F: not that I know of .
C: Yeah ,
C: cuz everybody 's scared .
A: Yeah .
C: You 'll see a little smoke coming up from the the CPU or something trying to trying to do it ,
F: That 's right
C: but
C: uh , yeah .
C: But , you 're right
C: that that that 's a real good point , that uh , we we don't know yeah , uh , I mean , what if any of these ta
C: I guess that 's why they 're pushing that in the uh in the evaluation .
A: Yeah .
C: Uh ,
C: But um ,
C: Good .
C: OK .
C: Anything else going on ?
C: at you guys ' end ,
C: or ?
B: I don't have good result , with the inc including the new parameters ,
B: I don't have good result .
B: Are similar or a little bit worse .
A: With what what other new p new parameter ?
G: You 're talking about your voicing ?
C: Yeah .
C: So maybe You probably need to back up a bit
B: Yeah .
B: Mm - hmm .
A: Yeah .
C: seeing as how Sunil ,
B: I tried to include another new parameter to the traditional parameter ,
C: yeah .
B: the coe the cepstrum coefficient ,
A: Uh - huh .
B: that , like , the auto - correlation , the R - zero and R - one over R - zero
A: Mm - hmm .
A: Mm - hmm .
B: and another estimation of the var the variance of the difference for of the spec si uh , spectrum of the signal
B: and and the spectrum of time after filt mel filter bank .
A: I 'm so sorry .
A: I didn't get it .
B: Nuh . Well .
B: Anyway .
B: The First you have the sp the spectrum of the signal ,
A: Mm - hmm .
B: and you have the on the other side you have the output of the mel filter bank .
A: Mm - hmm .
B: You can extend the coefficient of the mel filter bank and obtain an approximation of the spectrum of the signal .
A: Mmm .
A: OK .
B: I do the difference A: OK .
B: I found a difference at the variance of this different
A: Uh - huh .
B: because , suppose we we think that if the variance is high , maybe you have n uh , noise .
A: Yeah .
B: And if the variance is small , maybe you have uh , speech .
A: Uh - huh .
B: To to
B: To B: The idea is to found another feature for discriminate between voice sound and unvoice sound .
A: OK .
B: And we try to use this new feature feature .
B: And I did experiment B: I need to change to obtain this new feature I need to change the size the window size size .
B: of the a of the analysis window size ,
B: to have more information .
A: Yeah .
A: Make it longer .
B: Uh , sixty - two point five milliseconds I think .
A: OK .
B: And
B: I do I did two type of experiment to include this feature directly with the with the other feature
B: and to train a neural network to select it voice - unvoice - silence silence
A: Unvoiced .
A: Well .
B: and to to concat this new feature .
B: But the result are
B: n with the neural network I have more or less the same result .
A: As using just the cepstrum ,
B: Result .
A: or ?
B: Yeah .
B: Yeah .
A: OK .
B: It 's neve e e
B: sometime it 's worse ,
B: sometime it 's a little bit better ,
B: but not significantly .
B: And A: Uh , is it with TI - digits , or with ?
B: No , I work with eh , Italian and Spanish basically .
A: OK .
A: OK .
B: And if I don't y use the neural network , and use directly the feature the results are worse .
A: Uh - huh .
B: But Doesn't help .
D: Mm - hmm .
C: I I I really wonder though .
C: I mean we 've had these discussions before ,
C: and and one of the things that struck me was that uh , about this line of thought that was particularly interesting to me was that we um whenever you condense things , uh , in an irreversible way , um , you throw away some information .
C: And , that 's mostly viewed on as a good thing , in the way we use it ,
C: because we wanna suppress things that will cause variability for uh particular , uh , phonetic units .
C: Um ,
C: but , you 'll do throw something away .
C: And so the question is , uh , can we figure out if there 's something we 've thrown away that we shouldn't have .
C: And um .
C: So ,
C: when they were looking at the difference between the filter bank and the FFT that was going into the filter bank , I was thinking " oh , OK ,
C: so they 're picking on something
C: they 're looking on it to figure out noise , or voice voiced property whatever . "
C: So that that 's interesting .
C: Maybe that helps to drive the the thought process of coming up with the features .
C: But for me sort of the interesting thing was , " well , but is there just something in that difference which is useful ? "
C: So another way of doing it , maybe , would be just to take the FFT uh , power spectrum , and feed it into a neural network ,
C: and then use it , you know , in combination , or alone , or or whatever
B: To know F: Wi - with what targets ?
A: Voiced , unvoiced is like C: Uh , no .
A: Oh .
A: Or anything .
C: No
C: the just the same same way we 're using I mean , the same way that we 're using the filter bank .
F: Phones .
A: Oh , OK .
D: Mm - hmm .
C: Exact way the same way we 're using the filter bank .
C: I mean , the filter bank is good for all the reasons that we say it 's good .
C: But it 's different .
C: And , you know , maybe if it 's used in combination , it will get at something that we 're missing .
D: Mm - hmm .
C: And maybe , you know , using , orth you know , KLT , or uh , um , adding probabilities , I mean , all th all the different ways that we 've been playing with , that we would let the essentially let the neural network determine what is it that 's useful , that we 're missing here .
D: Mm - hmm .
D: Yeah , but there is so much variability in the power spectrum .
A: Mm - hmm .
C: Well , that 's probably why y i it would be unlikely to work as well by itself ,
D: Mm - hmm .
C: but it might help in combination .
D: Mmm .
C: But I I I have to tell you ,
C: I can't remember the conference ,
C: but , uh , I think it 's about ten years ago , I remember going to one of the speech conferences
C: and and uh ,
C: I saw within very short distance of one another a couple different posters that showed about the wonders of some auditory inspired front - end or something ,
C: and a couple posters away it was somebody who compared one to uh , just putting in the FFT
C: and the FFT did slightly better .
D: Mm - hmm .
C: So I mean the i i It 's true there 's lots of variability ,
C: but again we have these wonderful statistical mechanisms for quantifying that a that variability , and you know , doing something reasonable with it .
D: Mm - hmm .
C: So ,
C: um ,
C: uh ,
C: It - it 's same , you know , argument that 's gone both ways about uh , you know , we have these data driven filters , in LDA ,
C: and on the other hand , if it 's data driven it means it 's driven by things that have lots of variability , and that are necessarily not necessarily gonna be the same in training and test ,
C: so , in some ways it 's good to have data driven things ,
C: and in some ways it 's bad to have data driven things .
C: So ,
A: Yeah , d
C: part of what we 're discovering , is ways to combine things that are data driven than are not .
A: Yeah .
C: Uh , so anyway , it 's just a thought , that that if we if we had that maybe it 's just a baseline
C: uh , which would show us " well , what are we really getting out of the filters " ,
C: or maybe i i
C: probably not by itself ,
C: but in combination ,
D: Mm - hmm .
C: uh ,
C: you know , maybe there 's something to be gained from it ,
C: and let the C: But , you know , y you 've only worked with us for a short time ,
C: maybe in a year or two you w you will actually come up with the right set of things to extract from this information .
C: But , maybe the neural net and the H M Ms could figure it out quicker than you .
B: Maybe .
C: So .
C: It 's just a thought .
B: Yeah ,
B: I can I will try to do that .
C: Yeah .
A: What one one um p one thing is like what before we started using this VAD in this Aurora , the th what we did was like , I I guess most of you know about this , adding this additional speech - silence bit to the cepstrum and training the HMM on that .
C: Mm - hmm .
A: That is just a binary feature
A: and that seems to be improving a lot on the SpeechDat - Car where there is a lot of noise
A: but not much on the TI - digits .
A: So , a adding an additional feature to distin to discriminate between speech and nonspeech was helping .
A: That 's it .
D: Wait I I 'm sorry ?
A: Yeah , we actually added an additional binary feature to the cepstrum ,
A: just the baseline .
D: Yeah ?
B: You did some experiment .
A: Yeah ,
A: yeah .
A: Well , in in the case of TI - digits it didn't actually give us anything ,
A: because there wasn't any f anything to discriminate between speech ,
D: Yeah .
A: and it was very short .
D: Hmm .
A: But Italian was like very it was a huge improvement on Italian .
D: Well D: Mm - hmm .
D: But anyway the question is even more , is within speech , can we get some features ?
D: Are we drop dropping information that can might be useful within speech ,
D: I mean . To maybe to distinguish between voice sound and unvoiced sounds ?
A: OK .
A: Mm - hmm .
A: Yeah , yeah .
A: Yeah .
C: And it 's particularly more relevant now since we 're gonna be given the endpoints .
D: Yeah .
D: Mm - hmm .
C: So .
A: Yeah , yeah .
C: Uh .
D: Mmm .
C: So .
C: Um .
A: Mmm .
A: There was a paper in ICASSP this ICASSP over the uh extracting some higher - order uh , information from the cepstral coefficients
A: and I forgot the name .
A: Some is some harmonics
A: I don't know ,
A: I can I can pull that paper out from ICASSP .
D: Yeah .
C: Talking cumulants or something ?
A: It A: Huh ?
C: Cumulants or something .
A: Uh , I don't know .
C: But No .
A: I don't remember .
A: It wa it was taking the , um A: It was about finding the higher - order moments of C: Yeah ,
A: Yeah .
C: cumulants ,
A: And I 'm not sure about whether it is the higher - order moments , or C: yeah .
A: maybe higher - order cumulants
C: Oh .
A: and A: Yeah .
C: Or m e
A: It was it was A: Yeah .
A: I mean , he was showing up uh some something on noisy speech ,
C: Yeah .
A: some improvement on the noisy speech .
D: Mm - hmm .
A: Some small vocabulary tasks .
C: Uh .
A: So it was on PLP derived cepstral coefficients .
C: Yeah , but again You could argue that th that 's exactly what the neural network does .
A: Mmm .
C: So n neural network uh , is in some sense equivalent to computing , you know , higher - order moments of what you A: trying to f
A: to
A: Moments ,
A: yeah .
C: yeah .
A: Yeah .
C: So .
C: I mean , it doesn't do it very specifically ,
D: Mm - hmm .
C: and
C: pretty you know .
C: But .
A: Yep .
C: Uh ,
C: anything on your end you want to talk about ?
C: Uh .
G: Um , nothing I wanna really talk about .
G: I can I can just uh , um , share a little bit G: Sunil hasn't hasn't heard about uh , what I 've been doing .
C: Yeah .
G: Um ,
G: so ,
G: um , I told you I was I was I was getting prepared to take this qualifier exam .
G: So basically that 's just , um , trying to propose um , uh , your next your your following years of of your PHD work ,
G: trying trying to find a project to to define and and to work on .
G: So , I 've been , uh , looking into , um , doing something about r uh , speech recognition using acoustic events .
G: So ,
G: um , the idea is you have all these these different events ,
G: for example voicing ,
G: nasality ,
G: R - coloring ,
G: you know burst
G: or noise , uh , frication ,
G: that kinda stuff ,
G: um , building robust um , primary detectors for these acoustic events ,
G: and using the outputs of these robust detectors to do speech recognition .
G: Um ,
G: and , um , these these primary detectors , um , will be , uh , inspired by , you know , multi - band techniques , um , doing things , um , similar to Larry Saul 's work on , uh , graphical models to to detect these these , uh , acoustic events .
G: And , um , so I I been I been thinking about that
G: and some of the issues that I 've been running into are , um , exactly what what kind of acoustic events I need ,
G: what um , what acoustic events will provide a a good enough coverage to in order to do the later recognition steps .
G: And , also , um , once I decide a set of acoustic events , um , h how do I how do I get labels ?
G: Training data for for these acoustic events .
G: And , then later on down the line , I can start playing with the the models themselves ,
G: the the primary detectors .
G: Um ,
G: so ,
G: um , I kinda see like , after after building the primary detectors I see um , myself taking the outputs and feeding them in , sorta tandem style into into a um , Gaussian mixtures HMM back - end , um , and doing recognition .
G: Um .
G: So , that 's that 's just generally what I 've been looking at .
A: Yeah .
G: Um ,
C: By by the way , uh , the voiced - unvoiced version of that for instance could tie right in to what Carmen was looking at .
G: Yeah .
C: So ,
D: Mm - hmm .
C: you know , um , if you if a multi - band approach was helpful as as I think it is ,
C: it seems to be helpful for determining voiced - unvoiced ,
G: Mm - hmm .
C: that one might be another thing .
G: Yeah .
B: Mm - hmm .
G: Yeah .
G: Um ,
G: were were you gonna say something ?
F: Mmm .
G: Oh .
G: It looked G: OK ,
G: never mind .
G: Um , yeah .
G: And so , this this past week um , I 've been uh , looking a little bit into uh , TRAPS um , and doing doing TRAPS on on these e events too ,
G: just , um , seeing seeing if that 's possible .
G: Uh ,
G: and
G: um , other than that , uh , I was kicked out of I - house for living there for four years .
C: Oh no .
C: So you live in a cardboard box in the street now
G: Yeah .
C: or , no ?
G: Uh , well , s s som something like that .
C: Yeah .
G: In Albany ,
G: yeah .
G: Yeah .
G: And uh .
G: Yep .
G: That 's it .
C: Suni - i d ' you v did uh did you find a place ?
C: Is that out of the way ?
A: Uh , no
A: not yet .
A: Uh , yesterday I called up a lady who ha who will have a vacant room from May thirtieth
A: and she said she 's interviewing two more people .
A: So .
A: And she would get back to me on Monday .
A: So that 's that 's only thing I have
A: and Diane has a few more houses .
A: She 's going to take some pictures and send me after I go back .
C: OK .
A: So it 's that 's F: Oh .
F: So you 're not down here permanently yet ?
A: No .
A: I 'm going back to OGI today .
F: Ah !
F: Oh , OK .
G: Oh .
C: OK .
C: And then , you 're coming back uh A: Uh , i I mean , I I p I plan to be here on thirty - first .
C: Thirty - first ,
C: OK .
G: Thirty - first .
A: Yeah ,
A: well if there 's a house available or place to C: Well , I mean i i if if A: Yeah , I hope .
C: They 're available ,
C: and they 'll be able to get you something ,
C: so worst comes to worst we 'll put you up in a hotel for for for a while
A: Yeah .
A: So , in that case , I 'm going to be here on thirty - first definitely .
C: until you C: OK .
E: You know , if you 're in a desperate situation and you need a place to stay , you could stay with me for a while .
E: I 've got a spare bedroom right now .
A: Oh . OK .
A: Thanks .
A: That sure is nice of you .
A: So , it may be he needs more than me .
G: Oh r
G: oh .
G: Oh no , no .
G: My my cardboard box is actually a nice spacious two bedroom apartment .
C: So a two bedroom cardboard box .
C: Th - that 's great .
G: yeah
A: Yeah . Yeah . Yeah .
C: Thanks Dave .
A: Yeah .
C: Um ,
A: Yeah .
C: Do y wanna say anything about C: You you actually been C: Uh , last week you were doing this stuff with Pierre , you were you were mentioning .
C: Is that that something worth talking about ,
C: or ?
E: Um ,
E: it 's E: Well , um , it I don't think it directly relates .
E: Um , well , so , I was helping a speech researcher named Pierre Divenyi
E: and he 's int He wanted to um , look at um , how people respond to formant changes , I think .
E: Um .
E: So he he created a lot of synthetic audio files of vowel - to - vowel transitions ,
E: and then he wanted a psycho - acoustic um , spectrum .
E: And he wanted to look at um , how the energy is moving over time in that spectrum
E: and compare that to the to the listener tests .
E: And , um .
E: So , I gave him a PLP spectrum .
E: And to um he he t wanted to track the peaks so he could look at how they 're moving .
E: So I took the um , PLP LPC coefficients and um , I found the roots .
E: This was something that Stephane suggested .
E: I found the roots of the um , LPC polynomial to , um , track the peaks in the , um , PLP LPC spectra .
A: well there is aligned spectral pairs , is like the the A: Is that the aligned s
C: It 's a r root LPC , uh , of some sort .
A: Oh , no .
D: Mm - hmm .
A: So you just C: Yeah .
A: instead of the log you took the root square , I mean cubic root or something .
A: What di w I didn't get that .
C: No , no .
C: It 's it 's it 's taking the finding the roots of the LPC polynomial .
A: Polynomial .
A: Yeah .
A: Is that the line spectral C: So it 's like line spectral pairs .
A: Oh , it 's like line sp
C: Except I think what they call line spectral pairs they push it towards the unit circle ,
C: don't they ,
C: to sort of ?
A: Yeah , yeah , yeah , yeah .
C: But it C: But uh , you know .
C: But what we 'd used to do w when I did synthesis at National Semiconductor twenty years ago , the technique we were playing with initially was was taking the LPC polynomial and and uh , finding the roots .