A: OK , we 're on .
C: OK ,
C: what are we talking about today ?
B: I don't know .
B: Do you have news from the conference talk ?
B: Uh ,
B: that was programmed for yesterday I guess .
C: Uh D: Yesterday
C: Uh D: Yesterday morning on video conference .
C: Uh ,
B: Well
C: oh ,
E: Oh . Conference call .
C: I 'm sorry .
C: I know now I know what you 're talking about .
C: No , nobody 's told me anything .
B: Alright .
A: Oh , this was the , uh , talk where they were supposed to try to decide B: To to decide what to do ,
B: yeah .
D: Yeah .
A: Ah , right .
C: Yeah .
C: No , that would have been a good thing to find out before this meeting ,
C: that 's .
C: No , I have no I have no idea .
C: Um ,
C: Uh ,
C: so
C: I mean , let 's let 's assume for right now that we 're just kind of plugging on ahead ,
B: Yeah .
C: because even if they tell us that , uh , the rules are different , uh , we 're still interested in doing what we 're doing .
C: So what are you doing ?
B: Mm - hmm .
B: Uh , well , we 've a little bit worked on trying to see , uh , what were the bugs and the problem with the latencies .
D: To improve B: So ,
B: We took first we took the LDA filters
B: and , uh , we designed new filters ,
B: using uh recursive filters actually .
C: So when you say " we " , is that something Sunil is doing
C: or is that ?
B: I 'm sorry ?
C: Who is doing that ?
B: Uh , us .
C: Oh , oh .
B: Yeah .
C: Oh , OK .
D: But B: So we took the filters the FIR filters and we <comment> designed , uh , IIR filters that have the same frequency response .
C: Mm - hmm .
B: Well , similar , but that have shorter delays .
C: Mm - hmm .
B: So they had two filters ,
B: one for the low frequency bands
B: and another for the high frequency bands .
B: And so we redesigned two filters .
B: And the low frequency band has sixty - four milliseconds of delay ,
B: and the high frequency band filter has something like eleven milliseconds compared to the two hundred milliseconds of the IIR filters .
B: But it 's not yet test .
B: So we have the filters
B: but we still have to implement a routine that does recursive filtering
C: OK .
B: and C: You you had a discussion with Sunil about this though ?
B: No .
B: No .
C: Uh - huh .
C: Yeah , you should talk with him .
B: Yeah , yeah .
C: Yeah .
C: No , I mean , because the the the the whole problem that happened before was coordination ,
C: right ?
B: Mm - hmm .
C: So so you need to discuss with him what we 're doing ,
B: Yeah .
C: uh , cuz they could be doing the same thing and or something .
B: Mm - hmm .
B: Uh ,
B: I B: yeah ,
B: I don't know if th that 's what they were trying to C: Right .
B: They were trying to do something different like taking , uh well , using filter that takes only a past
B: and
B: this is just a little bit different .
B: But I will I will send him an email and tell him exactly what we are doing ,
B: so .
C: Yeah ,
C: yeah .
C: Um ,
C: I mean B: Um ,
C: We just we just have to be in contact more .
C: I think that the the fact that we we did that with had that thing with the latencies was indicative of the fact that there wasn't enough communication .
B: Mm - hmm .
C: So .
C: OK .
B: Alright .
B: Um ,
B: Yeah .
B: Well , there is w one , um , remark about these filters , that they don't have a linear phase .
B: So ,
C: Right .
B: Well , I don't know ,
B: perhaps it perhaps it doesn't hurt
B: because the phase is almost linear
B: but .
B: Um ,
B: and so , yeah , for the delay I gave you here , it 's it 's , uh , computed on the five hertz modulation frequency ,
B: which is the mmm , well , the most important for speech
B: so .
B: Uh ,
B: this is the first thing .
D: The low f f
C: So that would be , uh , a reduction of a hundred and thirty - six milliseconds ,
B: Yeah .
C: which , uh C: What was the total we ended up with through the whole system ?
B: Three hundred and thirty .
C: So that would be within ?
B: Yeah ,
B: but there are other points actually ,
B: uh ,
B: which will perhaps add some more delay .
B: Is that some other other stuff in the process were perhaps not very um perf well , not very correct ,
B: like the downsampling which w was simply dropping frames .
C: Yeah .
B: Um ,
B: so we will try also to add a nice downsampling
B: having a filter that that C: Uh - huh .
B: well , a low - pass filter at at twenty - five hertz .
B: Uh , because wh when when we look at the LDA filters , well , they are basically low - pass
B: but they leave a lot of what 's above twenty - five hertz .
C: Yeah .
B: Um ,
B: and so , yeah ,
B: this will be another filter which would add ten milliseconds again .
C: Yeah .
B: Um ,
B: yeah ,
B: and then there 's a third thing ,
B: is that , um , basically the way on - line normalization was done uh , is just using this recursion on on the um , um , on the feature stream ,
C: Yeah .
B: and but this is a filter ,
B: so it has also a delay .
B: Uh ,
B: and when we look at this filter actually it has a delay of eighty - five milliseconds .
B: So if we C: Eighty - five .
B: Yeah .
B: If we want to be very correct ,
B: so if we want to the estimation of the mean t t to to be well , the right estimation of the mean , we have to t to take eighty - five milliseconds in the future .
B: Mmm .
C: Hmm !
C: That 's a little bit of a problem .
B: Yeah .
B: Um ,
B: But , well , when we add up everything it 's it will be alright .
B: We would be at six
B: so , sixty - five , plus ten , plus for the downsampling ,
B: plus eighty - five for the on - line normalization .
B: So it 's
C: Uh ,
C: yeah , but then there 's B: plus plus eighty for the neural net and PCA .
C: Oh .
B: So it would be around two hundred and forty B: so , well ,
C: Just just barely in there .
B: plus plus the frames ,
B: but it 's OK .
A: What 's the allowable ?
C: Two - fifty ,
C: unless they changed the rules .
B: Hmm .
C: Which there is there 's some discussion of .
C: But A: What were they thinking of changing it to ?
B: Yeah .
C: Uh , well the people who had very low latency want it to be low uh , very  very very narrow , uh , latency bound .
C: And the people who have longer latency don't .
C: So .
A: Huh .
B: So , yeah .
C: Unfortunately we 're the main ones with long latency ,
C: but
A: Ah !
C: But , uh ,
C: you know , it 's B: Yeah ,
B: and basically the best proposal had something like thirty or forty milliseconds of latency .
C: Yeah .
B: So .
B: Well .
C: Yeah ,
C: so they were basically C: I mean ,
C: they were more or less trading computation for performance
C: and we were , uh , trading latency for performance .
C: And they were dealing with noise explicitly and we weren't ,
C: and so I think of it as complementary ,
C: that if we can put the A: Think of it as what ?
C: Complementary .
A: Hmm .
C: I think the best systems C: so , uh , everything that we did in in a way it was it was just adamantly insisting on going in with a brain damaged system ,
C: which is something actually , we 've done a lot over the last thirteen years .
C: Uh , which is we say , well this is the way we should do it .
C: And then we do it .
C: And then someone else does something that 's straight forward .
C: So , w th w this was a test that largely had additive noise
C: and we did we adde did absolutely nothing explicitly to handle ad additive noise .
A: Right .
C: We just , uh , you know , trained up systems to be more discriminant .
C: And , uh , we did this , uh , RASTA - like filtering
C: which was done in the log domain
C: and was tending to handle convolutional noise .
C: We did we actually did nothing about additive noise .
C: So , um ,
C: the , uh , spectral sub subtraction schemes a couple places did seem to seem to do a nice job .
C: And so ,
C: uh ,
C: we 're talking about putting putting some of that in while still keeping some of our stuff .
C: I think you should be able to end up with a system that 's better than both
C: but clearly the way that we 're operating for this other stuff does involved some latency to to get rid of most of that latency .
C: To get down to forty or fifty milliseconds we 'd have to throw out most of what we 're doing .
C: And and , uh , I don't think there 's any good reason for it in the application actually .
C: I mean , you 're you 're you 're speaking to a recognizer on a remote server
C: and , uh , having a a a quarter second for some processing to clean it up . It doesn't seem like it 's that big a deal .
A: Mm - hmm .
C: These aren't large vocabulary things
C: so the decoder shouldn't take a really long time , and .
C: So .
A: And I don't think anybody 's gonna notice the difference between a quarter of a second of latency and thirty milliseconds of latency .
C: No .
C: What what does wa was your experience when you were doing this stuff with , uh , the the the surgical , uh , uh , microscopes and so forth .
C: Um , how long was it from when somebody , uh , finished an utterance to when , uh , something started happening ?
A: Um , we had a silence detector ,
A: so
A: we would look for the end of an utterance based on the silence detector .
C: Mm - hmm .
A: And I I can't remember now off the top of my head how many frames of silence we had to detect before we would declare it to be the end of an utterance .
C: Mm - hmm .
C: Mm - hmm .
A: Um ,
A: but it was ,
A: uh ,
A: I would say it was probably around the order of two hundred and fifty milliseconds .
C: Yeah ,
C: and that 's when you 'd start doing things .
A: Yeah ,
A: we did the back trace at that point
C: Yeah .
A: to get the answer .
C: Of course that didn't take too long at that point .
A: No ,
A: no it was pretty quick .
C: Yeah .
C: Yeah ,
A: So C: so you you so you had a
C: so you had a a quarter second delay before , uh , plus some little processing time ,
A: this w
A: Right .
C: and then the the microscope would start moving or something .
A: Right .
C: Yeah .
A: Right .
C: And there 's physical inertia there ,
C: so probably the the motion itself was all A: And it felt to , uh , the users that it was instantaneous .
A: I mean , as fast as talking to a person .
A: It th I don't think anybody ever complained about the delay .
C: Yeah ,
C: so you would think as long as it 's under half a second or something .
A: Yeah .
C: Uh , I 'm not an expert on that
C: but .
A: Yeah .
A: I don't remember the exact numbers
A: but
C: Yeah .
A: it was something like that .
A: I don't think you can really tell .
A: A person I don't think a person can tell the difference between , uh , you know , a quarter of a second and a hundred milliseconds ,
A: and C: Yeah .
A: I 'm not even sure if we can tell the difference between a quarter of a second and half a second .
C: Yeah .
A: I mean it just it feels so quick .
C: I mean , basically if you yeah , if you said , uh , um , " what 's the , uh , uh what 's the shortest route to the opera ? "
C: and it took half a second to get back to you ,
A: Yeah .
C: I mean , it would be f
C: I mean , it might even be too abrupt .
C: You might have to put in a s a s a delay .
A: Yeah .
A: I mean , it may feel different than talking to a person
C: Yeah .
A: because when we talk to each other we tend to step on each other 's utterances .
A: So like if I 'm asking you a question , you may start answering before I 'm even done .
C: Yeah .
A: So it it would probably feel different
C: Right .
A: but I don't think it would feel slow .
C: Right .
C: Well , anyway ,
C: I mean , I think C: we could cut we know what else , we could cut down on the neural net time
C: by by , uh , playing around a little bit ,
C: going more into the past ,
C: or something like that .
C: We t we talked about that .
A: So is the latency from the neural net caused by how far ahead you 're looking ?
C: Mm - hmm .
B: Mm - hmm .
C: And there 's also C: well , there 's the neural net and there 's also this , uh , uh , multi - frame , uh , uh , KLT .
A: Wasn't there A: Was it in the , uh , recurrent neural nets where they weren't looking ahead at all ?
C: They weren't looking ahead much .
C: They p they looked ahead a little bit .
A: A little bit .
A: OK .
C: Yeah .
C: Yeah ,
C: I mean , you could do this with a recurrent net .
C: And and then C: But you also could just , um ,
C: I mean , we haven't experimented with this
C: but I imagine you could , um , uh , predict a , uh um , a label , uh , from more in the past than in than than in the future .
C: I mean , we 've d we 've done some stuff with that before .
C: I think it it works OK .
B: Mm - hmm .
C: So .
A: We 've always had usually we used the symmetric windows
A: but
C: Yeah ,
A: I don't think C: but we 've but we played a little bit with with asymmetric , guys .
A: Yeah .
C: You can do it .
C: So .
C: So , that 's what that 's what you 're busy with ,
C: s messing around with this ,
B: Uh , yeah .
C: yeah .
C: And , uh ,
D: Also we were thinking to to , uh , apply the eh , spectral subtraction from Ericsson
B: Yeah .
C: Uh - huh .
D: and to to change the contextual KLT for LDA .
A: Change the what ?
D: The contextual KLT .
A: I 'm missing that last word .
A: Context
C: K KLT .
E: Oh . KLT .
D: KLT A: KLT .
A: Oh , KLT .
C: Mm - hmm .
D: KLT ,
A: Uh - huh .
D: I 'm sorry .
D: Uh , to change and use LDA discriminative .
B: Yeah .
D: But C: Uh - huh .
D: I don't know .
C: Uh ,
A: What is the advantage of that ?
D: Uh B: Well , it 's that by the for the moment we have , uh , something that 's discriminant and nonlinear .
B: And the other is linear but it 's not discriminant at all .
B: Well , it 's it 's a linear transformation , that B: Uh C: So at least just to understand maybe what the difference was between how much you were getting from just putting the frames together and how much you 're getting from the discriminative ,
C: what the nonlinearity does for you or doesn't do for you .
C: Just to understand it a little better I guess .
B: Mmm .
B: Well B: uh B: yeah .
B: Actually what we want to do , perhaps it 's to replace to to have something that 's discriminant but linear , also .
B: And to see if it if it improves ov over over the non - discriminant linear transformation .
B: And if the neural net is better than this
A: Hmm .
B: or , well .
C: Yeah , well , that 's what I meant , is to see whether whether it having the neural net really buys you anything .
B: So .
B: Ye
B: Mmm .
C: Uh , I mean , it doe did look like it buys you something over just the KLT .
B: Yeah .
C: But maybe it 's just the discrimination
C: and and maybe yeah , maybe the nonlinear discrimination isn't necessary .
D: S maybe .
B: Yeah .
B: Mm - hmm .
D: Maybe .
C: Could be .
C: Good good to know .
C: But the other part you were saying was the spectral subtraction ,
C: so you just kind of , uh B: Yeah .
C: At what stage do you do that ?
C: Do you you 're doing that ,
C: um ?
B: So it would be on the
B: um D: We was think
B: on on the mel frequency bands ,
B: so . Yeah ,
D: Yeah ,
D: we no nnn
B: be before everything .
C: OK ,
C: so just do that on the mel f
D: We we was thinking to do before after VAD
D: or
D: Oh , <comment> we don't know exactly when it 's better .
B: Yeah ,
B: um D: Before after VAD
D: or D: and then
C: So so you know that that that the way that they 're B: Um .
C: uh , one thing that would be no good to find out about from this conference call is that what they were talking about , what they 're proposing doing , was having a third party , um , run a good VAD , and and determine boundaries .
D: Yeah .
C: And then given those boundaries , then have everybody do the recognition .
D: Begin to work .
C: The reason for that was that , um , uh if some one p one group put in the VAD and another didn't ,
C: uh , or one had a better VAD than the other
C: since that they 're not viewing that as being part of the the task ,
C: and that any any manufacturer would put a bunch of effort into having some s kind of good speech - silence detection .
C: It still wouldn't be perfect
C: but I mean ,
C: e the argument was " let 's not have that be part of this test . "
C: " Let 's let 's separate that out . "
C: And so ,
C: uh , I guess they argued about that yesterday
C: and ,
C: yeah , I 'm sorry ,
C: I don't don't know the answer
C: but we should find out .
C: I 'm sure we 'll find out soon
C: what they , uh what they decided .
C: So , uh C: Yeah , so there 's the question of the VAD
C: but otherwise it 's it 's on the the , uh the mel fil filter bank , uh , energies I guess ?
D: Mm - hmm .
C: You do doing the ?
B: Mmm , yeah .
D: Mm - hmm .
C: And you 're you 're subtracting in the in the in the I guess it 's power power domain ,
C: uh , or or magnitude domain .
C: Probably power domain , right ?
C: why
B: I guess it 's power domain , yeah .
B: I don't remember exactly .
D: I don't remember .
C: Yeah ,
B: But B: yeah ,
B: so it 's before everything else ,
C: yep .
B: and C: I mean , if you look at the theory , it 's it should be in the power domain
C: but but , uh , I 've seen implementations where people do it in the magnitude domain
B: Yeah .
C: and B: Mmm .
C: I have asked people why
C: and they shrug their shoulders and say , " oh , it works . "
C: So .
B: Yeah .
C: Uh ,
C: and there 's this C: I guess there 's this mysterious C: I mean people who do this a lot I guess have developed little tricks of the trade .
C: I mean , there 's there 's this , um C: you don't just subtract the the estimate of the noise spectrum .
C: You subtract th that times B: A little bit more
B: and C: Or or less ,
B: Yeah .
C: or A: Really ?
B: Yeah .
A: Huh !
C: Yeah .
B: And generated this this ,
C: Uh .
B: um ,
B: so you have the estimation of the power spectra of the noise ,
B: and you multiply this by a factor which is depend dependent on the SNR .
D: Hmm , maybe .
B: So . Well .
A: Hmm !
B: When the speech lev when the signal level is more important , compared to this noise level , the coefficient is small , and around one .
B: But when the power le the s signal level is uh small compared to the noise level , the coefficient is more important .
B: And this reduce actually the music musical noise ,
B: uh
A: Oh !
B: which is more important during silence portions ,
B: when the s the energy 's small .
A: Uh - huh .
A: Hmm !
B: So there are tricks like this
B: but , mmm .
A: Hmm !
C: Yeah .