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Yeah.
QMSum_120
false
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.
QMSum_120
false
Here.
QMSum_120
false
They should be more close.
QMSum_120
false
Ah , no. This is this ? More close. Is this ? And this.
QMSum_120
false
Mm - hmm.
QMSum_120
false
Yeah.
QMSum_120
false
Mm - hmm.
QMSum_120
false
So they are this is there is less difference.
QMSum_120
false
Mm - hmm.
QMSum_120
false
So if you look at the spectral envelope , just the very smooth properties of it , you get something closer to that.
QMSum_120
false
This is less it 's less robust.
QMSum_120
false
Less robust. Yeah.
QMSum_120
false
Oh , yeah.
QMSum_120
false
And the notion is if you have the full spectrum , with all the little nitty - gritty details , that that has the effect of both ,
QMSum_120
false
Yeah.
QMSum_120
false
and it would be a multiplication in in frequency domain
QMSum_120
false
Mm - hmm.
QMSum_120
false
so that would be like an addition in log power spectrum domain.
QMSum_120
false
Mm - hmm. Mm - hmm.
QMSum_120
false
And so this is saying , well , if you really do have that sort of vocal tract envelope , and you subtract that off , what you get is the excitation. And I call that lies because you don't really have that , you just have some kind of signal - processing trickery to get something that 's kind of smooth. It 's not really what 's happening in the vocal tract
QMSum_120
false
Yeah.
QMSum_120
false
so you 're not really getting the vocal excitation.
QMSum_120
false
Right.
QMSum_120
false
That 's why I was going to the why I was referring to it in a more a more , uh , uh , conservative way , when I was saying " well , it 's yeah , it 's the excitation ". But it 's not really the excitation. It 's whatever it is that 's different between
QMSum_120
false
Oh. This moved in the
QMSum_120
false
So so , stand standing back from that , you sort of say there 's this very detailed representation.
QMSum_120
false
Yeah.
QMSum_120
false
You go to a smooth representation.
QMSum_120
false
Mm - hmm.
QMSum_120
false
You go to a smooth representation cuz this typically generalizes better.
QMSum_120
false
Mm - hmm.
QMSum_120
false
Um , but whenever you smooth you lose something , so the question is have you lost something you can you use ?
QMSum_120
false
Right.
QMSum_120
false
Um , probably you wouldn't want to go to the extreme of just ta saying " OK , our feature set will be the FFT " , cuz we really think we do gain something in robustness from going to something smoother , but maybe there 's something that we missed.
QMSum_120
false
Mm - hmm.
QMSum_120
false
So what is it ?
QMSum_120
false
Yeah.
QMSum_120
false
And then you go back to the intuition that , well , you don't really get the excitation , but you get something related to it.
QMSum_120
false
Mm - hmm.
QMSum_120
false
And it and as you can see from those pictures , you do get something that shows some periodicity , uh , in frequency ,
QMSum_120
false
Mm - hmm.
QMSum_120
false
you know , and and and also in time.
QMSum_120
false
Hmm.
QMSum_120
false
So
QMSum_120
false
That 's that 's really neat.
QMSum_120
false
so ,
QMSum_120
false
So you don't have one for unvoiced picture ?
QMSum_120
false
Uh , not here.
QMSum_120
false
Oh.
QMSum_120
false
No , I have s
QMSum_120
false
Mm - hmm.
QMSum_120
false
Yeah.
QMSum_120
false
But not here.
QMSum_120
false
But presumably you 'll see something that won't have this kind of , uh , uh , uh , regularity in frequency , uh , in the
QMSum_120
false
But Yeah. Well.
QMSum_120
false
Not here.
QMSum_120
false
I would li I would like to see those pictures.
QMSum_120
false
Well , so.
QMSum_120
false
Yeah.
QMSum_120
false
I can't see you now.
QMSum_120
false
Yeah.
QMSum_120
false
Yeah.
QMSum_120
false
Yeah.
QMSum_120
false
Mm - hmm.
QMSum_120
false
I don't have.
QMSum_120
false
And so you said this is pretty doing this kind of thing is pretty robust to noise ?
QMSum_120
false
It seems , yeah. Um ,
QMSum_120
false
Huh.
QMSum_120
false
Pfft. Oops. The mean is different with it , because the the histogram for the the classifica
QMSum_120
false
No , no , no. But th the kind of robustness to noise
QMSum_120
false
Oh !
QMSum_120
false
So if if you take this frame , uh , from the noisy utterance and the same frame from the clean utterance
QMSum_120
false
Hmm.
QMSum_120
false
You end up with a similar difference
QMSum_120
false
Y y y yeah. We end up with
QMSum_120
false
over here ?
QMSum_120
false
Yeah.
QMSum_120
false
OK. Cool !
QMSum_120
false
I have here the same frame for the clean speech
QMSum_120
false
Oh , that 's clean.
QMSum_120
false
the same cle
QMSum_120
false
Oh , OK
QMSum_120
false
But they are a difference.
QMSum_120
false
Yeah , that 's
QMSum_120
false
Because here the FFT is only with two hundred fifty - six point
QMSum_120
false
Oh.
QMSum_120
false
and this is with five hundred twelve.
QMSum_120
false
Yeah. This is kind of inter interesting also
QMSum_120
false
OK.
QMSum_120
false
because if we use the standard , uh , frame length of of , like , twenty - five milliseconds , um , what happens is that for low - pitched voiced , because of the frame length , y you don't really have you don't clearly see this periodic structure ,
QMSum_120
false
Mm - hmm.
QMSum_120
false
because of the first lobe of of each each of the harmonics.
QMSum_120
false
So this one inclu is a longer Ah.
QMSum_120
false
So , this is like yeah , fifty milliseconds or something like that.
QMSum_120
false
Fifty millis Yeah.
QMSum_120
false
Yeah , but it 's the same frame and
QMSum_120
false
Oh , it 's that time - frequency trade - off thing.
QMSum_120
false
Yeah.
QMSum_120
false
Right ? I see. Yeah.
QMSum_120