input: PhD A: OK , we 're going .
  PhD C: Eight , eight ?
  PhD D: This is three .
  PhD C: Three .
  PhD D: Yep . Yep .
  Professor B: Test . Hmm . Let 's see . Move it bit . Test ? Test ? OK , I guess it 's alright . So , let 's see . Yeah , Barry 's not here and Dave 's not here . Um , I can say about {disfmarker} just q just quickly to get through it , that Dave and I submitted this ASRU .
  PhD A: This is for ASRU .
  Professor B: Yeah . So . Um . Yeah , it 's {disfmarker} it 's interesting . I mean , basically we 're dealing with rever reverberation , and , um , when we deal with pure reverberation , the technique he 's using works really , really well . Uh , and when they had the reverberation here , uh , we 'll measure the signal - to - noise ratio and it 's , uh , about nine DB . So ,
  PhD D: Hmm .
  Professor B: um ,
  PhD A: You mean , from the actual , uh , recordings ?
  Professor B: a fair amount of {disfmarker}
  PhD D: k
  PhD A: It 's nine DB ?
  Professor B: Yeah . Yeah . Um {disfmarker} And actually it brought up a question which may be relevant to the Aurora stuff too . Um , I know that when you figured out the filters that we 're using for the Mel scale , there was some experimentation that went on at {disfmarker} at , uh {disfmarker} at OGI . Um , but one of the differences that we found between the two systems that we were using , {comment} the {disfmarker} the Aurora HTK system baseline system {comment} and the system that we were {disfmarker} the {disfmarker} the uh , other system we were using , the uh , the SRI system , was that the SRI system had maybe a , um , hundred hertz high - pass . And the , uh , Aurora HTK , it was like twenty .
  PhD D: Yep . S sixty - four .
  Professor B: Uh .
  PhD D: S sixty - four .
  Professor B: Sixty - four ? Uh .
  PhD D: Yeah , if you 're using the baseline .
  Professor B: Is that the ba band center ?
  PhD D: No , the edge .
  Professor B: The edge is really , uh , sixty - four ?
  PhD D: Yeah .
  Professor B: For some reason , uh , Dave thought it was twenty ,
  PhD D:  So the , uh , center would be somewhere around like hundred
  Professor B: but .
  PhD D: and {disfmarker} hundred and {disfmarker} hundred {disfmarker} hundred and {disfmarker} maybe {disfmarker} it 's like {disfmarker} fi hundred hertz .
  Professor B: But do you know , for instance , h how far down it would be at twenty hertz ? What the {disfmarker} how much rejection would there be at twenty hertz , let 's say ?
  PhD D: At twenty hertz .
  Professor B: Yeah , any idea what the curve looks like ?
  PhD D: Twenty hertz frequency {disfmarker} Oh , it 's {disfmarker} it 's zero at twenty hertz , right ? The filter ?
  PhD C: Yea - actually , the left edge of the first filter is at sixty - four .
  PhD D: Sixt - s sixty - four .
  PhD C: So {disfmarker}
  PhD D: So anything less than sixty - four is zero .
  PhD C: Mmm .
  Professor B: It 's actually set to zero ? What kind of filter is that ?
  PhD C: Yeah .
  PhD D: Yeah .
  Professor B: Is this {disfmarker} oh , from the {disfmarker} from {disfmarker}
  PhD C: It {disfmarker} This is the filter bank in the frequency domain that starts at sixty - four .
  Professor B: Oh , so you , uh {disfmarker} so you really set it to zero , the FFT ?
  PhD D: Yeah ,
  PhD C: Yeah .
  PhD D: yeah . So it 's {disfmarker} it 's a weight on the ball spectrum . Triangular weighting .
  Professor B: Right . OK . Um {disfmarker} OK . So that 's {disfmarker} that 's a little different than Dave thought , I think . But {disfmarker} but , um , still , it 's possible that we 're getting in some more noise . So I wonder , is it {disfmarker} @ @ Was there {disfmarker} their experimentation with , uh , say , throwing away that filter or something ? And , uh {disfmarker}
  PhD D: Uh , throwing away the first ?
  Professor B: Yeah .
  PhD D: Um , yeah , we {disfmarker} we 've tried including the full {disfmarker} full bank . Right ? From zero to four K .
  PhD C: Mm - hmm .
  PhD D: And that 's always worse than using sixty - four hertz .
  Professor B: Right , but the question is , whether sixty - four hertz is {disfmarker} is , uh , too , uh , low .
  PhD D: Yeah , I mean , make it a hundred or so ?
  Professor B: Yeah .
  PhD D: I t I think I 've tried a hundred and it was more or less the same , or slightly worse .
  Professor B: On what test set ?
  PhD D: On the same , uh , SpeechDat - Car , Aurora .
  Professor B: Um , it was on the SpeechDat - Car .
  PhD D: Yeah . So I tried a hundred to four K . Yeah .
  Professor B: Um ,
  PhD D: So it was {disfmarker}
  Professor B: and on {disfmarker} and on the , um , um , {vocalsound} TI - digits also ?
  PhD D: No , no , no . I think I just tried it on SpeechDat - Car .
  Professor B: Mmm . That 'd be something to look at sometime because what , um , eh , he was looking at was performance in this room .
  PhD D: Mm - hmm .
  Professor B: Would that be more like {disfmarker} Well , you 'd think that 'd be more like SpeechDat - Car , I guess , in terms of the noise . The SpeechDat - Car is more , uh , sort of roughly stationary , a lot of it . And {disfmarker} and TI - digits maybe is not so much as {disfmarker}
  PhD D: Yeah .
  PhD C: Mm - hmm .
  Professor B: Yeah .
  PhD D: Yeah .
  Professor B: Mm - hmm . OK . Well , maybe it 's not a big deal . But , um {disfmarker} Anyway , that was just something we wondered about . But , um , uh , certainly a lot of the noise , uh , is , uh , below a hundred hertz . Uh , the signal - to - noise ratio , you know , looks a fair amount better if you {disfmarker} if you high - pass filter it from this room .
  PhD D: Yeah .
  Professor B: But , um {disfmarker} but it 's still pretty noisy . Even {disfmarker} even for a hundred hertz up , it 's {disfmarker} it 's still fairly noisy . The signal - to - noise ratio is {disfmarker} is {disfmarker} is actually still pretty bad .
  PhD C: Mm - hmm .
  PhD A: Hmm .
  Professor B: So , um , I mean , the main {disfmarker} the {disfmarker} the {disfmarker}
  PhD A: So that 's on th that 's on the f the far field ones though , right ? Yeah .
  Professor B: Yeah , that 's on the far field . Yeah , the near field 's pretty good .
  PhD A: So wha what is , uh {disfmarker} what 's causing that ?
  Professor B: Well , we got a {disfmarker} a video projector in here , uh , and , uh {disfmarker} which we keep on during every {disfmarker} every session we record ,
  PhD A: Yeah .
  Professor B: which , you know , I {disfmarker} I {disfmarker} w we were aware of
  PhD A: Uh - huh .
  Professor B: but {disfmarker} but we thought it wasn't a bad thing .
  PhD A: Yeah .
  Professor B: I mean , that 's a nice noise source . Uh , and there 's also the , uh {disfmarker} uh , air conditioning .
  PhD A: Hmm .
  Professor B: Which , uh , you know , is a pretty low frequency kind of thing .
  PhD A: Mm - hmm .
  Professor B: But {disfmarker} but , uh {disfmarker} So , those are {disfmarker} those are major components , I think ,
  PhD A: I see .
  Professor B: uh , for the stationary kind of stuff .
  PhD A: Mmm .
  Professor B: Um , but , um , it , uh {disfmarker} I guess , I {disfmarker} maybe I said this last week too but it {disfmarker} it {disfmarker} it really became apparent to us that we need to {disfmarker} to take account of noise . And , uh , so I think when {disfmarker} when he gets done with his prelim study I think {vocalsound} one of the next things we 'd want to do is to take this , uh {disfmarker} uh , noise , uh , processing stuff and {disfmarker} and , uh {disfmarker} uh , synthesize some speech from it .
  PhD A: When are his prelims ?
  Professor B: And then {disfmarker} Um , I think in about , um , a little less than two weeks .
  PhD A: Oh . Wow .
  Professor B: Yeah . Yeah . So . Uh , it might even be sooner . Uh , let 's see , this is the sixteenth , seventeenth ? Yeah , I don't know if he 's before {disfmarker} It might even be in a week .
  PhD A: So , I
  Professor B: A week ,
  PhD A: Huh . I {disfmarker} I guessed that they were gonna do it some time during the semester
  Professor B: week and a half .
  PhD A: but they 'll do it any time , huh ?
  Professor B: They seem to be {disfmarker} Well , the semester actually is starting up .
  PhD A: Is it already ?
  Professor B: Yeah , the semester 's late {disfmarker} late August they start here .
  PhD A: Yikes .
  Professor B: So they do it right at the beginning of the semester .
  PhD A: Yeah .
  Professor B: Yeah . So , uh {disfmarker} Yep . I mean , that {disfmarker} that was sort of one {disfmarker} I mean , the overall results seemed to be first place in {disfmarker} in {disfmarker} in the case of either , um , artificial reverberation or a modest sized training set . Uh , either way , uh , i uh , it helped a lot . And {disfmarker} But if you had a {disfmarker} a really big training set , a recognizer , uh , system that was capable of taking advantage of a really large training set {disfmarker} I thought that {disfmarker} One thing with the HTK is that is has the {disfmarker} as we 're using {disfmarker} the configuration we 're using is w s is {disfmarker} being bound by the terms of Aurora , we have all those parameters just set as they are . So even if we had a hundred times as much data , we wouldn't go out to , you know , ten or t or a hundred times as many Gaussians or anything . So , um , it 's kind of hard to take advantage of {disfmarker} of {disfmarker} of big chunks of data . Uh , whereas the other one does sort of expand as you have more training data .
  PhD C: Mm - hmm .
  PhD D: Mmm , yeah .
  Professor B: It does it automatically , actually . And so , um , uh , that one really benefited from the larger set . And it was also a diverse set with different noises and so forth . Uh , so , um , that , uh {disfmarker} that seemed to be {disfmarker} So , if you have that {disfmarker} that better recognizer that can {disfmarker} that can build up more parameters , and if you , um , have the natural room , which in this case has a p a pretty bad signal - to - noise ratio , then in that case , um , the right thing to do is just do {disfmarker} u use speaker adaptation . And {disfmarker} and not bother with {disfmarker} with this acoustic , uh , processing . But I think that that would not be true if we did some explicit noise - processing as well as , uh , the convolutional kind of things we were doing .
  PhD C: Mm - hmm .
  Professor B: So . That 's sort of what we found .
  PhD D: Hmm .
  PhD A: I , um {disfmarker} {vocalsound} uh , started working on the uh {disfmarker} Mississippi State recognizer . So , I got in touch with Joe and {disfmarker} and , uh , from your email and things like that .
  PhD D: Oh , OK .
  PhD A: And , uh , they added me to the list {disfmarker} uh , the mailing list .
  PhD D: OK , great .
  PhD A: And he gave me all of the pointers and everything that I needed . And so I downloaded the , um {disfmarker} There were two things , uh , that they had to download . One was the , uh , I guess the software . And another wad {disfmarker} was a , um , sort of like a sample {disfmarker} a sample run . So I downloaded the software and compiled all of that . And it compiled fine .
  PhD D: Eight .
  PhD A: No problems .
  PhD D: Oh , eh , great .
  PhD A: And , um , I grabbed the sample stuff but I haven't , uh , compiled it .
  PhD D: That sample was released only yesterday or the day before , right ?
  PhD A: No {disfmarker} Well , I haven't grabbed that one yet . So there 's two .
  PhD D: Oh , there is another short sample set {disfmarker}
  PhD A: There was another short one , yeah .
  PhD D: o o sample .
  PhD A: And so I haven't grabbed the latest one that he just , uh , put out yet .
  PhD D: OK . Oh , OK . F Yeah , OK .
  PhD A: So . Um , but , the software seemed to compile fine and everything , so . And , um , So .
  Professor B: Is there any word yet about the issues about , um , adjustments for different feature sets or anything ?
  PhD A: No , I {disfmarker} I d You asked me to write to him and I think I forgot to ask him about that . Or if I did ask him , he didn't reply .
  Professor B: Yeah .
  PhD A: I {disfmarker} I don't remember yet . Uh , I 'll {disfmarker} I 'll d I 'll double check that and ask him again .
  Professor B: Yeah . Yeah , it 's like that {disfmarker} that could r turn out to be an important issue for us .
  PhD D: Hmm . Mmm .
  PhD A: Yeah . Yeah .
  Professor B: Yeah .
  PhD D: Cuz they have it {disfmarker}
  PhD A: Maybe I 'll send it to the list . Yeah .
  PhD D: Cuz they have , uh , already frozen those in i insertion penalties and all those stuff is what {disfmarker} I feel . Because they have this document explaining the recognizer .
  PhD A: Uh - huh .
  PhD D: And they have these tables with , uh , various language model weights , insertion penalties .
  PhD A: OK , I haven't seen that one yet .
  PhD D: u
  PhD A: So .
  PhD D: Uh , it 's th it 's there on that web .
  PhD A: OK .
  PhD D: And , uh , on that , I mean , they have run some experiments using various insertion penalties and all those {disfmarker}
  PhD A: And so they 've picked {disfmarker} the values .
  PhD D: Yeah , I think they pi p
  PhD A: Oh , OK .
  PhD D: yeah , they picked the values from {disfmarker}
  PhD A: OK .
  Professor B: For r w what test set ?
  PhD D: Uh , p the one that they have reported is a NIST evaluation , Wall Street Journal .
  Professor B: But that has nothing to do with what we 're testing on , right ?
  PhD C: Mm - hmm .
  PhD D: You know . No . So they 're , like {disfmarker} um {disfmarker} So they are actually trying to , uh , fix that {disfmarker} those values using the clean , uh , training part of the Wall Street Journal . Which is {disfmarker} I mean , the Aurora . Aurora has a clean subset .
  Professor B: Right .
  PhD D: I mean , they want to train it and then this {disfmarker} they 're going to run some evaluations .
  Professor B: So they 're set they 're setting it based on that ?
  PhD D: Yeah .
  Professor B: OK . So now , we may come back to the situation where we may be looking for a modification of the features to account for the fact that we can't modify these parameters .
  PhD A: Yeah .
  Professor B: But , um ,
  PhD D: Yeah .
  Professor B: uh {disfmarker} but it 's still worth , I think , just {disfmarker} since {disfmarker} you know , just chatting with Joe about the issue .
  PhD A: Yeah , OK . Do you think that 's something I should just send to him
  Professor B: Um {disfmarker}
  PhD A: or do you think I should send it to this {disfmarker} there 's an {disfmarker} a m a mailing list .
  Professor B: Well , it 's not a secret . I mean , we 're , you know , certainly willing to talk about it with everybody , but I think {disfmarker} I think that , um {disfmarker} um , it 's probably best to start talking with him just to {disfmarker}
  PhD A: OK .
  Professor B: Uh @ @ {comment} you know , it 's a dialogue between two of you about what {disfmarker} you know , what does he think about this and what {disfmarker} what {disfmarker} you know {disfmarker} what could be done about it .
  PhD A: Yeah . OK .
  Professor B: Um , if you get ten people in {disfmarker} involved in it there 'll be a lot of perspectives based on , you know , how {disfmarker}
  PhD A: Yeah .
  Professor B: you know .
  PhD A: Right .
  Professor B: Uh {disfmarker} But , I mean , I think it all should come up eventually ,
  PhD A: OK .
  Professor B: but if {disfmarker} if {disfmarker} if there is any , uh , uh , way to move in {disfmarker} a way that would {disfmarker} that would , you know , be more open to different kinds of features . But if {disfmarker} if , uh {disfmarker} if there isn't , and it 's just kind of shut down and {disfmarker} and then also there 's probably not worthwhile bringing it into a larger forum where {disfmarker} where political issues will come in .
  PhD A: Yeah . OK .
  PhD D: Oh . So this is now {disfmarker} it 's {disfmarker} it 's compiled under Solaris ?
  PhD A: Yeah .
  PhD D: Yeah , OK .
  PhD A: Yep .
  PhD D: Because he {disfmarker} there was some mail r saying that it 's {disfmarker} may not be stable for Linux and all those .
  PhD A: Yeah . Yeah , i that was a particular version .
  PhD D: SUSI
  PhD A: Yeah , SUSI or whatever it was
  PhD D: yeah . Yeah , yeah .
  PhD A: but we don't have that .
  PhD D: Yeah , OK .
  PhD A: So . Should be OK .
  PhD D: OK , that 's fine .
  PhD A: Yeah , it compiled fine actually .
  PhD D: Yeah .
  PhD A: No {disfmarker} no errors . Nothing . So .
  Professor B: Uh , this is slightly off topic
  PhD D: That 's good .
  Professor B: but , uh , I noticed , just glancing at the , uh , Hopkins workshop , uh , web site that , uh , um {disfmarker} one of the thing I don't know {disfmarker} Well , we 'll see how much they accomplish , but one of the things that they were trying to do in the graphical models thing was to put together a {disfmarker} a , uh , tool kit for doing , uh r um , arbitrary graphical models for , uh , speech recognition .
  PhD A: Hmm .
  Professor B: So {disfmarker} And Jeff , uh {disfmarker} the two Jeffs were
  PhD A: Who 's the second Jeff ?
  Professor B: Uh {disfmarker} Oh , uh , do you know Geoff Zweig ?
  PhD A: No .
  Professor B: Oh . Uh , he {disfmarker} he , uh {disfmarker} he was here for a couple years
  PhD A: Oh , OK .
  Professor B: and he , uh {disfmarker} got his PHD . He {disfmarker} And he 's , uh , been at IBM for the last couple years .
  PhD A: Oh , OK .
  Professor B: So .
  PhD A: Wow . That would be neat .
  Professor B: Uh , so he did {disfmarker} he did his PHD on dynamic Bayes - nets , uh , for {disfmarker} for speech recognition . He had some continuity built into the model , presumably to handle some , um , inertia in the {disfmarker} in the production system , and , um {disfmarker}
  PhD A: Hmm .
  Professor B: So .
  PhD D: Hmm .
  PhD C: Um , I 've been playing with , first , the , um , VAD . Um , {vocalsound} so it 's exactly the same approach , but the features that the VAD neural network use are , uh , MFCC after noise compensation . Oh , I think I have the results .
  Professor B: What was it using before ?
  PhD C: Before it was just P L
  PhD D: 
  PhD C: So .
  PhD D: Yeah , it was actually {disfmarker} No . Not {disfmarker} I mean , it was just the noisy features I guess .
  PhD C: Yeah ,
  PhD D: Yeah , yeah , yeah ,
  PhD C: noisy {disfmarker} noisy features .
  PhD D: not compensated .
  PhD C: Um {disfmarker} This is what we get after {disfmarker} This {disfmarker} So , actually , we , yeah , here the features are noise compensated and there is also the LDA filter . Um , and then it 's a pretty small neural network which use , um , {vocalsound} nine frames of {disfmarker} of six features from C - zero to C - fives , plus the first derivatives . And it has one hundred hidden units .
  PhD A: Is that nine frames u s uh , centered around the current frame ? Or {disfmarker}
  PhD C: Yeah . Mm - hmm .
  Professor B: S so , I 'm {disfmarker} I 'm sorry , there 's {disfmarker} there 's {disfmarker} there 's how many {disfmarker} how many inputs ?
  PhD C: So it 's twelve times nine .
  Professor B: Twelve times nine inputs , and a hundred , uh , hidden .
  PhD C: Hidden and
  PhD D: Two outputs .
  PhD C: two outputs .
  Professor B: Two outputs . OK . So I guess about eleven thousand parameters , which {disfmarker} actually shouldn't be a problem , even in {disfmarker} in small phones . Yeah .
  PhD C: Mm - hmm .
  PhD A: So , I 'm {disfmarker} I 'm {disfmarker} s so what is different between this and {disfmarker} and what you {disfmarker}
  PhD C: It should be OK . So the previous syst It 's based on the system that has a fifty - three point sixty - six percent improvement . It 's the same system . The only thing that changed is the n a p eh {disfmarker} a es the estimation of the silence probabilities .
  PhD A: Ah . OK .
  PhD C: Which now is based on , uh , cleaned features .
  Professor B: And , it 's a l it 's a lot better .
  PhD A: Wow .
  PhD C: Yeah .
  Professor B: That 's great .
  PhD C: Um {disfmarker} So it 's {disfmarker} it 's not bad , but the problem is still that the latency is too large .
  Professor B: What 's the latency ?
  PhD C: Because {disfmarker} um {disfmarker} the {disfmarker} the latency of the VAD is two hundred and twenty milliseconds . And , uh , the VAD is used uh , i for on - line normalization , and it 's used before the delta computation . So if you add these components it goes t to a hundred and seventy , right ?
  Professor B: I {disfmarker} I 'm confused . You started off with two - twenty and you ended up with one - seventy ?
  PhD C: With two an two hundred and seventy .
  Professor B: Two - seventy .
  PhD C: If {disfmarker} Yeah , if you add the c delta comp delta computation
  Professor B: Oh .
  PhD C: which is done afterwards . Um {disfmarker}
  Professor B: So it 's two - twenty . I the is this {disfmarker} are these twenty - millisecond frames ? Is that why ? Is it after downsampling ? or {disfmarker}
  PhD C: The two - twenty is one hundred milliseconds for the um {disfmarker} No , it 's forty milliseconds for t for the , uh , uh , cleaning of the speech . Um {disfmarker} then there is , um , the neural network which use nine frames . So it adds forty milliseconds .
  Professor B: a OK .
  PhD C: Um , after that , um , you have the um , filtering of the silence probabilities . Which is a million filter it , and it creates a one hundred milliseconds delay . So , um {disfmarker}
  Professor B: 
  PhD D: Plus there is a delta at the input .
  PhD C: Yeah , and there is the delta at the input which is ,
  Professor B: One hundred milliseconds for smoothing .
  PhD C: um {disfmarker} So it 's {disfmarker} @ @ {disfmarker}
  Professor B: Uh , median .
  PhD C: 
  PhD D: It 's like forty plus {disfmarker} forty {disfmarker} plus {disfmarker}
  Professor B: And then forty {disfmarker}
  PhD C: Mmm . Forty {disfmarker} This forty plus twenty , plus one hundred .
  Professor B: forty p 
  PhD C: Uh {disfmarker}
  PhD D: So it 's two hundred actually .
  PhD C: Yeah , there are twenty that comes from {disfmarker} There is ten that comes from the LDA filters also . Right ?
  PhD D: Oh , OK .
  PhD C: Uh , so it 's two hundred and ten , yeah .
  PhD D: If you are using {disfmarker}
  Professor B: Uh {disfmarker}
  PhD C: Plus the frame ,
  PhD D: t If you are using three frames {disfmarker}
  PhD C: so it 's two - twenty .
  PhD D: If you are phrasing f {comment} using three frames , it is thirty here for delta .
  PhD C: Yeah , I think it 's {disfmarker} it 's five frames , but .
  PhD D: So five frames , that 's twenty . OK , so it 's who un {comment} two hundred and ten .
  Professor B: Uh , p Wait a minute . It 's forty {disfmarker} {vocalsound} forty for the {disfmarker} for the cleaning of the speech ,
  PhD C: So . Forty cleaning .
  Professor B: forty for the I N {disfmarker} ANN , a hundred for the smoothing .
  PhD C: Yeah .
  Professor B: Well , but at ten {disfmarker} ,
  PhD C: Twenty for the delta .
  Professor B: Twenty for delta .
  PhD D: At th {nonvocalsound} At the input . I mean , that 's at the input to the net .
  PhD C: Yeah .
  Professor B: Delta at input to net ?
  PhD D: And there i
  PhD C: Yeah .
  PhD D: Yeah . So it 's like s five , six cepstrum plus delta at nine {disfmarker} nine frames of {disfmarker}
  Professor B: And then ten milliseconds for {disfmarker}
  PhD D: Fi - There 's an LDA filter .
  Professor B: ten milliseconds for LDA filter , and t and ten {disfmarker} another ten milliseconds you said for the frame ?
  PhD C: For the frame I guess . I computed two - twenty {disfmarker} Yeah , well , it 's {disfmarker} I guess it 's for the fr {disfmarker} the {disfmarker}
  Professor B: OK . And then there 's delta besides that ?
  PhD C: So this is the features that are used by our network and then afterwards , you have to compute the delta on the , uh , main feature stream ,
  Professor B: OK .
  PhD C: which is um , delta and double - deltas , which is fifty milliseconds .
  Professor B: Yeah . No , I mean , the {disfmarker} after the noise part , the forty {disfmarker} the {disfmarker} the other hundred and eighty {disfmarker} Well , I mean , Wait a minute . Some of this is , uh {disfmarker} is , uh {disfmarker} is in parallel , isn't it ? I mean , the LDA {disfmarker} Oh , you have the LDA as part of the V D - uh , VAD ? Or {disfmarker}
  PhD C: The VAD use , uh , LDA filtered features also .
  Professor B: Oh , it does ?
  PhD C: Mm - hmm .
  Professor B: Ah . So in that case there isn't too much in parallel . Uh {disfmarker}
  PhD C: No . There is , um , just downsampling , upsampling , and the LDA .
  Professor B: Um , so the delta at the end is how much ?
  PhD C: It 's fifty .
  PhD D: It 's {disfmarker}
  Professor B: Fifty . Alright . So {disfmarker}
  PhD C: But well , we could probably put the delta , um , {vocalsound} before on - line normalization . It should not that make a big difference ,
  PhD A: What if you used a smaller window for the delta ?
  PhD C: because {disfmarker}
  PhD A: Could that help a little bit ? I mean , I guess there 's a lot of things you could do to {disfmarker}
  PhD C: Yeah .
  Professor B: Yeah .
  PhD C: Yeah ,
  Professor B: So
  PhD C: but , nnn {disfmarker}
  Professor B: Yeah . So if you {disfmarker} if you put the delta before the , uh , ana on - line {disfmarker} If {disfmarker} Yeah {disfmarker}
  PhD C: Mm - hmm .
  Professor B: uh {disfmarker} then {disfmarker} then it could go in parallel .
  PhD C: Cuz i
  Professor B: And then y then you don't have that additive {disfmarker}
  PhD C: Yeah ,
  PhD D: Yep .
  PhD C: cuz the time constant of the on - line normalization is pretty long compared to the delta window ,
  Professor B: OK .
  PhD C: so . It should not make {disfmarker}
  Professor B: OK . And you ought to be able to shove tw , uh {disfmarker} sh uh {disfmarker} pull off twenty milliseconds from somewhere else to get it under two hundred , right ? I mean {disfmarker}
  PhD A: Is two hundred the d
  Professor B: The hundred milla
  PhD C: Mm - hmm .
  Professor B: mill a hundred milliseconds for smoothing is sort of an arbitrary amount . It could be eighty and {disfmarker} and probably do @ @ {disfmarker}
  PhD C: Yeah ,
  PhD A: i a hun
  PhD C: yeah .
  PhD A: uh {disfmarker} Wh - what 's the baseline you need to be under ? Two hundred ?
  Professor B: Well , we don't know . They 're still arguing about it .
  PhD C: 
  PhD A: Oh .
  Professor B: I mean , if it 's two {disfmarker} if {disfmarker} if it 's , uh {disfmarker} if it 's two - fifty , then we could keep the delta where it is if we shaved off twenty . If it 's two hundred , if we shaved off twenty , we could {disfmarker} we could , uh , meet it by moving the delta back .
  PhD A: So , how do you know that what you have is too much if they 're still deciding ?
  Professor B: Uh , we don't , but it 's just {disfmarker} I mean , the main thing is that since that we got burned last time , and {disfmarker} you know , by not worrying about it very much , we 're just staying conscious of it .
  PhD A: Uh - huh . Oh , OK , I see .
  Professor B: And so , th I mean , if {disfmarker} if {disfmarker} if a week before we have to be done someone says , " Well , you have to have fifty milliseconds less than you have now " , it would be pretty frantic around here . So {disfmarker}
  PhD A: Ah , OK .
  Professor B: Uh {disfmarker}
  PhD A: But still , that 's {disfmarker} that 's a pretty big , uh , win . And it doesn't seem like you 're {disfmarker} in terms of your delay , you 're , uh , that {disfmarker}
  Professor B: He added a bit on , I guess , because before we were {disfmarker} we were {disfmarker} had {disfmarker} were able to have the noise , uh , stuff , uh , and the LVA be in parallel .
  PhD C: Hmm .
  Professor B: And now he 's {disfmarker} he 's requiring it to be done first .
  PhD C: Well , but I think the main thing , maybe , is the cleaning of the speech , which takes forty milliseconds or so .
  Professor B: Right . Well , so you say {disfmarker} let 's say ten milliseconds {disfmarker} seconds for the LDA .
  PhD C: And {disfmarker} and {disfmarker} but {disfmarker} the LDA is , well , pretty short right now .
  Professor B: Well , ten . And then forty for the other .
  PhD C: Yeah .
  PhD D: Yeah , the LDA {disfmarker} LDA {disfmarker} we don't know , is , like {disfmarker} is it very crucial for the features , right ?
  PhD C: No . I just {disfmarker} This is the first try .
  PhD D: Yeah .
  Professor B: Right ,
  PhD C: I mean , I {disfmarker} maybe the LDA 's not very useful then .
  Professor B: so you could start pulling back ,
  PhD D: S s h
  Professor B: but {disfmarker}
  PhD D: Yeah ,
  Professor B: But I think you have {disfmarker}
  PhD D: l
  Professor B: I mean , you have twenty for delta computation which y now you 're sort of doing twice , right ? But yo w were you doing that before ?
  PhD C: Mmm . Well , in the proposal , um , the input of the VAD network were just three frames , I think .
  PhD D: On the {disfmarker} in the {disfmarker} Mm - hmm . Just {disfmarker} Yeah , just the static , no delta .
  Professor B: Right .
  PhD C: Uh , static features .
  Professor B: So , what you have now is fort uh , forty for the {disfmarker} the noise , twenty for the delta , and ten for the LDA . That 's seventy milliseconds of stuff which was formerly in parallel ,
  PhD C: 
  Professor B: right ? So I think ,
  PhD C: Mm - hmm .
  Professor B: you know , that 's {disfmarker} that 's the difference as far as the timing , right ?
  PhD C: Yeah .
  Professor B: Um , and you could experiment with cutting various pieces of these back a bit , but {disfmarker} I mean , we 're s we 're not {disfmarker} we 're not in terrible shape .
  PhD A: Yeah , that 's what it seems like to me . It 's pretty good .
  Professor B: Yeah .
  PhD C: Mm - hmm .
  Professor B: It 's {disfmarker} it 's not like it 's adding up to four hundred milliseconds or something .
  PhD A: Where {disfmarker} where is this {disfmarker} where is this fifty - seven point O two in {disfmarker} in comparison to the last evaluation ?
  Professor B: Well , it 's {disfmarker} I think it 's better than anything , uh , anybody got .
  PhD A: Oh , is that right ?
  PhD C: Yeah . The best was fifty - four point five .
  Professor B: Yeah .
  PhD D: Point s
  PhD A: Oh .
  Professor B: Yeah . Uh
  PhD C: And our system was forty - nine , but with the neural network .
  PhD A: Wow . So this is almost ten percent .
  Professor B: With the f with the neural net . Yeah , and r and {disfmarker}
  PhD C: It would
  PhD D: Yeah , so this is {disfmarker} this is like the first proposal . The proposal - one . It was forty - four , actually .
  Professor B: Yeah . Yeah . And we still don't have the neural net in . So {disfmarker} so it 's {disfmarker}
  PhD A: Wow .
  Professor B: You know . So it 's {disfmarker} We 're {disfmarker} we 're doing better .
  PhD A: This is {disfmarker} this is really good .
  Professor B: I mean , we 're getting better recognition . I mean , I 'm sure other people working on this are not sitting still either , but {disfmarker}
  PhD A: Yeah .
  Professor B: but {disfmarker} but , uh {disfmarker} Uh , I mean , the important thing is that we learn how to do this better , and , you know . So . Um , Yeah . So , our , um {disfmarker} Yeah , you can see the kind of {disfmarker} kind of numbers that we 're having , say , on SpeechDat - Car which is a hard task , cuz it 's really , um {disfmarker} I think it 's just sort of {disfmarker} sort of reasonable numbers , starting to be . I mean , it 's still terri
  PhD C: Mm - hmm . Yeah , even for a well - matched case it 's sixty percent error rate reduction ,
  Professor B: Yeah .
  PhD C: which is {disfmarker}
  Professor B: Yeah . Probably half . Good !
  PhD C: Um , Yeah . So actually , this is in between {vocalsound} what we had with the previous VAD and what Sunil did with an IDL VAD . Which gave sixty - two percent improvement , right ?
  PhD D: Yeah , it 's almost that .
  PhD C: So {disfmarker}
  PhD D: It 's almost an average somewhere around {disfmarker}
  PhD C: Yeah .
  PhD D: Yeah .
  PhD A: What was that ? Say that last part again ?
  PhD C: So , if you use , like , an IDL VAD , uh , for dropping the frames ,
  PhD D: o o Or the best we can get .
  PhD C: the best that we can get {disfmarker} i That means that we estimate the silence probability on the clean version of the utterances . Then you can go up to sixty - two percent error rate reduction , globally .
  PhD A: Mmm .
  PhD C: Mmm {disfmarker} Yeah .
  PhD A: So that would be even {disfmarker} That wouldn't change this number down here to sixty - two ?
  PhD C: Yeah .
  Professor B: Yeah . So you {disfmarker} you were get
  PhD C: If you add a g good v very good VAD , that works as well as a VAD working on clean speech ,
  PhD A: Yeah . Yeah .
  PhD C: then you wou you would go {disfmarker}
  PhD A: So that 's sort of the best you could hope for .
  PhD C: Mm - hmm .
  PhD A: I see .
  Professor B: Probably . Yeah . So fi si fifty - three is what you were getting with the old VAD .
  PhD C: Yeah .
  Professor B: And , uh {disfmarker} and sixty - two with the {disfmarker} the , you know , quote , unquote , cheating VAD . And fifty - seven is what you got with the real VAD .
  PhD C: Mm - hmm .
  Professor B: That 's great .
  PhD C: Uh , yeah , the next thing is , I started to play {disfmarker} Well , I don't want to worry too much about the delay , no . Maybe it 's better to wait
  Professor B: OK .
  PhD C: for the decision
  Professor B: Yeah .
  PhD C: from the committee . Uh , but I started to play with the , um , {vocalsound} {vocalsound} uh , tandem neural network . Mmm I just did the configuration that 's very similar to what we did for the February proposal . And {disfmarker} Um . So . There is a f a first feature stream that use uh straight MFCC features .
  Professor B: Mm - hmm .
  PhD C: Well , these features actually . And the other stream is the output of a neural network , using as input , also , these , um , cleaned MFCC . Um {disfmarker}
  PhD A: Those are th those are th what is going into the tandem net ?
  PhD C: I don't have the comp Mmm ?
  PhD A: Those two ?
  PhD C: So there is just this feature stream , {comment} the fifteen MFCC plus delta and double - delta .
  Professor B: No .
  PhD A: Yeah ?
  PhD C: Um , so it 's {disfmarker} makes forty - five features {comment} that are used as input to the HTK . And then , there is {disfmarker} there are more inputs that comes from the tandem MLP .
  PhD A: Oh , oh . OK . I see .
  Professor B: Yeah , h he likes to use them both ,
  PhD A: Uh - huh .
  Professor B: cuz then it has one part that 's discriminative ,
  PhD C: Yeah . Um {disfmarker}
  Professor B: one part that 's not .
  PhD A: Right . OK .
  PhD C: So , um , uh , yeah . Right now it seems that {disfmarker} i I just tested on SpeechDat - Car while the experiment are running on your {disfmarker} on TI - digits . Well , it improves on the well - matched and the mismatched conditions , but it get worse on the highly mismatched . Um ,
  PhD A: Compared to these numbers ?
  PhD C: Compared to these numbers , yeah . Um ,
  Professor B: y
  PhD C: like , on the well - match and medium mismatch , the gain is around five percent relative , but it goes down a lot more , like fifteen percent on the HM case .
  Professor B: You 're just using the full ninety features ?
  PhD C:  The {disfmarker}
  Professor B: Y you have ninety features ?
  PhD C: i I have , um {disfmarker} From the networks , it 's twenty - eight . So {disfmarker}
  Professor B: And from the other side it 's forty - five .
  PhD C: So , d i It 's forty - five .
  Professor B: So it 's {disfmarker} you have seventy - three features ,
  PhD C: Yeah .
  Professor B: and you 're just feeding them like that .
  PhD C: Yeah .
  Professor B: There isn't any KLT or anything ?
  PhD C: Mm - hmm . There 's a KLT after the neural network , as {disfmarker} as before .
  PhD A: That 's how you get down to twenty - eight ?
  PhD C: Yeah .
  PhD A: Why twenty - eight ?
  PhD C: I don't know .
  PhD A: Oh .
  PhD C: Uh . It 's {disfmarker} i i i It 's because it 's what we did for the first proposal . We tested , uh , trying to go down
  PhD A: Ah .
  Professor B: It 's a multiple of seven .
  PhD C: and Yeah .
  PhD D: Yeah .
  PhD C: So {disfmarker} Um .
  PhD D: Yeah .
  PhD C: I wanted to do something very similar to the proposal as a first {disfmarker} first try .
  PhD D: Yeah .
  PhD A: I see .
  Professor B: Yeah .
  PhD A: Yeah . That makes sense .
  PhD C: But we have to {disfmarker} for sure , we have to go down , because the limit is now sixty features .
  Professor B: Yeah .
  PhD C: So , uh , we have to find a way to decrease the number of features . Um {disfmarker}
  PhD A: So , it seems funny that {disfmarker} I don't know , maybe I don't u quite understand everything , {comment} but that adding features {disfmarker} I guess {disfmarker} I guess if you 're keeping the back - end fixed . Maybe that 's it . Because it seems like just adding information shouldn't give worse results . But I guess if you 're keeping the number of Gaussians fixed in the recognizer , then {disfmarker}
  Professor B: Well , yeah .
  PhD C: Mmm .
  Professor B: But , I mean , just in general , adding information {disfmarker} Suppose the information you added , well , was a really terrible feature and all it brought in was noise .
  PhD A: Yeah .
  Professor B: Right ? So {disfmarker} so , um {disfmarker} Or {disfmarker} or suppose it wasn't completely terrible , but it was completely equivalent to another one feature that you had , except it was noisier .
  PhD A: Uh - huh .
  Professor B: Right ? In that case you wouldn't necessarily expect it to be better at all .
  PhD A: Oh , yeah , I wasn't necessarily saying it should be better . I 'm just surprised that you 're getting fifteen percent relative worse on the wel
  Professor B: Uh - huh .
  PhD C: But it 's worse .
  Professor B: On the highly mismatched condition .
  PhD A: On the highly mismatch .
  PhD C: Yeah , I {disfmarker}
  PhD A: Yeah .
  Professor B: So , " highly mismatched condition " means that in fact your training is a bad estimate of your test .
  PhD C: Uh - huh .
  Professor B: So having {disfmarker} having , uh , a g a l a greater number of features , if they aren't maybe the right features that you use , certainly can e can easily , uh , make things worse . I mean , you 're right . If you have {disfmarker} if you have , uh , lots and lots of data , and you have {disfmarker} and your {disfmarker} your {disfmarker} your training is representative of your test , then getting more sources of information should just help . But {disfmarker} but it 's {disfmarker} It doesn't necessarily work that way .
  PhD A: Huh .
  PhD C: Mm - hmm .
  Professor B: So I wonder , um , Well , what 's your {disfmarker} what 's your thought about what to do next with it ?
  PhD C: Um , I don't know . I 'm surprised , because I expected the neural net to help more when there is more mismatch , as it was the case for the {disfmarker}
  Professor B: Mm - hmm .
  PhD D: So , was the training set same as the p the February proposal ? OK .
  PhD C: Yeah , it 's the same training set , so it 's TIMIT with the TI - digits ' , uh , noises , uh , added .
  PhD D: 
  Professor B: Mm - hmm .
  PhD C: Um {disfmarker}
  Professor B: Well , we might {disfmarker} uh , we might have to experiment with , uh better training sets . Again . But ,
  PhD C: Mm - hmm .
  Professor B: I {disfmarker} The other thing is , I mean , before you found that was the best configuration , but you might have to retest those things now that we have different {disfmarker} The rest of it is different , right ? So , um , uh , For instance , what 's the effect of just putting the neural net on without the o other {disfmarker} other path ?
  PhD C: Mm - hmm .
  Professor B: I mean , you know what the straight features do .
  PhD C: Yeah .
  Professor B: That gives you this . You know what it does in combination .
  PhD C: Mm - hmm .
  Professor B: You don't necessarily know what {disfmarker}
  PhD A: What if you did the {disfmarker} Would it make sense to do the KLT on the full set of combined features ? Instead of just on the {disfmarker}
  PhD C: Yeah . I g I guess . Um . The reason I did it this ways is that in February , it {disfmarker} we {disfmarker} we tested different things like that , so , having two KLT , having just a KLT for a network , or having a global KLT .
  PhD A: Oh , I see .
  PhD C: And {disfmarker}
  PhD A: So you tried the global KLT before
  PhD C: Well {disfmarker}
  PhD A: and it didn't really {disfmarker}
  PhD C: Yeah . And , uh , th Yeah .
  PhD A: I see .
  PhD C: The differences between these configurations were not huge , but {disfmarker} it was marginally better with this configuration .
  PhD A: Uh - huh . Uh - huh .
  Professor B: But , yeah , that 's obviously another thing to try ,
  PhD C: Um .
  Professor B: since things are {disfmarker} things are different .
  PhD C: Mm - hmm . Mm - hmm .
  Professor B: And I guess if the {disfmarker} These are all {disfmarker} so all of these seventy - three features are going into , um , the , uh {disfmarker} the HMM .
  PhD C: Yeah .
  Professor B: And is {disfmarker} are {disfmarker} i i are {disfmarker} are any deltas being computed of tha of them ?
  PhD C: Of the straight features , yeah .
  Professor B: n Not of the {disfmarker}
  PhD C: So . But n th the , um , tandem features are u used as they are .
  Professor B: Are not .
  PhD C: So , yeah , maybe we can add some context from these features also as {disfmarker} Dan did in {disfmarker} in his last work .
  Professor B: Could . i Yeah , but the other thing I was thinking was , um {disfmarker} Uh , now I lost track of what I was thinking . But .
  PhD A: What is the {disfmarker} You said there was a limit of sixty features or something ?
  PhD C: Mm - hmm .
  PhD A: What 's the relation between that limit and the , um , forty - eight {disfmarker} uh , forty eight hundred bits per second ?
  Professor B: Oh , I know what I was gonna say .
  PhD C: Um , not {disfmarker} no relation .
  Professor B: No relation .
  PhD A: So I {disfmarker} I {disfmarker} I don't understand ,
  PhD C: The f the forty - eight hundred bits is for transmission of some features .
  PhD A: because i I mean , if you 're only using h
  PhD C: And generally , i it {disfmarker} s allows you to transmit like , fifteen , uh , cepstrum .
  Professor B: The issue was that , um , this is supposed to be a standard that 's then gonna be fed to somebody 's recognizer somewhere which might be , you know , it {disfmarker} it might be a concern how many parameters are use {disfmarker} u used and so forth . And so , uh , they felt they wanted to set a limit . So they chose sixty . Some people wanted to use hundreds of parameters and {disfmarker} and that bothered some other people .
  PhD A: Uh - huh .
  Professor B: u And so they just chose that . I {disfmarker} I {disfmarker} I think it 's kind of r arbitrary too . But {disfmarker} but that 's {disfmarker} that 's kind of what was chosen . I {disfmarker} I remembered what I was going to say . What I was going to say is that , um , maybe {disfmarker} {vocalsound} maybe with the noise removal , uh , these things are now more correlated . So you have two sets of things that are kind of uncorrelated , uh , within themselves , but they 're pretty correlated with one another .
  PhD C: Mm - hmm .
  Professor B: And , um , they 're being fed into these , uh , variants , only Gaussians and so forth , and {disfmarker} and , uh ,
  PhD C: Mm - hmm .
  Professor B: so maybe it would be a better idea now than it was before to , uh , have , uh , one KLT over everything , to de - correlate it .
  PhD C: Mm - hmm . Yeah , I see .
  Professor B: Maybe . You know .
  PhD D: What are the S N Rs in the training set , TIMIT ?
  PhD C: It 's , uh , ranging from zero to clean ? Yeah . From zero to clean .
  PhD D: Mm - hmm .
  Professor B: Yeah . So we found this {disfmarker} this , uh {disfmarker} this Macrophone data , and so forth , that we were using for these other experiments , to be pretty good .
  PhD C: Mm - hmm .
  Professor B: So that 's {disfmarker} i after you explore these other alternatives , that might be another way to start looking , is {disfmarker} is just improving the training set .
  PhD C: Mm - hmm .
  Professor B: I mean , we were getting , uh , lots better recognition using that , than {disfmarker} Of course , you do have the problem that , um , u i {comment} we are not able to increase the number of Gaussians , uh , or anything to , uh , uh , to match anything . So we 're only improving the training of our feature set , but that 's still probably something .
  PhD A: So you 're saying , add the Macrophone data to the training of the neural net ? The tandem net ?
  Professor B: Yeah , that 's the only place that we can train .
  PhD A: Yeah .
  Professor B: We can't train the other stuff with anything other than the standard amount ,
  PhD A: Right .
  Professor B: so . Um , um {disfmarker}
  PhD A: What {disfmarker} what was it trained on again ? The one that you used ?
  PhD C: It 's TIMIT with noise .
  PhD A: Uh - huh .
  Professor B: Yeah .
  PhD C: So , yeah , it 's rather a small {disfmarker}
  Professor B: How big is the net , by the way ?
  PhD C: Um , Uh , it 's , uh , five hundred hidden units . And {disfmarker}
  Professor B: And again , you did experiments back then where you made it bigger and it {disfmarker} and that was {disfmarker} that was sort of the threshold point . Much less than that , it was worse ,
  PhD C: Yeah .
  Professor B: and
  PhD C: Yeah .
  Professor B: much more than that , it wasn't much better . Hmm .
  PhD C: Yeah . @ @ ?
  PhD D: So is it {disfmarker} is it though the performance , big relation in the high ma high mismatch has something to do with the , uh , cleaning up that you {disfmarker} that is done on the TIMIT after adding noise ?
  PhD C: 
  PhD D: So {disfmarker} it 's {disfmarker} i All the noises are from the TI - digits ,
  PhD C: Yeah .
  PhD D: right ? So you {disfmarker} i
  PhD C: Um {disfmarker} They {disfmarker} k uh {disfmarker}
  PhD D: Well , it it 's like the high mismatch of the SpeechDat - Car after cleaning up , maybe having more noise than the {disfmarker} the training set of TIMIT after clean {disfmarker} s after you do the noise clean - up .
  PhD C: Mmm .
  PhD D: I mean , earlier you never had any compensation , you just trained it straight away .
  PhD C: Mm - hmm .
  PhD D: So it had like all these different conditions of S N Rs , actually in their training set of neural net .
  PhD C: Mm - hmm . Mm - hmm .
  PhD D: But after cleaning up you have now a different set of S N Rs , right ?
  PhD C: Yeah .
  PhD D: For the training of the neural net .
  PhD C: Mm - hmm .
  PhD D: And {disfmarker} is it something to do with the mismatch that {disfmarker} that 's created after the cleaning up , like the high mismatch {disfmarker}
  PhD C: You mean the {disfmarker} the most noisy occurrences on SpeechDat - Car might be a lot more noisy than {disfmarker}
  PhD D: Mm - hmm . Of {disfmarker} that {disfmarker} I mean , the SNR after the noise compensation of the SpeechDat - Car .
  Professor B: Oh , so {disfmarker} Right . So the training {disfmarker} the {disfmarker} the neural net is being trained with noise compensated stuff .
  PhD C: Maybe .
  PhD D:  Yeah .
  PhD C: Yeah , yeah .
  Professor B: Which makes sense ,
  PhD D: Yeah .
  Professor B: but , uh , you 're saying {disfmarker} Yeah , the noisier ones are still going to be , even after our noise compensation , are still gonna be pretty noisy .
  PhD D: Yeah .
  PhD C: Mm - hmm .
  PhD D: Yeah , so now the after - noise compensation the neural net is seeing a different set of S N Rs than that was originally there in the training set . Of TIMIT . Because in the TIMIT it was zero to some clean .
  Professor B: Right . Yes .
  PhD D: So the net saw all the SNR @ @ conditions .
  Professor B: Right .
  PhD D: Now after cleaning up it 's a different set of SNR .
  Professor B: Right .
  PhD D: And that SNR may not be , like , com covering the whole set of S N Rs that you 're getting in the SpeechDat - Car .
  Professor B: Right , but the SpeechDat - Car data that you 're seeing is also reduced in noise by the noise compensation .
  PhD C: Yeah .
  PhD D: Yeah , yeah , yeah , yeah , it is . But , I 'm saying , there could be some {disfmarker} some issues of {disfmarker}
  Professor B: So .
  PhD C: Mm - hmm .
  Professor B: Yeah .
  PhD C: Well , if the initial range of SNR is different , we {disfmarker} the problem was already there before . And {disfmarker}
  Professor B: Yeah .
  PhD C: Because {disfmarker} Mmm {disfmarker}
  Professor B: Yeah , I mean , it depends on whether you believe that the noise compensation is equally reducing the noise on the test set and the training set .
  PhD C: Hmm .
  Professor B: Uh {disfmarker}
  PhD D: On the test set , yeah . 
  Professor B: Right ? I mean , you 're saying there 's a mismatch in noise that wasn't there before ,
  PhD D: Hmm . Mm - hmm .
  Professor B: but if they were both the same before , then if they were both reduic reduced equally , then , there would not be a mismatch .
  PhD D: Mm - hmm .
  Professor B: So , I mean , this may be {disfmarker} Heaven forbid , this noise compensation process may be imperfect , but . Uh , so maybe it 's treating some things differently .
  PhD C: Yeah , uh {disfmarker}
  PhD D: Well , I {disfmarker} I don't know . I {disfmarker} I just {disfmarker} that could be seen from the TI - digits , uh , testing condition because , um , the noises are from the TI - digits , right ? Noise {disfmarker}
  PhD C: Yeah . So {disfmarker}
  PhD D: So cleaning up the TI - digits and if the performance goes down in the TI - digits mismatch {disfmarker} high mismatch like this {disfmarker}
  PhD C: Clean training , yeah .
  PhD D: on a clean training , or zero DB testing .
  PhD C: Yeah , we 'll {disfmarker} so we 'll see . Uh .
  PhD D: Yeah .
  PhD C: Maybe .
  PhD D: Then it 's something to do .
  PhD C: Mm - hmm .
  Professor B: I mean , one of the things about {disfmarker}
  PhD C: Yeah .
  Professor B: I mean , the Macrophone data , um , I think , you know , it was recorded over many different telephones .
  PhD C: Mm - hmm .
  Professor B: And , um , so , there 's lots of different kinds of acoustic conditions . I mean , it 's not artificially added noise or anything . So it 's not the same . I don't think there 's anybody recording over a car from a car , but {disfmarker} I think it 's {disfmarker} it 's varied enough that if {disfmarker} if doing this adjustments , uh , and playing around with it doesn't , uh , make it better , the most {disfmarker} uh , it seems like the most obvious thing to do is to improve the training set . Um {disfmarker} I mean , what we were {disfmarker} uh {disfmarker} the condition {disfmarker} It {disfmarker} it gave us an enormous amount of improvement in what we were doing with Meeting Recorder digits , even though there , again , these m Macrophone digits were very , very different from , uh , what we were going on here . I mean , we weren't talking over a telephone here . But it was just {disfmarker} I think just having a {disfmarker} a nice variation in acoustic conditions was just a good thing .
  PhD C: Mm - hmm . Yep .
  PhD D: Mmm .
  PhD C: Yeah , actually {vocalsound} to s eh , what I observed in the HM case is that the number of deletion dramatically increases . It {disfmarker} it doubles .
  Professor B: Number of deletions .
  PhD C: When I added the num the neural network it doubles the number of deletions . Yeah , so I don't you know {vocalsound} how to interpret that , but , mmm {disfmarker}
  Professor B: Yeah . Me either .
  PhD C: t
  PhD A: And {disfmarker} and did {disfmarker} an other numbers stay the same ? Insertion substitutions stay the same ?
  PhD C: They p stayed the same ,
  PhD A: Roughly ?
  PhD C: they {disfmarker} maybe they are a little bit uh , lower .
  PhD A: Uh - huh .
  PhD C: They are a little bit better . Yeah . But {disfmarker}
  Professor B: Did they increase the number of deletions even for the cases that got better ?
  PhD C: Mm - hmm .
  Professor B: Say , for the {disfmarker} I mean , it {disfmarker}
  PhD C: No , it doesn't .
  Professor B: So it 's only the highly mismatched ?
  PhD C: No .
  Professor B: And it {disfmarker} Remind me again , the " highly mismatched " means that the {disfmarker}
  PhD C: Clean training and {disfmarker}
  Professor B: Uh , sorry ?
  PhD C: It 's clean training {disfmarker} Well , close microphone training and distant microphone , um , high speed , I think .
  Professor B: Close mike training {disfmarker}
  PhD C: Well {disfmarker} The most noisy cases are the distant microphone for testing .
  Professor B: Right . So {disfmarker} Well , maybe the noise subtraction is subtracting off speech .
  PhD C: Separating . Yeah .
  Professor B: Wh
  PhD C: But {disfmarker} Yeah . I mean , but without the neural network it 's {disfmarker} well , it 's better . It 's just when we add the neural networks .
  Professor B: Yeah , right .
  PhD C: The feature are the same except that {disfmarker}
  Professor B: Uh , that 's right , that 's right . Um {disfmarker}
  PhD A: Well that {disfmarker} that says that , you know , the , um {disfmarker} the models in {disfmarker} in , uh , the recognizer are really paying attention to the neural net features .
  PhD C: Yeah .
  PhD A: Uh .
  PhD C: Mm - hmm .
  Professor B: But , yeah , actually {disfmarker} {nonvocalsound} the TIMIT noises {pause} are sort of a range of noises and they 're not so much the stationary driving kind of noises , right ? It 's {disfmarker} it 's pretty different . Isn't it ?
  PhD C: Uh , there is a car noise . So there are f just four noises . Um , uh , " Car " , I think , " Babble " ,
  PhD D: " Babble . "
  PhD C: " Subway " , right ? and {disfmarker}
  PhD D: " Street " or " Airport " or something .
  PhD C: and {disfmarker} " Street " isn't {disfmarker}
  PhD D: Or " Train station " .
  PhD C: " Train station " , yeah .
  PhD D: Yeah .
  PhD C: So {disfmarker} it 's mostly {disfmarker} Well , " Car " is stationary ,
  Professor B: Mm - hmm .
  PhD C: " Babble " , it 's a stationary background plus some voices ,
  Professor B: Mm - hmm .
  PhD C: some speech over it . And the other two are rather stationary also .
  Professor B: Well , I {disfmarker} I think that if you run it {disfmarker} Actually , you {disfmarker} maybe you remember this . When you {disfmarker} in {disfmarker} in the old experiments when you ran with the neural net only , and didn't have this side path , um , uh , with the {disfmarker} the pure features as well , did it make things better to have the neural net ?
  PhD C: Mm - hmm .
  Professor B: Was it about the same ? Uh , w i
  PhD C: It was {disfmarker} b a little bit worse .
  Professor B: Than {disfmarker} ?
  PhD C: Than just the features , yeah .
  Professor B: So , until you put the second path in with the pure features , the neural net wasn't helping at all .
  PhD C: Mm - hmm .
  Professor B: Well , that 's interesting .
  PhD C: It was helping , uh , if the features are b were bad ,
  Professor B: Yeah .
  PhD C: I mean . Just plain P L Ps or M F
  Professor B: Yeah .
  PhD C: C Cs . as soon as we added LDA on - line normalization , and {vocalsound} all these things , then {disfmarker}
  Professor B: They were doing similar enough things . Well , I still think it would be k sort of interesting to see what would happen if you just had the neural net without the side thing .
  PhD C: Yeah ,
  Professor B: And {disfmarker} and the thing I {disfmarker} I have in mind is , uh , maybe you 'll see that the results are not just a little bit worse .
  PhD C: mm - hmm .
  Professor B: Maybe that they 're a lot worse . You know ? And , um {disfmarker} But if on the ha other hand , uh , it 's , say , somewhere in between what you 're seeing now and {disfmarker} and {disfmarker} and , uh , what you 'd have with just the pure features , then maybe there is some problem of a {disfmarker} of a , uh , combination of these things , or correlation between them somehow .
  PhD C: Mm - hmm .
  Professor B: If it really is that the net is hurting you at the moment , then I think the issue is to focus on {disfmarker} on , uh , improving the {disfmarker} the net .
  PhD C: Yeah ,
  Professor B: Um .
  PhD C: mm - hmm .
  Professor B: So what 's the overall effe I mean , you haven't done all the experiments but you said it was i somewhat better , say , five percent better , for the first two conditions , and fifteen percent worse for the other one ? But it 's {disfmarker} but of course that one 's weighted lower ,
  PhD C: Y yeah , oh . Yeah .
  Professor B: so I wonder what the net effect is .
  PhD C: I d I {disfmarker} I think it 's {disfmarker} it was one or two percent . That 's not that bad , but it was l like two percent relative worse on SpeechDat - Car . I have to {disfmarker} to check that . Well , I have {disfmarker} I will .
  PhD D: Well , it will {disfmarker} overall it will be still better even if it is fifteen percent worse , because the fifteen percent worse is given like f w twenty - five {disfmarker} point two five eight .
  Professor B: Right .
  PhD C: Mm - hmm . Hmm .
  Professor B: Right . So the {disfmarker} so the worst it could be , if the others were exactly the same , is four ,
  PhD D: Is it like {disfmarker}
  Professor B: and {disfmarker} and , uh , in fact since the others are somewhat better {disfmarker}
  PhD D: Yeah , so it 's four . Is i So either it 'll get cancelled out , or you 'll get , like , almost the same .
  Professor B: Uh .
  PhD C: Yeah , it was {disfmarker} it was slightly worse .
  PhD D: Slightly bad . Yeah .
  PhD C: Um ,
  Professor B: Yeah , it should be pretty close to cancelled out .
  PhD D: Yeah .
  PhD A: You know , I 've been wondering about something .
  PhD C: Mm - hmm .
  PhD A: In the , um {disfmarker} a lot of the , um {disfmarker} the Hub - five systems , um , recently have been using LDA . and {disfmarker} and they , um {disfmarker} They run LDA on the features right before they train the models . So there 's the {disfmarker} the LDA is {disfmarker} is right there before the H M
  PhD D: Yeah .
  PhD A: So , you guys are using LDA but it seems like it 's pretty far back in the process .
  PhD D: Uh , this LDA is different from the LDA that you are talking about . The LDA that you {disfmarker} saying is , like , you take a block of features , like nine frames or something , {comment} and then do an LDA on it ,
  PhD A: Yeah . Uh - huh .
  PhD D: and then reduce the dimensionality to something like twenty - four or something like that .
  PhD A: Yeah , you c you c you can .
  PhD D: And then feed it to HMM .
  PhD A: I mean , it 's {disfmarker} you know , you 're just basically i
  PhD D: Yeah , so this is like a two d two dimensional tile .
  PhD A: You 're shifting the feature space . Yeah .
  PhD D: So this is a two dimensional tile . And the LDA that we are f applying is only in time , not in frequency {disfmarker} high cost frequency . So it 's like {disfmarker} more like a filtering in time , rather than doing a r
  PhD A: Ah . OK . So what i what about , um {disfmarker} i u what i w I mean , I don't know if this is a good idea or not , but what if you put {disfmarker} ran the other kind of LDA , uh , on your features right before they go into the HMM ?
  PhD D: Uh , it {disfmarker}
  PhD C: Mm - hmm . No , actually , I think {disfmarker} i
  PhD D: m
  PhD C: Well . What do we do with the ANN is {disfmarker} is something like that except that it 's not linear . But it 's {disfmarker} it 's like a nonlinear discriminant analysis .
  PhD A: Yeah . Right , it 's the {disfmarker} It 's {disfmarker} Right . The {disfmarker} So {disfmarker} Yeah , so it 's sort of like {disfmarker}
  PhD C: But .
  PhD A: The tandem stuff is kind of like i nonlinear LDA .
  PhD C: Yeah . It 's {disfmarker}
  PhD A: I g
  PhD C: Yeah .
  PhD A: Yeah .
  Professor B: Yeah .
  PhD A: But I mean , w but the other features that you have , um , th the non - tandem ones ,
  PhD C: Uh . Mm - hmm . Yeah , I know . That {disfmarker} that {disfmarker} Yeah . Well , in the proposal , they were transformed u using PCA , but {disfmarker}
  PhD A: Uh - huh .
  PhD C: Yeah , it might be that LDA could be better .
  Professor B: The a the argument i is kind of i in {disfmarker} and it 's not like we really know , but the argument anyway is that , um , uh , we always have the prob I mean , discriminative things are good . LDA , neural nets , they 're good .
  PhD A: Yeah .
  Professor B: Uh , they 're good because you {disfmarker} you {disfmarker} you learn to distinguish between these categories that you want to be good at distinguishing between . And PCA doesn't do that . It {disfmarker} PAC - PCA {disfmarker} low - order PCA throws away pieces that are uh , maybe not {disfmarker} not gonna be helpful just because they 're small , basically .
  PhD A: Right .
  Professor B: But , uh , the problem is , training sets aren't perfect and testing sets are different . So you f you {disfmarker} you face the potential problem with discriminative stuff , be it LDA or neural nets , that you are training to discriminate between categories in one space but what you 're really gonna be g getting is {disfmarker} is something else .
  PhD A: Uh - huh .
  Professor B: And so , uh , Stephane 's idea was , uh , let 's feed , uh , both this discriminatively trained thing and something that 's not . So you have a good set of features that everybody 's worked really hard to make ,
  PhD A: Yeah .
  Professor B: and then , uh , you {disfmarker} you discriminately train it , but you also take the path that {disfmarker} that doesn't have that ,
  PhD A: Uh - huh .
  Professor B: and putting those in together . And that {disfmarker} that seem So it 's kind of like a combination of the {disfmarker} uh , what , uh , Dan has been calling , you know , a feature {disfmarker} uh , you know , a feature combination versus posterior combination or something . It 's {disfmarker} it 's , you know , you have the posterior combination but then you get the features from that and use them as a feature combination with these {disfmarker} these other things . And that seemed , at least in the last one , as he was just saying , he {disfmarker} he {disfmarker} when he only did discriminative stuff , i it actually was {disfmarker} was {disfmarker} it didn't help at all in this particular case .
  PhD A: Yeah .
  Professor B: There was enough of a difference , I guess , between the testing and training . But by having them both there {disfmarker} The fact is some of the time , the discriminative stuff is gonna help you .
  PhD A: Mm - hmm .
  Professor B: And some of the time it 's going to hurt you ,
  PhD A: Right .
  Professor B: and by combining two information sources if , you know {disfmarker} if {disfmarker} if {disfmarker}
  PhD A: So you wouldn't necessarily then want to do LDA on the non - tandem features because now you 're doing something to them that {disfmarker}
  Professor B: That i i I think that 's counter to that idea .
  PhD A: Yeah , right .
  Professor B: Now , again , it 's {disfmarker} we 're just trying these different things . We don't really know what 's gonna work best . But if that 's the hypothesis , at least it would be counter to that hypothesis to do that .
  PhD A: Right .
  Professor B: Um , and in principle you would think that the neural net would do better at the discriminant part than LDA .
  PhD A: Right . Yeah . Well {disfmarker} y
  Professor B: Though , maybe not .
  PhD A: Yeah . Exactly . I mean , we , uh {disfmarker} we were getting ready to do the tandem , uh , stuff for the Hub - five system , and , um , Andreas and I talked about it , and the idea w the thought was , " Well , uh , yeah , that i you know {disfmarker} th the neural net should be better , but we should at least have uh , a number , you know , to show that we did try the LDA in place of the neural net , so that we can you know , show a clear path .
  Professor B: Right .
  PhD A: You know , that you have it without it , then you have the LDA , then you have the neural net , and you can see , theoretically . So . I was just wondering {disfmarker} I {disfmarker} I {disfmarker}
  Professor B: Well , I think that 's a good idea .
  PhD A: Yeah .
  Professor B: Did {disfmarker} did you do that
  PhD A: Um . No .
  Professor B: or {disfmarker} tha that 's a {disfmarker}
  PhD A: That 's what {disfmarker} that 's what we 're gonna do next as soon as I finish this other thing . So .
  Professor B: Yeah . Yeah . No , well , that 's a good idea . I {disfmarker} I {disfmarker}
  PhD A: We just want to show .
  Professor B: i Yeah .
  PhD A: I mean , it {disfmarker} everybody believes it ,
  Professor B: Oh , no it 's a g
  PhD A: but you know , we just {disfmarker}
  Professor B: No , no , but it might not {disfmarker} not even be true .
  PhD A: Yeah .
  Professor B: I mean , it 's {disfmarker} it 's {disfmarker} it 's {disfmarker} it 's {disfmarker} it 's a great idea . I mean , one of the things that always disturbed me , uh , in the {disfmarker} the resurgence of neural nets that happened in the eighties was that , um , a lot of people {disfmarker} Because neural nets were pretty easy to {disfmarker} to use {disfmarker} a lot of people were just using them for all sorts of things without , uh , looking at all into the linear , uh {disfmarker} uh , versions of them .
  PhD A: Yeah . Mm - hmm . Yeah .
  Professor B: And , uh , people were doing recurrent nets but not looking at IIR filters , and {disfmarker} You know , I mean , uh , so I think , yeah , it 's definitely a good idea to try it .
  PhD A: Yeah , and everybody 's putting that on their {vocalsound} systems now , and so , I that 's what made me wonder about this ,
  Professor B: Well , they 've been putting them in their systems off and on for ten years ,
  PhD A: but .
  Professor B: but {disfmarker} but {disfmarker} but , uh ,
  PhD A: Yeah , what I mean is it 's {disfmarker} it 's like in the Hub - five evaluations , you know , and you read the system descriptions and everybody 's got , {vocalsound} you know , LDA on their features .
  Professor B: And now they all have that . I see .
  PhD A: And so .
  Professor B: Yeah .
  PhD A: Uh .
  PhD C: It 's the transformation they 're estimating on {disfmarker} Well , they are trained on the same data as the final HMM are .
  PhD A: Yeah , so it 's different . Yeah , exactly . Cuz they don't have these , you know , mismatches that {disfmarker} that you guys have .
  PhD C: Mm - hmm .
  PhD A: So that 's why I was wondering if maybe it 's not even a good idea .
  PhD C: Mm - hmm .
  PhD A: I don't know . I {disfmarker} I don't know enough about it ,
  PhD C: Mm - hmm .
  PhD A: but {disfmarker} Um .
  Professor B: I mean , part of why {disfmarker} I {disfmarker} I think part of why you were getting into the KLT {disfmarker} Y you were describing to me at one point that you wanted to see if , uh , you know , getting good orthogonal features was {disfmarker} and combining the {disfmarker} the different temporal ranges {disfmarker} was the key thing that was happening or whether it was this discriminant thing , right ? So you were just trying {disfmarker} I think you r I mean , this is {disfmarker} it doesn't have the LDA aspect but th as far as the orthogonalizing transformation , you were trying that at one point , right ?
  PhD C: Mm - hmm .
  Professor B: I think you were .
  PhD C: Mm - hmm . Yeah .
  Professor B: Does something . It doesn't work as well . Yeah . Yeah .
  PhD D: So , yeah , I 've been exploring a parallel VAD without neural network with , like , less latency using SNR and energy , um , after the cleaning up . So what I 'd been trying was , um , uh {disfmarker} After the b after the noise compensation , n I was trying t to f find a f feature based on the ratio of the energies , that is , cl after clean and before clean . So that if {disfmarker} if they are , like , pretty c close to one , which means it 's speech . And if it is n if it is close to zero , which is {disfmarker} So it 's like a scale @ @ probability value . So I was trying , uh , with full band and multiple bands , m ps uh {disfmarker} separating them to different frequency bands and deriving separate decisions on each bands , and trying to combine them . Uh , the advantage being like it doesn't have the latency of the neural net if it {disfmarker} if it can
  Professor B: Mm - hmm .
  PhD D: g And {pause} it gave me like , uh , one point {disfmarker} One {disfmarker} more than one percent relative improvement . So , from fifty - three point six it went to fifty f four point eight . So it 's , like , only slightly more than a percent improvement ,
  Professor B: Mm - hmm .
  PhD D: just like {disfmarker} Which means that it 's {disfmarker} it 's doing a slightly better job than the previous VAD ,
  Professor B: Mm - hmm .
  PhD D: uh , at a l lower delay .
  Professor B: Mm - hmm .
  PhD D: Um , so , um {disfmarker}
  Professor B: But {disfmarker} i d I 'm sorry ,
  PhD D: so {disfmarker} u
  Professor B: does it still have the median {pause} filter stuff ?
  PhD D: It still has the median filter .
  Professor B: So it still has most of the delay ,
  PhD D: So {disfmarker}
  Professor B: it just doesn't {disfmarker}
  PhD D: Yeah , so d with the delay , that 's gone is the input , which is the sixty millisecond . The forty plus {pause} twenty .
  Professor B: Well , w i
  PhD D: At the input of the neural net you have this , uh , f nine frames of context plus the delta .
  Professor B: Oh , plus the delta ,
  PhD C: Mm - hmm .
  Professor B: right . OK .
  PhD D: Yeah . So that delay , plus the LDA .
  Professor B: Mm - hmm .
  PhD D: Uh , so the delay is only the forty millisecond of the noise cleaning , plus the hundred millisecond smoothing at the output .
  Professor B: Mm - hmm . Mm - hmm .
  PhD D: Um . So . Yeah . So the {disfmarker} the {disfmarker} di the biggest {disfmarker} The problem f for me was to find a consistent threshold that works {pause} well across the different databases , because I t I try to make it work on tr SpeechDat - Car
  Professor B: Mm - hmm .
  PhD D: and it fails on TI - digits , or if I try to make it work on that it 's just the Italian or something , it doesn't work on the Finnish .
  Professor B: Mm - hmm .
  PhD D: So , um . So there are {disfmarker} there was , like , some problem in balancing the deletions and insertions when I try different thresholds .
  Professor B: Mm - hmm .
  PhD D: So {disfmarker} The {disfmarker} I 'm still trying to make it better by using some other features from the {disfmarker} after the p clean up {disfmarker} maybe , some , uh , correlation {disfmarker} auto - correlation or some s additional features of {disfmarker} to mainly the improvement of the VAD . I 've been trying .
  Professor B: Now this {disfmarker} this {disfmarker} this , uh , " before and after clean " , it sounds like you think that 's a good feature . That {disfmarker} that , it {disfmarker} you th think that the , uh {disfmarker} the {disfmarker} i it appears to be a good feature , right ?
  PhD D: Mm - hmm .
  Professor B: What about using it in the neural net ?
  PhD D: Yeah .
  PhD C: Yeah , eventually we could {disfmarker} could just
  PhD D: Yeah , so {disfmarker} Yeah , so that 's the {disfmarker} Yeah . So we 've been thinking about putting it into the neural net also .
  Professor B: Yeah .
  PhD D: Because they did {disfmarker} that itself {disfmarker}
  PhD C: Then you don't have to worry about the thresholds and {disfmarker}
  PhD D: There 's a threshold and {disfmarker} Yeah .
  Professor B: Yeah .
  PhD C: but just {disfmarker}
  PhD D: Yeah . So that {disfmarker} that 's , uh {disfmarker}
  Professor B: Yeah . So if we {disfmarker} if we can live with the latency or cut the latencies elsewhere , then {disfmarker} then that would be a , uh , good thing .
  PhD D: Yeah . Yeah .
  Professor B: Um , anybody {disfmarker} has anybody {disfmarker} you guys or {disfmarker} or Naren , uh , somebody , tried the , uh , um , second th second stream thing ? Uh .
  PhD D: Oh , I just {disfmarker} I just h put the second stream in place and , uh ran one experiment , but just like {disfmarker} just to know that everything is fine .
  Professor B: Uh - huh .
  PhD D: So it was like , uh , forty - five cepstrum plus twenty - three mel {disfmarker} log mel .
  Professor B: Yeah .
  PhD D: And {disfmarker} and , just , like , it gave me the baseline performance of the Aurora , which is like zero improvement .
  Professor B: Yeah . Yeah .
  PhD D: So I just tried it on Italian just to know that everything is {disfmarker} But I {disfmarker} I didn't export anything out of it because it was , like , a weird feature set .
  Professor B: Yeah .
  PhD D: So .
  Professor B: Yeah . Well , what I think , you know , would be more what you 'd want to do is {disfmarker} is {disfmarker} is , uh , put it into another neural net . Right ?
  PhD C: Mm - hmm .
  PhD D: Yeah , yeah , yeah , yeah .
  Professor B: And then {disfmarker} But , yeah , we 're {disfmarker} we 're not quite there yet . So we have to {vocalsound} figure out the neural nets , I guess .
  PhD C: Yeah .
  PhD D: The uh , other thing I was wondering was , um , if the neural net , um , has any {disfmarker} because of the different noise con unseen noise conditions for the neural net , where , like , you train it on those four noise conditions , while you are feeding it with , like , a additional {disfmarker} some four plus some {disfmarker} f few more conditions which it hasn't seen , actually ,
  PhD C: Mm - hmm .
  PhD D: from the {disfmarker} f f while testing .
  PhD C: Yeah , yeah . Right .
  PhD D: Um {disfmarker} instead of just h having c uh , those cleaned up t cepstrum , sh should we feed some additional information , like {disfmarker} The {disfmarker} the {disfmarker} We have the VAD flag . I mean , should we f feed the VAD flag , also , at the input so that it {disfmarker} it has some additional discriminating information at the input ?
  PhD C: Hmm - hmm ! Um {disfmarker}
  Professor B: Wh - uh , the {disfmarker} the VAD what ?
  PhD D: We have the VAD information also available at the back - end .
  Professor B: Uh - huh .
  PhD D: So if it is something the neural net is not able to discriminate the classes {disfmarker}
  Professor B: Yeah .
  PhD D: I mean {disfmarker} Because most of it is sil I mean , we have dropped some silence f We have dropped so silence frames ?
  Professor B: Mm - hmm .
  PhD D: No , we haven't dropped silence frames still .
  PhD C: Uh , still not . Yeah .
  PhD D: Yeah . So {disfmarker}
  PhD C: Th
  PhD D: the b b biggest classification would be the speech and silence . So , by having an additional , uh , feature which says " this is speech and this is nonspeech " , I mean , it certainly helps in some unseen noise conditions for the neural net .
  PhD A: What {disfmarker} Do y do you have that feature available for the test data ?
  PhD D: Well , I mean , we have {disfmarker} we are transferring the VAD to the back - end {disfmarker} feature to the back - end . Because we are dropping it at the back - end after everything {disfmarker} all the features are computed .
  PhD A: Oh , oh , I see .
  PhD D: So {disfmarker}
  PhD A: I see .
  PhD D: so the neural {disfmarker} so that is coming from a separate neural net or some VAD .
  PhD A: OK . OK .
  PhD D: Which is {disfmarker} which is certainly giving a
  PhD A: So you 're saying , feed that , also , into {pause} the neural net .
  PhD D: to {disfmarker} Yeah . So it it 's an {disfmarker} additional discriminating information .
  PhD A: Yeah . Yeah . Right .
  PhD D: So that {disfmarker}
  Professor B: You could feed it into the neural net . The other thing {comment} you could do is just , um , p modify the , uh , output probabilities of the {disfmarker} of the , uh , uh , um , neural net , tandem neural net , {comment} based on the fact that you have a silence probability .
  PhD D: Mm - hmm .
  Professor B: Right ?
  PhD C: Mm - hmm .
  Professor B: So you have an independent estimator of what the silence probability is , and you could multiply the two things , and renormalize .
  PhD C: Yeah .
  Professor B: Uh , I mean , you 'd have to do the nonlinearity part and deal with that . Uh , I mean , go backwards from what the nonlinearity would , you know {disfmarker} would be .
  PhD D: Through {disfmarker} t to the soft max .
  Professor B: But {disfmarker} but , uh {disfmarker}
  PhD C: Yeah , so {disfmarker} maybe , yeah , when {disfmarker}
  PhD A: But in principle wouldn't it be better to feed it in ? And let the net do that ?
  Professor B: Well , u Not sure .
  PhD A: Hmm .
  Professor B: I mean , let 's put it this way . I mean , y you {disfmarker} you have this complicated system with thousands and thousand parameters
  PhD A: Yeah .
  Professor B: and you can tell it , uh , " Learn this thing . " Or you can say , " It 's silence ! Go away ! " I mean , I mean , i Doesn't {disfmarker} ? I think {disfmarker} I think the second one sounds a lot more direct .
  PhD A: What {disfmarker} what if you {disfmarker}
  Professor B: Uh .
  PhD A: Right . So , what if you then , uh {disfmarker} since you know this , what if you only use the neural net on the speech portions ?
  Professor B: Well , uh ,
  PhD C: That 's what {disfmarker}
  PhD A: Well , I guess that 's the same . Uh , that 's similar .
  Professor B: Yeah , I mean , y you 'd have to actually run it continuously ,
  PhD A: But I mean {disfmarker} I mean , train the net only on {disfmarker}
  Professor B: but it 's {disfmarker} @ @ {disfmarker} Well , no , you want to train on {disfmarker} on the nonspeech also , because that 's part of what you 're learning in it , to {disfmarker} to {disfmarker} to generate , that it 's {disfmarker} it has to distinguish between .
  PhD D: Speech .
  PhD A: But I mean , if you 're gonna {disfmarker} if you 're going to multiply the output of the net by this other decision , uh , would {disfmarker} then you don't care about whether the net makes that distinction , right ?
  Professor B: Well , yeah . But this other thing isn't perfect .
  PhD A: Ah .
  Professor B: So that you bring in some information from the net itself .
  PhD A: Right , OK . That 's a good point .
  Professor B: Yeah . Now the only thing that {disfmarker} that bothers me about all this is that I {disfmarker} I {disfmarker} I {disfmarker} The {disfmarker} the fact {disfmarker} i i It 's sort of bothersome that you 're getting more deletions .
  PhD C: Yeah . But {disfmarker} So I might maybe look at , is it due to the fact that um , the probability of the silence at the output of the network , is , uh ,
  Professor B: Is too high .
  PhD C: too {disfmarker} too high or {disfmarker}
  Professor B: Yeah . So maybe {disfmarker} So {disfmarker}
  PhD C: If it 's the case , then multiplying it again by {disfmarker} i by something ?
  PhD D: It may not be {disfmarker} it {disfmarker}
  Professor B: Yeah .
  PhD C: Mm - hmm .
  PhD D: Yeah , it {disfmarker} it may be too {disfmarker} it 's too high in a sense , like , everything is more like a , um , flat probability .
  Professor B: Yeah .
  PhD C: Oh - eee - hhh .
  PhD D: So , like , it 's not really doing any distinction between speech and nonspeech {disfmarker}
  PhD C: Uh , yeah .
  PhD D: or , I mean , different {disfmarker} among classes .
  Professor B: Yeah .
  PhD C: Mm - hmm .
  PhD A: Be interesting to look at the {disfmarker} Yeah , for the {disfmarker} I wonder if you could do this . But if you look at the , um , highly mism high mismat the output of the net on the high mismatch case and just look at , you know , the distribution versus the {disfmarker} the other ones , do you {disfmarker} do you see more peaks or something ?
  PhD C: Yeah . Yeah , like the entropy of the {disfmarker} the output ,
  PhD A: Yeah .
  Professor B: Yeah , for instance .
  PhD C: or {disfmarker}
  Professor B: But I {disfmarker} bu
  PhD C: It {disfmarker} it seems that the VAD network doesn't {disfmarker} Well , it doesn't drop , uh , too many frames because the dele the number of deletion is reasonable . But it 's just when we add the tandem , the final MLP , and then {disfmarker}
  Professor B: Yeah . Now the only problem is you don't want to ta I guess wait for the output of the VAD before you can put something into the other system ,
  PhD C: u
  Professor B: cuz that 'll shoot up the latency a lot , right ? Am I missing something here ?
  PhD C: But {disfmarker}
  PhD D: Mm - hmm .
  PhD C: Yeah . Right .
  Professor B: Yeah . So that 's maybe a problem with what I was just saying . But {disfmarker} but {disfmarker} I I guess {disfmarker}
  PhD A: But if you were gonna put it in as a feature it means you already have it by the time you get to the tandem net , right ?
  PhD D: Um , well . We {disfmarker} w we don't have it , actually ,
  Professor B: No .
  PhD D: because it 's {disfmarker} it has a high rate energy {disfmarker}
  PhD A: Ah .
  PhD D: the VAD has a {disfmarker}
  Professor B: Yeah .
  PhD A: OK .
  Professor B: It 's kind of done in {disfmarker} I mean , some of the things are , not in parallel , but certainly , it would be in parallel with the {disfmarker} with a tandem net .
  PhD A: Right .
  Professor B: In time . So maybe , if that doesn't work , um {disfmarker} But it would be interesting to see if that was the problem , anyway . And {disfmarker} and {disfmarker} and then I guess another alternative would be to take the feature that you 're feeding into the VAD , and feeding it into the other one as well .
  PhD C: Mm - hmm .
  Professor B: And then maybe it would just learn {disfmarker} learn it better .
  PhD C: Mm - hmm .
  Professor B: Um {disfmarker} But that 's {disfmarker} Yeah , that 's an interesting thing to try to see , if what 's going on is that in the highly mismatched condition , it 's , um , causing deletions by having this silence probability up {disfmarker} up too high ,
  PhD C: Mm - hmm .
  Professor B: at some point where the VAD is saying it 's actually speech .
  PhD C: Yeah .
  Professor B: Which is probably true .
  PhD C: So , m
  Professor B: Cuz {disfmarker} Well , the V A if the VAD said {disfmarker} since the VAD is {disfmarker} is {disfmarker} is right a lot , uh {disfmarker}
  PhD C: Yeah .
  Professor B: Hmm . Anyway . Might be .
  PhD C: Mm - hmm .
  Professor B: Yeah . Well , we just started working with it . But these are {disfmarker} these are some good ideas I think .
  PhD C: Mm - hmm . Yeah , and the other thing {disfmarker} Well , there are other issues maybe for the tandem , like , uh , well , do we want to , w uh n Do we want to work on the targets ? Or , like , instead of using phonemes , using more context dependent units ?
  PhD A: For the tandem net you mean ?
  PhD C: Well , I 'm {disfmarker} Yeah .
  PhD A: Hmm .
  PhD C: I 'm thinking , also , a w about Dan 's work where he {disfmarker} he trained {vocalsound} a network , not on phoneme targets but on the HMM state targets . And {disfmarker} it was giving s slightly better results .
  Professor B: Problem is , if you are going to run this on different m test sets , including large vocabulary ,
  PhD C: Yeah . Yeah .
  Professor B: um ,
  PhD C: Uh {disfmarker}
  Professor B: I think {disfmarker}
  PhD C: Mmm . I was just thinking maybe about , like , generalized diphones , and {disfmarker} come up with a {disfmarker} a reasonable , not too large , set of context dependent units , and {disfmarker} and {disfmarker} Yeah . And then anyway we would have to reduce this with the KLT .
  Professor B: Yeah .
  PhD C: So . But {disfmarker} I don't know .
  Professor B: Yeah . Well , maybe . But I d I d it {disfmarker} it {disfmarker} i it 's all worth looking at ,
  PhD C: Mm - hmm .
  Professor B: but it sounds to me like , uh , looking at the relationship between this and the {disfmarker} speech noise stuff is {disfmarker} is {disfmarker} is probably a key thing .
  PhD C: Mm - hmm .
  Professor B: That and the correlation between stuff .
  PhD A: So if , uh {disfmarker} if the , uh , high mismatch case had been more like the , uh , the other two cases {comment} in terms of giving you just a better performance , {comment} how would this number have changed ?
  PhD C: Mm - hmm . Oh , it would be {disfmarker} Yeah . Around five percent better , I guess . If {disfmarker} if {disfmarker} i
  PhD A: y Like sixty ?
  Professor B: Well , we don't know what 's it 's gonna be the TI - digits yet . He hasn't got the results back yet .
  PhD C: Yeah . If you extrapolate the SpeechDat - Car well - matched and medium - mismatch , it 's around , yeah , maybe five .
  PhD A: Uh - huh . Yeah . So this would be sixty - two ?
  Professor B: Sixty - two .
  PhD A: Which is {disfmarker}
  Professor B: Yeah .
  PhD C: Sixty - two , yeah .
  PhD D: Somewhere around sixty , must be . Right ? Yeah .
  PhD C: Well , it 's around five percent , because it 's {disfmarker} s Right ? If everything is five percent .
  PhD D: Yeah . Yeah .
  PhD A: All the other ones were five percent ,
  PhD C: Mm - hmm .
  PhD A: the {disfmarker}
  Professor B: Yeah .
  PhD C: I d I d I just have the SpeechDat - Car right now , so {disfmarker}
  PhD A: Yeah .
  PhD C: It 's running {disfmarker} it shou we should have the results today during the afternoon ,
  PhD A: Hmm .
  PhD C: but {disfmarker} Well .
  Professor B: Hmm . Well {disfmarker} Um {disfmarker} So I won't be here for {disfmarker}
  PhD A: When {disfmarker} When do you leave ?
  Professor B: Uh , I 'm leaving next Wednesday . May or may not be in in the morning . I leave in the afternoon . Um ,
  PhD A: But you 're {disfmarker}
  Professor B: so I {disfmarker}
  PhD A: are you {disfmarker} you 're not gonna be around this afternoon ?
  Professor B: Yeah .
  PhD A: Oh .
  Professor B: Oh , well . I 'm talking about next week . I 'm leaving {disfmarker} leaving next Wednesday .
  PhD A: Uh - huh .
  Professor B: This afternoon {disfmarker} uh {disfmarker} Oh , right , for the Meeting meeting ? Yeah , that 's just cuz of something on campus .
  PhD A: Ah , OK , OK .
  Professor B: Yeah . But , um , yeah , so next week I won't , and the week after I won't , cuz I 'll be in Finland . And the week after that I won't . By that time you 'll be {disfmarker} {comment} Uh , you 'll both be gone {pause} from here . So there 'll be no {disfmarker} definitely no meeting on {disfmarker} on September sixth . Uh ,
  PhD A: What 's September sixth ?
  Professor B: and {disfmarker} Uh , that 's during Eurospeech .
  PhD A: Oh , oh , right . OK .
  Professor B: So , uh , Sunil will be in Oregon . Uh , Stephane and I will be in Denmark . Uh {disfmarker} Right ? So it 'll be a few weeks , really , before we have a meeting of the same cast of characters . Um , but , uh {disfmarker} I guess , just {disfmarker} I mean , you guys should probably meet . And maybe Barry {disfmarker} Barry will be around . And {disfmarker} and then uh , uh , we 'll start up again with Dave and {disfmarker} Dave and Barry and Stephane and us on the , uh , twentieth . No . Thirteenth ? About a month ?
  PhD A: So , uh , you 're gonna be gone for the next three weeks or something ?
  Professor B: I 'm gone for two and a half weeks starting {disfmarker} starting next Wed - late next Wednesday .
  PhD A: So that 's {disfmarker} you won't be at the next three of these meetings . Is that right ?
  Professor B: Uh , I won't {disfmarker} it 's probably four because of {disfmarker} is it three ? Let 's see , twenty - third , thirtieth , sixth . That 's right , next three . And the {disfmarker} the third one won't {disfmarker} probably won't be a meeting , cuz {disfmarker} cuz , uh , Su - Sunil , Stephane , and I will all not be here .
  PhD A: Oh , right . Right .
  Professor B: Um {disfmarker} Mmm . {comment} So it 's just , uh , the next two where there will be {disfmarker} there , you know , may as well be meetings ,
  PhD A: OK .
  Professor B: but I just won't be at them . And then starting up on the thirteenth , {nonvocalsound} uh , we 'll have meetings again but we 'll have to do without Sunil here somehow .
  PhD A: When do you go back ?
  Professor B: So .
  PhD D: Thirty - first , August .
  Professor B: Yeah . Yeah . So . Cool .
  PhD A: When is the evaluation ? November , or something ?
  Professor B: Yeah , it was supposed to be November fifteenth . Has anybody heard anything different ?
  PhD C: I don't know . The meeting in {disfmarker} is the five and six of December . So {disfmarker}
  PhD D: p s It 's like {disfmarker} Yeah , it 's tentatively all full . Yeah .
  PhD C: Mm - hmm .
  PhD D: Uh , that 's a proposed date , I guess .
  PhD C: Yeah , um {disfmarker} so the evaluation should be on a week before or {disfmarker}
  PhD A: Yeah .
  Professor B: Yep . But , no , this is good progress . So . Uh {disfmarker} OK .
  PhD A: Should we do digits ?
  Professor B: Guess we 're done . Digits ? Yep .
  PhD A: OK .
  Professor B: It 's a wrap .
input_prefix: What did the team discuss about the most noisy cases?