C: um , r
D: Well , I think what I was s I thought what I was saying was that , um , at any given point you are gonna start off with what you had from before .
C: Oh .
D: From D: and so if you 're splitting things up into utterances D: So , for instance , in a dialogue system , <comment> where you 're gonna be asking , uh , you know , th for some information , there 's some initial th something .
D: And , you know , the first time out you you might have some general average .
D: But you you d you don't have very much information yet .
D: But at after they 've given one utterance you 've got something .
D: You can compute your mean cepstra from that ,
C: Mm - hmm .
D: and then can use it for the next thing that they say ,
D: uh ,
D: so that , you know , the performance should be better that second time .
D: Um ,
D: and I think the heuristics of exactly how people handle that and how they handle their training I 'm sure vary from place to place .
D: But I think the ideally , it seems to me anyway , that you you would wanna do the same thing in training as you do in test .
D: But that 's that 's just , uh , a prejudice .
D: And I think anybody working on this with some particular task would experiment .
C: Right .
C: I g I guess the question I had was , um , amount of data e u was the amount of data that you 'd give it to , um update this estimate .
C: Because say you if you have say five thousand utterances in your training set , um , and you you keep the mean from the last utterance ,
C: by the time it gets to the five thousandth utterance D: No , but those are all different people with different I mean , i in y
D: So for instance , in in the in a telephone task , these are different phone calls .
D: So you don't wanna @ @ <comment> chain it together from a from a different phone call .
C: OK ,
C: so so so they would D: So it 's within speaker ,
C: g s
G: Yeah .
D: within phone call ,
D: if it 's a dialogue system , it 's within whatever this characteristic you 're trying to get rid of is expected to be consistent over ,
G: Hmm .
C: r and it D: right ?
C: right .
C: OK ,
C: so you 'd you and so in training you would start over at at every new phone call or at every new speaker .
D: Yeah .
C: Yeah ,
C: OK .
D: Yeah .
D: Now , you know , maybe you 'd use something from the others
D: just because at the beginning of a call you don't know anything ,
D: and so you might have some kind of general thing that 's your best guess to start with .
D: But D: So , s I I D: you know , a lot of these things are proprietary
D: so we 're doing a little bit of guesswork here .
D: I mean , what do comp what do people do who really face these problems in the field ?
D: Well , they have companies
D: and they don't tell other people exactly what they do .
D: But but I mean , when you the the hints that you get from what they when they talk about it are that they do they all do something like this .
C: R right .
C: Right ,
C: OK .
C: I see .
C: Bec - because I so this SmartKom task first off , it 's this TV and movie information system .
D: Yeah , but you might have somebody who 's using it
C: And D: and then later you might have somebody else who 's using it .
C: Yeah .
C: Yeah .
D: And so you 'd wanna set some C: Right .
C: Right .
C: I I see .
C: I was I was about to say . So if if you ask it " What what movies are on TV tonight ? " ,
D: Yeah .
D: Yeah .
C: if I look at my wristwatch when I say that it 's about two seconds .
D: Yeah .
C: The way I currently have the mean subtraction , um , set up , the the analysis window is two seconds .
C: So what you just said , about what do you start with , raises a question of what do I start with then ?
D: Mm - hmm .
C: I guess it because D: Well ,
D: w OK ,
D: so in that situation , though , th maybe what 's a little different there , is I think you 're talking about there 's only one D: it it it also depends D: we 're getting a little off track here .
C: Oh ,
D: r But but but C: right .
D: Uh , there 's been some discussion about whether the work we 're doing in that project is gonna be for the kiosk or for the mobile or for both .
D: And I think for this kind of discussion it matters .
D: If it 's in the kiosk , then the physical situation is the same .
D: It 's gonna you know , the exact interaction of the microphone 's gonna differ depending on the person and so forth .
D: But at least the basic acoustics are gonna be the same .
D: So f if it 's really in one kiosk , then I think that you could just chain together and and you know , as much as much speech as possible to D: because what you 're really trying to get at is the is the reverberation characteristic .
C: Yeah .
D: But in in the case of the mobile , uh , <comment> presumably the acoustic 's changing all over the place .
C: Right .
D: And in that case you probably don't wanna have it be endless because you wanna have some sort of it 's it 's not a question of how long do you think it 's you can get an approximation to a stationary something , given that it 's not really stationary .
C: Right .
G: Hmm .
C: Right .
D: So .
C: And I I g I guess I s just started thinking of another question ,
C: which is , for for the very first frame , w what what do I do
C: if I 'm if I take if I use that frame to calculate the mean , then I 'm just gonna get n nothing .
D: Mm - hmm .
D: Right .
C: Um ,
C: so I should probably have some kind of default mean for the first f couple of frames ?
D: Yeah .
D: Yeah .
C: OK .
D: Yeah .
D: Or subtract nothing .
D: I mean , it 's C: Or subtract nothing .
C: And and that 's that 's I guess that 's something that 's p people have figured out how to deal with in cepstral mean subtraction as well ?
D: Yeah , yeah .
D: Yeah , people do something .
D: They they , uh , they have some , um ,
D: uh ,
D: in in cepstral mean subtraction , for short - term window analysis windows , as is usually done , you 're trying to get rid of some very general characteristic .
D: And so , uh , if you have any other information about what a general kind of characteristic would be , then you you can do it there .
F: You can also F: you can also reflect the data .
F: So you take , uh F: you know ,
F: I 'm not sure how many frames you need .
F: But you take that many from the front and flip it around to a as the negative value .
C: Uh - huh .
D: Yeah ,
D: that 's F: So you can always D: Yeah .
D: The other thing is that and and I I remember B B N doing this , is that if you have a multi - pass system , um , if the first pass ta it takes most of the computation , the second and the third pass could be very , very quick ,
D: just looking at a relatively small n small , uh , space of hypotheses .
C: Mmm .
C: Uh - huh .
D: Then you can do your first pass without any subtraction at all .
C: Oh .
D: And then your second pass , uh , uh , eliminates those most of those hypotheses by , uh by having an improved improved version o of the analysis .
C: OK .
C: OK .
D: So .
C: OK .
C: So that was all I had , for now .
D: Yeah .
F: Do you wanna go , Barry ?
A: Yeah ,
A: OK .
A: Um , so for the past , uh , week an or two , I 've been just writing my , uh , formal thesis proposal .
A: Um , so I 'm taking this qualifier exam that 's coming up in two weeks .
A: And I I finish writing a proposal and submit it to the committee .
A: Um .
A: And uh , should I should I explain , uh , more about what what I 'm proposing to do , and s and stuff ?
D: Yes , briefly .
F: Yeah briefly .
A: OK .
A: Um ,
A: so briefly , I 'm proposing to do a n a new p approach to speech recognition using um , a combination of , uh , multi - band ideas and ideas , um ,  <comment> about the uh , acoustic phonec phonetic approach to speech recognition .
A: Um , so I will be using these graphical models that um , that implement the multi - band approach to recognize a set of intermediate categories that might involve , uh , things like phonetic features or other other f feature things that are more closely related to the acoustic signal itself .
A: Um , and the hope in all of this is that by going multi - band and by going into these , um intermediate classifications , that we can get a system that 's more robust to to unseen noises , and situations like that .
A: Um ,
A: and so ,
A: some of the research issues involved in this are , um , <comment> one , what kind of intermediate categories do we need to classify ?
A: Um , another one is um , what what other types of structures in these multi - band graphical models should we consider in order to um , combine evidence from the sub - bands ?
A: And , uh , the third one is how do we how do we merge all the , uh , information from the individual uh , multi - band classifiers to come up with word word recognition or or phone recognition things .
A: Um ,
A: so basically that 's that 's what I 've been doing .
F: So you 've got two weeks , huh ?
A: And ,
A: I got two weeks to brush up on d um , presentation stuff
A: and , um ,
D: Oh , I thought you were finishing your thesis in two weeks .
A: But .
A: Oh , that too .
D: Yeah .
A: Yeah .
F: Are you gonna do any dry runs for your thing ,
F: or are you just gonna A: Yes .
A: Yes .
A: I , um I 'm I 'm gonna do some .
A: Would you be interested ?
F: Sure .
A: To help out ?
F: Sure .
A: OK .
A: Thanks .
A: Yeah .
F: Is that it ?
F: Hhh .
A: That 's it .
F: OK .
F: Uh . Hhh .
F: Let 's see .
F: So we 've got forty minutes left ,
F: and it seems like there 's a lot of material .
F: An - any suggestions about where we where we should go next ?
B: Mmm , @ @ .
F: Uh .
F: Do you wanna go , Sunil ?
F: Maybe we 'll just start with you .
B: Yeah .
B: But I actually stuck most of this in our m last meeting with Guenter .
B: Um ,
B: but I 'll just B: Um ,
B: so the last week , uh , I showed some results with only SpeechDat - Car
B: which was like some fifty - six percent .
B: And , uh , I didn't h
B: I mean , I I found that the results B: I mean , I wasn't getting that r results on the TI - digit .
B: So I was like looking into " why , what is wrong with the TI - digits ? " .
B: Why why I was not getting it .
B: And I found that , the noise estimation is a reason for the TI - digits to perform worse than the baseline .
B: So , uh , I actually , picked th
B: I mean , the first thing I did was I just scaled the noise estimate by a factor which is less than one to see if that because I found there are a lot of zeros in the spectrogram for the TI - digits when I used this approach .
B: So the first thing I did was I just scaled the noise estimate .
B: And I found B: So the the results that I 've shown here are the complete results using the new B: Well , the n the new technique is nothing but the noise estimate scaled by a factor of point five .
B: So it 's just an ad - hoc B: I mean , some intermediate result , because it 's not optimized for anything .
B: So the results The trend the only trend I could see from those results was like the the p the current noise estimation or the , uh , noise composition scheme is working good for like the car noise type of thing .
B: Because I 've the only only p very good result in the TI - digits is the noise car noise condition for their test - A ,
B: which is like the best I could see that
B: uh , for any non - stationary noise like " Babble " or " Subway " or any " Street " , some " Restaurant " noise , it 's like it 's not performing w very well .
B: So ,
B: the  So that that 's the first thing I c uh , I could make out from this stuff .
B: And G: Yeah , I think what is important to see is that there is a big difference between the training modes .
G: Uh - huh .
B: Yeah .
G: If you have clean training , you get also a fifty percent improvement .
B: Yeah .
G: But if you have muddy condition training you get only twenty percent .
B: Yeah .
B: Yeah .
E: Mm - hmm .
G: Mm - hmm .
B: Uh , and in that twenty percent @ @ it 's very inconsistent across different noise conditions .
G: Mmm .
B: So I have like a forty - five percent for " Car noise "
B: and then there 's a minus five percent for the " Babble " ,
G: Mmm .
B: and there 's this thirty - three for the " Station " .
B: And so it 's it 's not it 's not actually very consistent across .
B: So .
B: The only correlation between the SpeechDat - Car and this performance is the c stationarity of the noise that is there in these conditions and the SpeechDat - Car .
G: Mm - hmm .
B: And , uh B: so B: so the overall result is like in the last page ,
B: which is like forty - seven ,
B: which is still very imbalanced because there are like fifty - six percent on the SpeechDat - Car and thirty - five percent on the TI - digits .
B: And B: uh ,
B: ps the fifty - six percent is like comparable to what the French Telecom gets ,
B: but the thirty - five percent is way off .
D: I 'm sort of confused
D: but D: this D: I 'm looking on the second page ,
B: Oh ,
B: yep .
D: and it says " fifty percent " D: looking in the lower right - hand corner ,
D: " fifty percent relative performance " .
G: For the clean training .
D: Is that G: u
G: And if you if you look D: is that fifty percent improvement ?
B: Yeah .
G: Yeah .
B: For that 's for the clean training and the noisy testing for the TI - digits .
D: So it 's improvement over the baseline mel cepstrum ?
B: Yeah .
B: Yeah .
D: But the baseline mel cepstrum under those training doesn't do as well
D: I I 'm I 'm trying to understand why it 's it 's eighty percent D: That 's an accuracy number , I guess ,
B: Yeah , yeah , yeah .
D: right ?
D: So that 's not as good as the one up above .
B: No .
D: But the fifty is better than the one up above ,
B: Yeah .
D: so I 'm confused .
B: Uh , actually the noise compensation whatever , uh , we are put in it works very well for the high mismatch condition .
B: I mean , it 's consistent in the SpeechDat - Car
B: and in the clean training also it gives it B: But this fifty percent is is that the the high mismatch performance equivalent to the high mismatch performance in the speech .
F: So n s So since the high mismatch performance is much worse to begin with , it 's easier to get a better relative improvement .
B: Yeah .
B: Yeah .
B: I do .
B: Yeah , yeah .
B: So by putting this noise E: Yeah .
E: Yeah , if we look at the figures on the right , we see that the reference system is very bad .
D: Oh .
B: Yeah .
B: The reference drops like a very fast D: Oh , oh , oh , oh , oh , oh .
E: Like for clean clean training condition .
D: I see .
B: Yeah .
D: I see .
E: Nnn .
D: This is this is TI digits <comment> we 're looking at ?
B: Yeah .
D: This whole page is TI - digits
B: Yeah .
B: Oh D: or this is ?
B: Oh . Yeah .
B: It 's not written anywhere .
B: Yeah , it 's TI - digits .
B: The first r spreadsheet is TI - digits .
D: Mmm .
G: Hmm .
D: How does clean training do for the , uh , " Car "
D: stuff ?
B: The " Car " ?
B: Oh .
B: Still it still , uh that that 's still consistent .
B: I mean , I get the best performance in the case of " Car " , which is the third column in the A condition .
D: No .
D: I mean , this is added noise .
D: I mean , this is TI - digits .
D: I 'm sorry .
D: I meant in in the in the , uh , multi - language , uh , uh , Finnish and B: Uh G: This is next next page .
B: That 's the next next spreadsheet , is G: Hmm .
B: So that is the performance for Italian , Finnish and Spanish .
D: " Training condition " D: Oh , right .
D: So " clean " corresponds to " high mismatch " .
B: Yeah .
D: And " increase " ,
D: That 's increase e
G: Improvement .
B: Improvement .
G: Yeah .
B: That 's " Percentage increase " is the percentage improvement over the baseline .
G: It 's it 's a B: So that 's D: Which means decrease in word error rate ?
B: Yeah .
D: OK ,
D: so " percentage increase " means decrease ?
D: OK .
B: Yeah , yeah .
G: Yeah .
G: The the w there was a very long discussion about this on on the on the , uh , Amsterdam meeting .
D: Yeah .
G: How to how to calculate it then .
B: Yeah .
B: There 's there 's a G: I I I guess you are using finally this the scheme which they B: Which is there in the spreadsheet .
B: I 'm not changing anything in there .
G: OK .
G: Mmm .
D: Alright .
B: So .
B: Uh ,
B: yeah .
B: So all the hi H M numbers are w very good ,
B: in the sense , they are better than what the French Telecom gets .
B: So .
B: But the the only number that 's still I mean , which Stephane also got in his result was that medium mismatch of the Finnish ,
B: which is very  which is a very strange situation where we used the we changed the proto for initializing the HMM B: I mean , this this is basically because it gets stuck in some local minimum in the training .
B: That seventy - five point seven nine in the Finnish mismatch which is that the eleven point nine six what we see .
D: Uh - huh .
G: Mmm .
B: Yeah .
D: So we have to jiggle it somehow ?
B: Yeah B: so we start with that different proto and it becomes eighty - eight ,
B: which is like some fifty percent improvement .
D: S Wait a minute .
D: Start with a different what ?
B: Different prototype ,
B: which is like a different initialization for the , uh , s transition probabilities .
B: It 's just that right now , the initialization is to stay more in the current state ,
B: which is point four point six , right ?
E: Yeah .