and then , uh , th you know , structural reasons ,
B: uh , uh , like the one that Chuck said ,
B: that that in fact , well , the data itself is  that you 're working with is not perfect .
A: Yeah .
A: Mm - hmm .
B: So , what I 'm saying is maybe that 's not a killer
B: because you 're just getting some characterization ,
B: one that 's driven by your intuition about voiced - unvoiced certainly ,
A: Mm - hmm .
B: but it 's just some characterization of something back in the in the in the almost raw data , rather than the smooth version .
A: Mm - hmm .
B: And your intuition is driving you towards particular kinds of , uh , statistical characterizations of , um , what 's missing from the spectral envelope .
A: Mm - hmm .
B: Um , obviously you have something about the excitation ,
B: um ,
B: and what is it about the excitation ,
B: and , you know and you 're not getting the excitation anyway , you know .
B: So B: so I I would almost take a B: uh , especially if if these trainings and so forth are faster , I would almost just take a uh , a scattershot at a few different ways of look of characterizing that difference
B: and , uh , you could have one of them but and and see , you know , which of them helps .
A: Mm - hmm .
C: So i is the idea that you 're going to take whatever features you develop and and just add them onto the future vector ?
A: OK .
C: Or , what 's the use of the the voiced - unvoiced detector ?
A: Uh , I guess we don't know exactly yet .
A: But , um A: Yeah .
C: It 's not part of a VAD system that you 're doing ?
A: Th
F: No .
A: Uh , no .
C: Oh ,
C: OK .
A: No .
A: No , the idea was , I guess , to to use them as as features .
C: Features .
C: I see .
A: Uh A: Yeah , it could be , uh it could be a neural network that does voiced and unvoiced detection ,
C: Mm - hmm .
A: but it could be in the also the big neural network that does phoneme classification .
C: Mm - hmm .
A: Mmm .
B: But each one of the mixture components A: Yeah .
B: I mean , you have , uh , uh , variance only ,
B: so it 's kind of like you 're just multiplying together these , um , probabilities from the individual features within each mixture .
B: So it 's B: so ,
B: uh ,
C: I think it 's a neat thing .
B: it seems l you know C: Uh , it seems like a good idea .
B: Yeah .
B: Um .
B: Yeah .
B: I mean , I know that , um , people doing some robustness things a ways back were were just doing just being gross and just throwing in the FFT
B: and actually it wasn't wasn't wasn't so bad .
B: Uh , so it would s
B: and and you know that i it 's gotta hurt you a little bit to not have a  a spectral , uh a s a smooth spectral envelope ,
B: so there must be something else that you get in return for that B: that , uh A: Mm - hmm .
B: uh B: So .
C: So how does C: uh , maybe I 'm going in too much detail ,
C: but how exactly do you make the difference between the FFT and the smoothed spectral envelope ?
C: Wha - wh i i uh , how is that , uh ?
A: Um , we just A: How did we do it up again ?
F: Uh , we distend the we have the twenty - three coefficient af after the mel f filter ,
A: Mm - hmm .
F: and we extend these coefficient between the all the frequency range .
C: Mm - hmm .
F: And i the interpolation i between the point is give for the triang triangular filter , the value of the triangular filter
F: and of this way we obtained this mode this model speech .
B: So you essentially take the values that th that you get from the triangular filter and extend them
A: S
B: to sor sort of like a rectangle , that 's at that m value .
F: Yeah .
F: Mm - hmm .
A: Yeah . I think we have linear interpolation .
A: So we have we have one point for one energy for each filter bank ,
F: mmm Yeah , it 's linear .
C: Mmm .
B: Oh .
F: Yeah .
A: which is the energy that 's centered on on on the triangle F: At the n
F: at the center of the filter C: So you you end up with a vector that 's the same length as the FFT vector ?
A: Yeah .
F: Yeah .
A: That 's right .
C: And then you just , uh , compute differences
F: Yeah .
F: I have here one example if you if you want see something like that .
C: and ,
A: Then we compute the difference .
A: Yeah .
A: Uh - huh .
C: uh , sum the differences ?
B: OK .
A: So .
A: And I think the variance is computed only from , like , two hundred hertz to one to fifteen hundred .
C: Oh ! OK .
B: Mm - hmm .
F: Two thou two <comment> fifteen hundred ?
B: Mm - hmm .
F: No .
A: Because B: Right .
F: Two hundred and fifty thousand .
A: Fifteen hundred .
A: Because F: Yeah .
A: Yeah .
F: Two thousand and fifteen hundred .
A: Above , um  it seems that A: Well , some voiced sound can have also , like , a noisy part on high frequencies ,
A: and B: Yeah .
A: But B: No , it 's makes sense to look at low frequencies .
A: Well , it 's just C: So this is uh , basically this is comparing an original version of the signal to a smoothed version of the same signal ?
F: Yeah .
B: Right .
B: So i so i i this is B: I mean , i you could argue about whether it should be linear interpolation or or or or zeroeth order ,
B: but but
C: Uh - huh .
B: at any rate something like this is what you 're feeding your recognizer , typically .
C: Like which of the ?
B: No .
B: Uh , so the mel cepstrum is the is the is the cepstrum of this  this , uh , spectrum or log spectrum ,
A: So this is A: Yeah .
C: Yeah .
C: Right , right .
B: whatever it B: You - you 're subtracting in in in power domain or log domain ?
A: In log domain .
F: Log domain .
A: Yeah .
B: OK .
B: So it 's sort of like division , when you do the yeah , the spectra .
F: Yeah .
A: Uh , yeah .
C: It 's the ratio .
B: Um .
B: Yeah .
B: But , anyway ,
B: um B: and that 's C: So what 's th uh , what 's the intuition behind this kind of a thing ?
C: I I don't know really know the signal - processing well enough to understand what  what is that doing .
A: So .
A: Yeah .
B: Yeah .
B: I guess that makes sense .
A: What happen if what we have have what we would like to have is some spectrum of the excitation signal ,
B: Yeah .
A: which is for voiced sound ideally
A: a a pulse train
C: Uh - huh .
A: and for unvoiced it 's something that 's more flat .
C: Uh - huh .
C: Right .
A: And the way to do this is that A: well , we have the we have the FFT because it 's computed in in the in the system ,
A: and we have the mel filter banks ,
C: Mm - hmm .
C: Mm - hmm .
A: and so if we if we , like , remove the mel filter bank from the FFT , we have something that 's close to the excitation signal .
E: Oh .
C: OK .
A: It 's something that 's like a a a train of p a pulse train for voiced sound
B: Yeah .
C: Oh ! OK .
C: Yeah .
A: and that 's that should be flat for B: Yeah .
C: I see .
C: So do you have a picture that sh ?
C: Is this for a voiced segment ,
A: So - It 's Y
C: this picture ?
A: yeah .
C: What does it look like for unvoiced ?
F: Yeah .
A: You have several some unvoiced ?
F: The dif
F: No . Unvoiced , I don't have
F: for unvoiced .
A: Oh .
B: Yeah .
F: I 'm sorry .
B: So , you know , all A: But A: Yeah .
B: Yeah .
F: Yeah .
F: This is the between A: This is another voiced example .
F: No .
A: Yeah .
F: But it 's this ,
A: Oh , yeah .
A: This is F: but between the frequency that we are considered for the excitation A: Right .
A: Mm - hmm .
F: for the difference
F: and this is the difference .
C: This is the difference .
A: Yeah .
C: OK .
A: So , of course , it 's around zero ,
B: Yeah .
E: Sure looks A: but E: Hmm .
A: Well ,
C: Hmm .
A: no .
F: Yeah .
A: It is F: Because we begin , uh , in fifteen point the fifteen point .
C: So ,
C: does does the periodicity of this signal say something about the the F: Fifteen p
A: So it 's B: Pitch .
A: Yeah .
C: the pitch ?
A: It 's the pitch .
C: OK .
B: Yeah .
A: Yeah .
A: Mm - hmm .
B: That 's like fundamental frequency .
A: Mm - hmm .
C: OK .
B: So , I mean , i t t
C: I see .
B: I mean , to first order what you 'd what you 're doing B: I mean , ignore all the details and all the ways which is that these are complete lies .
C: Mm - hmm .
B: Uh , the the you know , what you 're doing in feature extraction for speech recognition is you have , uh , in your head a a a a simplified production model for speech ,
F: Yeah .
C: Mm - hmm .
B: in which you have a periodic or aperiodic source that 's driving some filters .
F: This is the the auto - correlation the R - zero energy .
A: Do you have the mean A: do you have the mean for the auto - correlation ?
B: Uh , first order for speech recognition , you say " I don't care about the source " .
F: For F: Yeah .
F: I have the mean .
A: Well , I mean for the the energy .
C: Right .
B: Right ?
C: Right .
B: And so you just want to find out what the filters are .
F: Yeah .
B: The filters roughly act like a , um  a , uh  a an overall resonant you know , f some resonances and so forth that th that 's processing excitation .