[speaker001:] We're going? OK.
[speaker002:] OK.
[speaker001:] Sh Close your door on [disfmarker] door on the way out?
[speaker002:] Thanks.
[speaker001:] Thanks.
[speaker002:] Oh.
[speaker001:] Yeah. Probably wanna get this other door, too. OK. So. Um. [vocalsound] [vocalsound] What are we talking about today?
[speaker005:] Uh, well, first there are perhaps these uh Meeting Recorder digits that we tested.
[speaker001:] Oh, yeah. That was kind of uh interesting.
[speaker005:] So.
[speaker001:] The [disfmarker] both the uh [disfmarker] [vocalsound] the SRI System and the oth
[speaker005:] Um.
[speaker001:] And for one thing that [disfmarker] that sure shows the [vocalsound] difference between having a lot of uh training data [vocalsound] or not,
[speaker005:] Of data? Yeah.
[speaker001:] uh, the uh [disfmarker] [vocalsound] The best kind of number we have on the English uh [disfmarker] on near microphone only is [disfmarker] is uh three or four percent.
[speaker005:] Mm hmm.
[speaker001:] And uh it's significantly better than that, using fairly simple front ends [vocalsound] on [disfmarker] [vocalsound] on the uh [disfmarker] [vocalsound] uh, with the SRI system.
[speaker005:] Mm hmm.
[speaker001:] So I th I think that the uh [disfmarker] But that's [disfmarker] that's using uh a [disfmarker] a pretty huge amount of data, mostly not digits, of course, but [disfmarker] but then again [disfmarker] Well, yeah. In fact, mostly not digits for the actual training the H M Ms whereas uh in this case we're just using digits for training the H M
[speaker005:] Yeah. Right.
[speaker001:] Did anybody mention about whether the [disfmarker] the SRI system is a [disfmarker] [vocalsound] is [disfmarker] is doing the digits um the wor as a word model or as uh a sub s sub phone states?
[speaker005:] I guess it's [disfmarker] it's uh allophone models,
[speaker001:] Yeah.
[speaker005:] so, well [disfmarker]
[speaker001:] Probably. Huh?
[speaker005:] Yeah. I think so, because it's their very d huge, their huge system.
[speaker001:] Yeah.
[speaker005:] And. But. So. There is one difference [disfmarker] Well, the SRI system [disfmarker] the result for the SRI system that are represented here are with adaptation. So there is [disfmarker] It's their complete system and [disfmarker] including on line uh unsupervised adaptation.
[speaker001:] That's true.
[speaker005:] And if you don't use adaptation, the error rate is around fifty percent worse, I think, if I remember. Yeah.
[speaker001:] OK. It's tha it's that much, huh?
[speaker005:] Nnn. It's [disfmarker] Yeah. It's quite significant. Yeah.
[speaker001:] Oh. OK. Still.
[speaker005:] Mm hmm.
[speaker001:] But [disfmarker] but uh what [disfmarker] what I think I'd be interested to do given that, is that we [disfmarker] we should uh [vocalsound] take [disfmarker] I guess that somebody's gonna do this, right? [disfmarker] is to take some of these tandem things and feed it into the SRI system, right?
[speaker005:] Yeah.
[speaker001:] Yeah.
[speaker005:] We can do something like that. Yeah.
[speaker001:] Yeah. Because [disfmarker]
[speaker005:] But [disfmarker] But I guess the main point is the data because uh [vocalsound] I am not sure. Our back end is [disfmarker] is fairly simple but until now, well, the attempts to improve it or [disfmarker] have fail Ah, well, I mean uh what Chuck tried to [disfmarker] to [disfmarker] to do
[speaker001:] Yeah, but he's doing it with the same data, right?
[speaker005:] Yeah.
[speaker001:] I mean so to [disfmarker] [vocalsound] So there's [disfmarker] there's [disfmarker] there's two things being affected.
[speaker005:] So it's [disfmarker] Yeah.
[speaker001:] I mean. One is that [disfmarker] that, you know, there's something simple that's wrong with the back end. We've been playing a number of states
[speaker005:] Mm hmm.
[speaker001:] uh I [disfmarker] I don't know if he got to the point of playing with the uh number of Gaussians yet
[speaker005:] Mm hmm.
[speaker001:] but [disfmarker] but uh, uh, you know. But, yeah, so far he hadn't gotten any big improvement,
[speaker005:] Mm hmm.
[speaker001:] but that's all with the same amount of data which is pretty small.
[speaker005:] Yeah. Mmm.
[speaker001:] And um.
[speaker005:] So, yeah, we could retrain some of these tandem on [disfmarker] on huge [disfmarker]
[speaker001:] Well, you could do that, but I'm saying even with it not [disfmarker] with that part not retrained,
[speaker005:] Ah, yeah.
[speaker001:] just [disfmarker] just using [disfmarker] having the H M Ms [disfmarker] much better H M
[speaker005:] Just [disfmarker] f for the HMM models. Yeah.
[speaker001:] Yeah.
[speaker005:] Mm hmm. Mm hmm.
[speaker001:] Um. [vocalsound] But just train those H M Ms using different features, the features coming from our Aurora stuff.
[speaker005:] Yeah.
[speaker001:] So.
[speaker005:] Yeah. But [vocalsound] what would be interesting to see also is what [disfmarker] what [disfmarker] perhaps it's not related, the amount of data but the um recording conditions. I don't know. Because [vocalsound] it's probably not a problem of noise, because our features are supposed to be robust to noise. It's not a problem of channel, because there is um [vocalsound] [vocalsound] normalization with respect to the channel.
[speaker001:] Well, yeah.
[speaker005:] So [disfmarker]
[speaker001:] I [disfmarker] I [disfmarker] I'm sorry. What [disfmarker] what is the problem that you're trying to explain?
[speaker005:] The [disfmarker] the fact that [disfmarker] the result with the tandem and Aurora system are [vocalsound] uh so much worse.
[speaker001:] That the [disfmarker] Oh. So much worse?
[speaker005:] Yeah.
[speaker001:] Oh. I uh but I'm [disfmarker] I'm almost certain that it [disfmarker] it [disfmarker] [vocalsound] I mean, that it has to do with the um amount of training data.
[speaker005:] It [disfmarker]
[speaker001:] It [disfmarker] it's [disfmarker] it's orders of magnitude off.
[speaker005:] Yeah but [disfmarker] Yeah. Yeah but we train only on digits and it's [disfmarker] it's a digit task, so. Well.
[speaker001:] But [disfmarker] but having a huge [disfmarker]
[speaker005:] It [disfmarker]
[speaker001:] If [disfmarker] [vocalsound] if you look at what commercial places do, they use a huge amount of data.
[speaker005:] Mm hmm. Alright.
[speaker001:] This is a modest amount of data.
[speaker005:] Yeah. Mm hmm.
[speaker001:] So. [vocalsound] I mean, ordinarily you would say "well, given that you have enough occurrences of the digits, you can just train with digits rather than with, you know" [disfmarker]
[speaker005:] Mm hmm.
[speaker001:] But the thing is, if you have a huge [disfmarker] in other words, do word models [disfmarker] But if you have a huge amount of data then you're going to have many occurrences of similar uh allophones.
[speaker005:] Right. Mmm. Yeah.
[speaker001:] And that's just a huge amount of training for it. So it's [vocalsound] um [disfmarker] [vocalsound] I [disfmarker] I think it has to be that, because, as you say, this is, you know, this is near microphone,
[speaker005:] Mm hmm.
[speaker001:] it's really pretty clean data.
[speaker005:] Mm hmm.
[speaker001:] Um. Now, some of it could be the fact that uh [disfmarker] let's see, in the [disfmarker] in these multi train things did we include noisy data in the training?
[speaker005:] Yeah.
[speaker001:] I mean, that could be hurting us actually, for the clean case.
[speaker005:] Yeah. Well, actually we see that the clean train for the Aurora proposals are [disfmarker] are better than the multi train,
[speaker001:] It is if [disfmarker]
[speaker005:] yeah.
[speaker001:] Yeah. Yeah. Cuz this is clean data, and so that's not too surprising.
[speaker005:] Mm hmm.
[speaker001:] But um. Uh. So.
[speaker005:] Well, o I guess what I meant is that well, let's say if we [disfmarker] if we add enough data to train on the um on the Meeting Recorder digits, I guess we could have better results than this.
[speaker001:] Uh huh. Mm hmm.
[speaker005:] And. What I meant is that perhaps we can learn something uh from this, what's [disfmarker] what's wrong uh what [disfmarker] what is different between TI digits and these digits and [disfmarker]
[speaker001:] What kind of numbers are we getting on TI digits?
[speaker005:] It's point eight percent, so.
[speaker001:] Oh. I see.
[speaker005:] Four Fourier.
[speaker001:] So in the actual TI digits database we're getting point eight percent,
[speaker005:] Yeah. Yeah.
[speaker001:] and here we're getting three or four [disfmarker] three,
[speaker005:] Mm hmm.
[speaker001:] let's see, three for this? Yeah. Sure, but I mean, um point eight percent is something like double uh or triple what people have gotten who've worked very hard at doing that.
[speaker005:] Mm hmm.
[speaker001:] And [disfmarker] and also, as you point out, there's adaptation in these numbers also.
[speaker005:] Mmm.
[speaker001:] So if you, you know, put the ad adap take the adaptation off, then it [disfmarker] for the English Near you get something like two percent. And here you had, you know, something like three point four.
[speaker005:] Mm hmm.
[speaker001:] And I could easily see that difference coming from this huge amount of data that it was trained on.
[speaker005:] Mm hmm.
[speaker001:] So it's [disfmarker] You know, I don't think there's anything magical here.
[speaker005:] Yeah.
[speaker001:] It's, you know, we used a simple HTK system with a modest amount of data. And this is a [disfmarker] a, you know, modern [vocalsound] uh system
[speaker005:] Yeah.
[speaker001:] uh has [disfmarker] has a lot of nice points to it.
[speaker005:] Mm hmm.
[speaker001:] Um. So. I mean, the HTK is an older HTK, even. So.
[speaker005:] Mm hmm.
[speaker001:] Yeah it [disfmarker] it's not that surprising. But to me it just [disfmarker] it just meant a practical [vocalsound] point that um if we want to [vocalsound] publish results on digits that [disfmarker] that people pay [vocalsound] attention to we probably should uh [disfmarker] Cuz we've had the problem before that you get [disfmarker] show some [vocalsound] nice improvement on something that's [disfmarker] that's uh, uh [disfmarker] it seems like too large a number,
[speaker005:] Mm hmm.
[speaker001:] and uh [vocalsound] uh people don't necessarily take it so seriously. Um. Yeah. Yeah. So the three point four percent for this uh is [disfmarker] is uh [disfmarker] So why is it [disfmarker] It's an interesting question though, still. Why is [disfmarker] why is it three point four percent for the d the digits recorded in this environment as opposed to [vocalsound] the uh point eight percent for [disfmarker] for [disfmarker] for the original TI digits database? Um.
[speaker005:] Yeah. th that's [disfmarker] th that's my point
[speaker001:] Given [disfmarker] given the same [disfmarker]
[speaker005:] I [disfmarker] I [disfmarker] I don't I [disfmarker]
[speaker001:] Yeah. So ignore [disfmarker] ignoring the [disfmarker] the [disfmarker] the SRI system for a moment,
[speaker005:] Mm hmm.
[speaker001:] just looking at [vocalsound] the TI di the uh tandem system, if we're getting point eight percent, which, yes, it's high. It's, you know, it [disfmarker] it's not awfully high,
[speaker005:] Mm hmm.
[speaker001:] but it's, you know [disfmarker] it's [disfmarker] it's high. Um. [vocalsound] Why is it [vocalsound] uh four times as high, or more?
[speaker005:] Yeah, I guess.
[speaker001:] Right? I mean, there's [disfmarker] [vocalsound] even though it's close miked there's still [disfmarker] there really is background noise.
[speaker005:] Mm hmm.
[speaker001:] Um. And [vocalsound] uh I suspect when the TI digits were recorded if somebody fumbled or said something wrong or something that they probably made them take it over.
[speaker005:] Mm hmm.
[speaker001:] It was not [disfmarker] I mean there was no attempt to have it be realistic in any [disfmarker] in any sense at all.
[speaker005:] Well. Yeah. And acoustically, it's q it's [disfmarker] I listened. It's quite different. TI digit is [disfmarker] it's very, very clean and it's like studio recording
[speaker001:] Mm hmm.
[speaker005:] whereas these Meeting Recorder digits sometimes you have breath noise and Mmm.
[speaker001:] Right. Yeah. So I think they were [disfmarker]
[speaker005:] It's [nonvocalsound] not controlled at all, I mean.
[speaker001:] Bless you.
[speaker002:] Thanks.
[speaker001:] I [disfmarker] Yeah. I think it's [disfmarker] it's [disfmarker]
[speaker005:] Mm hmm.
[speaker001:] So. Yes.
[speaker005:] But
[speaker001:] It's [disfmarker] I think it's [disfmarker] it's the indication it's harder.
[speaker005:] Yeah.
[speaker001:] Uh. [vocalsound] Yeah and again, you know, i that's true either way. I mean so take a look at the uh [disfmarker] [vocalsound] um, the SRI results. I mean, they're much much better, but still you're getting something like one point three percent for uh things that are same data as in T [disfmarker] TI digits the same [disfmarker] same text.
[speaker005:] Mm hmm.
[speaker001:] Uh. And uh, I'm sure the same [disfmarker] same system would [disfmarker] would get, you know, point [disfmarker] point three or point four or something [vocalsound] on the actual TI digits. So this [disfmarker] I think, on both systems the [vocalsound] these digits are showing up as harder.
[speaker005:] Mmm.
[speaker001:] Um.
[speaker005:] Mm hmm.
[speaker001:] Which I find sort of interesting cause I think this is closer to [disfmarker] uh I mean it's still read. But I still think it's much closer to [disfmarker] to what [disfmarker] what people actually face, [vocalsound] um when they're [disfmarker] they're dealing with people saying digits over the telephone. I mean. [vocalsound] I don't think uh [disfmarker] I mean, I'm sure they wouldn't release the numbers, but I don't think that uh [vocalsound] the uh [disfmarker] the [disfmarker] the companies that [disfmarker] that do telephone [vocalsound] speech get anything like point four percent on their [vocalsound] digits. I'm [disfmarker] I'm [disfmarker] I'm sure they get [disfmarker] Uh, I mean, for one thing people do phone up who don't have uh uh Middle America accents and
[speaker005:] Mm hmm.
[speaker001:] it's a we we it's [disfmarker] it's [disfmarker] it's US. it has [disfmarker] has many people [vocalsound] [vocalsound] who sound in many different ways. So. Um. I mean. OK. That was that topic. What else we got?
[speaker005:] Um.
[speaker001:] Did we end up giving up on [disfmarker] on, any Eurospeech submissions,
[speaker005:] But [disfmarker]
[speaker001:] or [disfmarker]? I know Thilo and Dan Ellis are [disfmarker] are submitting something, but uh.
[speaker005:] Yeah. I [disfmarker] [vocalsound] I guess e the only thing with these [disfmarker] the Meeting Recorder and, well, [disfmarker] So, I think, yeah [disfmarker] I think we basically gave up.
[speaker001:] Um. [vocalsound] Now, actually for the [disfmarker] for the Aur uh
[speaker005:] But [disfmarker]
[speaker001:] we do have stuff for Aurora, right? Because [disfmarker] because we have ano an extra month or something.
[speaker005:] Yeah. Yeah. Yeah. So. Yeah, for sure we will do something for the special session.
[speaker001:] Yeah. Well, that's fine. So th so [disfmarker] so we have a couple [disfmarker] a couple little things on Meeting Recorder
[speaker005:] Yeah. Mm hmm.
[speaker001:] and we have [disfmarker] [vocalsound] We don't [disfmarker] we don't have to flood it with papers. We're not trying to prove anything to anybody. so. That's fine. Um. Anything else?
[speaker005:] Yeah. Well. So. Perhaps the point is that we've been working on [vocalsound] is, yeah, we have put the um the good VAD in the system and [vocalsound] it really makes a huge difference. Um. So, yeah. I think, yeah, this is perhaps one of the reason why our system was not [disfmarker] [vocalsound] not the best, because with the new VAD, it's very [disfmarker] the results are similar to the France Telecom results and perhaps even better sometimes.
[speaker001:] Hmm.
[speaker002:] Huh.
[speaker005:] Um. So there is this point. Uh. The problem is that it's very big and [vocalsound] [vocalsound] we still have to think how to [disfmarker] where to put it and [disfmarker] [vocalsound] um,
[speaker001:] Mm hmm.
[speaker005:] because it [disfmarker] it [disfmarker] well, this VAD uh either some delay and we [disfmarker] if we put it on the server side, it doesn't work, because on the server side features you already have LDA applied [vocalsound] from the f from the terminal side and [vocalsound] so you accumulate the delay so the VAD should be before the LDA which means perhaps on the terminal side and then smaller [vocalsound] and
[speaker001:] So wha where did this good VAD come from?
[speaker005:] So. It's um from OGI. So it's the network trained [disfmarker] it's the network with the huge amounts on hidden [disfmarker] of hidden units, and um nine input frames compared to the VAD that was in the proposal which has a very small amount of hidden units and fewer inputs.
[speaker001:] This is the one they had originally?
[speaker005:] Yeah.
[speaker001:] Oh. Yeah, but they had to [pause] get rid of it because of the space, didn't they?
[speaker005:] Yeah. So. Yeah. But the abso assumption is that we will be able to make a VAD that's small and that works fine. And. So we can [disfmarker]
[speaker001:] Well. So that's a problem. Yeah.
[speaker005:] Yeah but [disfmarker] nnn.
[speaker001:] But the other thing is uh to use a different VAD entirely. I mean, uh i if [disfmarker] if there's a [vocalsound] if [disfmarker] if [disfmarker] I [disfmarker] I don't know what the thinking was amongst the [disfmarker] the [disfmarker] the [vocalsound] the ETSI folk but um if everybody agreed sure let's use this VAD and take that out of there [disfmarker]
[speaker005:] Mm hmm. Mm hmm. They just want, apparently [disfmarker] they don't want to fix the VAD because they think there is some interaction between feature extraction and [disfmarker] and VAD or frame dropping But they still [vocalsound] want to [disfmarker] just to give some um [vocalsound] requirement for this VAD because it's [disfmarker] it will not be part of [disfmarker] they don't want it to be part of the standard.
[speaker001:] OK.
[speaker005:] So. So it must be at least uh somewhat fixed but not completely. So there just will be some requirements that are still not [disfmarker] uh not yet uh ready I think.
[speaker001:] Determined. I see.
[speaker005:] Nnn.
[speaker001:] But I was thinking that [disfmarker] that uh [vocalsound] s Sure, there may be some interaction, but I don't think we need to be stuck on using our or OGI's [pause] VAD. We could use somebody else's if it's smaller or [disfmarker]
[speaker005:] Yeah.
[speaker001:] You know, as long as it did the job.
[speaker005:] Mm hmm.
[speaker001:] So that's good.
[speaker005:] Uh. So there is this thing. There is um [disfmarker] Yeah. Uh I designed a new [disfmarker] a new filter because when I designed other filters with shorter delay from the LDA filters, [vocalsound] there was one filter with fif sixty millisecond delay and the other with ten milliseconds and [vocalsound] uh Hynek suggested that both could have sixty five sixty s
[speaker001:] Right.
[speaker005:] I think it's sixty five. Yeah.
[speaker001:] Yeah.
[speaker005:] Both should have sixty five because [disfmarker] Yeah.
[speaker001:] You didn't gain anything, right?
[speaker005:] And. So I did that and uh it's running. So, [vocalsound] let's see what will happen. Uh but the filter is of course closer to the reference filter.
[speaker001:] Mm hmm.
[speaker005:] Mmm. Um. Yeah. I think [disfmarker]
[speaker001:] So that means logically, in principle, it should be better. So probably it'll be worse.
[speaker005:] Yeah
[speaker001:] Or in the basic perverse nature uh of reality. Yeah. OK.
[speaker005:] Yeah. Sure.
[speaker003:] Yeah.
[speaker001:] OK.
[speaker005:] Yeah, and then we've started to work with this of um voiced unvoiced stuff.
[speaker001:] Mm hmm.
[speaker005:] And next week I think we will [vocalsound] perhaps try to have um a new system with uh uh MSG stream also see what [disfmarker] what happens. So, something that's similar to the proposal too, but with MSG stream.
[speaker001:] Mm hmm. Mm hmm.
[speaker005:] Mmm.
[speaker001:] OK.
[speaker004:] No, I w [vocalsound] I begin to play [vocalsound] with Matlab and to found some parameter robust for voiced unvoiced decision. But only to play. And we [disfmarker] [vocalsound] they [disfmarker] we found that maybe w is a classical parameter, the [vocalsound] sq the variance [vocalsound] between the um FFT of the signal and the small spectrum of time [vocalsound] we [disfmarker] after the um mel filter bank.
[speaker001:] Uh huh.
[speaker004:] And, well, is more or less robust. Is good for clean speech. Is quite good [vocalsound] for noisy speech.
[speaker001:] Huh? Mm hmm.
[speaker004:] but um we must to have bigger statistic with TIMIT,
[speaker001:] Mm hmm.
[speaker004:] and is not ready yet to use on,
[speaker001:] Yeah.
[speaker004:] well, I don't know.
[speaker001:] Yeah.
[speaker005:] Yeah. So, basically we wa want to look at something like the ex the ex excitation signal and [disfmarker]
[speaker001:] Right.
[speaker004:] Mm hmm.
[speaker005:] which are the variance of it and [disfmarker]
[speaker004:] I have here. I have here for one signal, for one frame.
[speaker005:] Mmm.
[speaker001:] Yeah.
[speaker004:] The [disfmarker] the mix of the two, noise and unnoise, and the signal is this.
[speaker001:] Uh huh.
[speaker004:] Clean, and this noise.
[speaker001:] Uh.
[speaker004:] These are the two [disfmarker] the mixed, the big signal is for clean.
[speaker001:] Well, I'm s uh [disfmarker] There's [disfmarker] None of these axes are labeled, so I don't know what this [disfmarker] What's this axis?
[speaker004:] Uh this is uh [disfmarker] this axis is [vocalsound] nnn, "frame".
[speaker001:] Frame.
[speaker004:] Mm hmm.
[speaker001:] And what's th what this?
[speaker004:] Uh, this is uh energy, log energy of the spectrum. Of the this is the variance, the difference [nonvocalsound] between the spectrum of the signal and FFT of each frame of the signal and this mouth spectrum of time after the f may fit for the two,
[speaker001:] For this one. For the noi
[speaker004:] this big, to here, they are to signal. This is for clean and this is for noise.
[speaker001:] Oh. There's two things on the same graph.
[speaker004:] Yeah. I don't know. I [disfmarker] I think that I have d another graph, but I'm not sure.
[speaker005:] Yeah.
[speaker001:] So w which is clean and which is noise?
[speaker005:] I think the lower one is noise.
[speaker004:] The lower is noise and the height is clean.
[speaker001:] OK. So it's harder to distinguish
[speaker004:] It's height.
[speaker001:] but it [disfmarker] but it g
[speaker005:] Yeah.
[speaker001:] with noise of course but [disfmarker] but [disfmarker]
[speaker004:] Oh. I must to have.
[speaker001:] Uh.
[speaker004:] Pity, but I don't have two different
[speaker001:] And presumably when there's a [disfmarker] a [disfmarker]
[speaker005:] So this should the [disfmarker] the [disfmarker] the t voiced portions.
[speaker001:] Uh huh.
[speaker004:] Yeah, it is the height is voiced portion.
[speaker005:] The p the peaks should be voiced portion.
[speaker004:] And this is the noise portion.
[speaker001:] Uh huh.
[speaker004:] And this is more or less like this. But I meant to have see [@ @] two [disfmarker] two the picture.
[speaker001:] Yeah. Yeah.
[speaker004:] This is, for example, for one frame.
[speaker001:] Yeah
[speaker004:] the [disfmarker] the spectrum of the signal. And this is the small version of the spectrum after ML mel filter bank.
[speaker001:] Yeah. And this is the difference?
[speaker004:] And this is I don't know. This is not the different. This is trying to obtain [vocalsound] with LPC model the spectrum but using Matlab without going factor and s
[speaker001:] No pre emphasis? Yeah.
[speaker004:] Not pre emphasis. Nothing.
[speaker001:] Yeah so it's [disfmarker]
[speaker004:] And the [disfmarker]
[speaker001:] doesn't do too well there.
[speaker004:] I think that this is good. This is quite similar. this is [disfmarker] [vocalsound] this is another frame. ho how I obtained the [vocalsound] envelope, [nonvocalsound] this envelope, with the mel filter bank.
[speaker001:] Right. So now I wonder [disfmarker] I mean, do you want to [disfmarker] I know you want to get at something orthogonal from what you get with the smooth spectrum Um. But if you were to really try and get a voiced unvoiced, do you [disfmarker] do you want to totally ignore that? I mean, do you [disfmarker] do you [disfmarker] I mean, clearly a [disfmarker] a very big [disfmarker] very big cues [vocalsound] for voiced unvoiced come from uh spectral slope and so on,
[speaker005:] Mm hmm.
[speaker001:] right? Um.
[speaker005:] Yeah. Well, this would be [disfmarker] this would be perhaps an additional parameter, simply isn't [disfmarker]
[speaker001:] Yeah. I see.
[speaker005:] Yeah.
[speaker004:] Yeah because when did noise clear [nonvocalsound] in these section is clear
[speaker005:] Uh.
[speaker001:] Mm hmm.
[speaker004:] if s [@ @] [nonvocalsound] val value is indicative that is a voice frame and it's low values
[speaker001:] Yeah. Yeah. Well, you probably want [disfmarker] I mean, [vocalsound] certainly if [vocalsound] you want to do good voiced unvoiced detection, you need a few features. Each [disfmarker] each feature is [vocalsound] by itself not enough. But, you know, people look at [disfmarker] at slope and [vocalsound] uh first auto correlation coefficient, divided by power.
[speaker005:] Mmm.
[speaker001:] Or [disfmarker] or uh um there's uh [disfmarker] I guess we prob probably don't have enough computation to do a simple pitch detector or something? I mean with a pitch detector you could have a [disfmarker] [vocalsound] have a [disfmarker] an estimate of [disfmarker] of what the [disfmarker]
[speaker005:] Mmm.
[speaker001:] Uh. Or maybe you could you just do it going through the P FFT's figuring out some um probable [vocalsound] um harmonic structure. Right.
[speaker005:] Mmm.
[speaker001:] And [disfmarker] and uh.
[speaker004:] you have read up and [disfmarker] you have a paper, [vocalsound] the paper that you s give me yesterday.
[speaker005:] Oh, yeah.
[speaker004:] they say that yesterday [vocalsound] they are some [nonvocalsound] problem
[speaker005:] But [disfmarker] Yeah, but it's not [disfmarker] it's, yeah, it's [disfmarker] it's another problem.
[speaker004:] and the [disfmarker]
[speaker005:] Yeah
[speaker004:] Is another problem.
[speaker005:] Um. Yeah, there is th this fact actually. If you look at this um spectrum,
[speaker001:] Yeah.
[speaker005:] What's this again? Is it [vocalsound] the mel filters?
[speaker004:] Yeah like this.
[speaker005:] Yeah.
[speaker004:] Of kind like this.
[speaker005:] OK. So the envelope here is the output of the mel filters
[speaker001:] Mm hmm.
[speaker005:] and what we clearly see is that in some cases, and it clearly appears here, and the [disfmarker] the harmonics are resolved by the f Well, there are still appear after mel filtering,
[speaker001:] Mm hmm.
[speaker005:] and it happens [vocalsound] for high pitched voice because the width of the lower frequency mel filters [vocalsound] is sometimes even smaller than the pitch.
[speaker001:] Yeah.
[speaker005:] It's around one hundred, one hundred and fifty hertz [vocalsound] Nnn.
[speaker001:] Right.
[speaker005:] And so what happens is that this uh, add additional variability to this envelope and [vocalsound] [vocalsound] um
[speaker001:] Yeah.
[speaker005:] so we were thinking to modify the mel spectrum to have something that [disfmarker] that's smoother on low frequencies.
[speaker001:] That's as [disfmarker] as a separate thing.
[speaker005:] i
[speaker001:] Yeah.
[speaker005:] Yeah. This is a separate thing.
[speaker004:] Yeah.
[speaker001:] Separate thing? Yeah.
[speaker005:] And.
[speaker001:] Yeah. Maybe so. Um. Yeah. So, what [disfmarker] Yeah. What I was talking about was just, starting with the FFT you could [disfmarker] you could uh do a very rough thing to estimate [disfmarker] estimate uh pitch.
[speaker005:] Yeah. Mm hmm.
[speaker001:] And uh uh, given [disfmarker] you know, given that, uh [vocalsound] you could uh uh come up with some kind of estimate of how much of the low frequency energy was [disfmarker] was explained by [disfmarker] [vocalsound] by uh uh those harmonics.
[speaker005:] Mm hmm.
[speaker001:] Uh. It's uh a variant on what you're s what you're doing. The [disfmarker] I mean, the [disfmarker] the [vocalsound] the mel does give a smooth thing. But as you say it's not that smooth here. And [disfmarker] and so if you [disfmarker] [vocalsound] if you just you know subtracted off uh your guess of the harmonics then something like this would end up with [vocalsound] quite a bit lower energy in the first fifteen hundred hertz or so and [disfmarker] and our first kilohertz, even.
[speaker005:] Mm hmm.
[speaker001:] And um [vocalsound] if was uh noisy, the proportion that it would go down would be
[speaker005:] Mm hmm.
[speaker001:] if it was [disfmarker] if it was unvoiced or something. So you oughta be able to [vocalsound] pick out voiced segments. At least it should be another [disfmarker] another cue.
[speaker005:] Mm hmm.
[speaker001:] So. [vocalsound] Anyway. OK? That's what's going on. Uh. What's up with you?
[speaker002:] Um [vocalsound] our t I went to [vocalsound] talk with uh Mike Jordan this [disfmarker] this week
[speaker001:] Mm hmm.
[speaker002:] um [nonvocalsound] and uh [vocalsound] shared with him the ideas about um [vocalsound] extending the Larry Saul work and um I asked him some questions about factorial H M so like later down the line when [vocalsound] we've come up with these [disfmarker] these feature detectors, how do we [disfmarker] [vocalsound] how do we uh [vocalsound] you know, uh model the time series that [disfmarker] that happens um [vocalsound] [vocalsound] and [vocalsound] and we talked a little bit about [vocalsound] factorial H M Ms and how [vocalsound] um when you're doing inference [disfmarker] or w when you're doing recognition, there's like simple Viterbi stuff that you can do for [disfmarker] [vocalsound] for these H M and [vocalsound] the uh [disfmarker] [vocalsound] the great advantages that um a lot of times the factorial H M Ms don't [vocalsound] um [vocalsound] don't over alert the problem there they have a limited number of parameters and they focus directly on [disfmarker] [vocalsound] on uh the sub problems at hand so [vocalsound] you can imagine [vocalsound] um [vocalsound] five or so parallel [vocalsound] um features um transitioning independently and then [vocalsound] at the end you [disfmarker] you uh couple these factorial H M Ms with uh [disfmarker] [vocalsound] with uh undirected links um based on [disfmarker] [vocalsound] based on some more data.
[speaker001:] Hmm.
[speaker002:] So he [disfmarker] he seemed [disfmarker] he seemed like really interested in [disfmarker] [vocalsound] in um [disfmarker] in this and said [disfmarker] said this is [disfmarker] this is something very do able and can learn a lot and um yeah, I've just been [vocalsound] continue reading um about certain things.
[speaker001:] Mm hmm.
[speaker002:] um thinking of maybe using um [vocalsound] um m modulation spectrum stuff to [vocalsound] um [disfmarker] as features um also in the [disfmarker] in the sub bands
[speaker001:] Mm hmm.
[speaker002:] because [vocalsound] it seems like [vocalsound] the modulation um spectrum tells you a lot about the intelligibility of [disfmarker] of certain um words and stuff So, um. Yeah. Just that's about it.
[speaker001:] OK.
[speaker003:] OK. And um so I've been looking at Avendano's work and um uh I'll try to write up in my next stat status report a nice description of [vocalsound] what he's doing, but it's [disfmarker] it's an approach to deal with [vocalsound] reverberation or that [disfmarker] the aspect of his work that I'm interested in the idea is that um [vocalsound] [vocalsound] [vocalsound] normally an analysis frames are um [vocalsound] too short to encompass reverberation effects um in full. You miss most of the reverberation tail in a ten millisecond window and so [vocalsound] [vocalsound] you [disfmarker] you'd like it to be that [vocalsound] um [vocalsound] the reverberation responses um simply convolved um in, but it's not really with these ten millisecond frames cuz you j But if you take, say, a two millisecond [vocalsound] um window [disfmarker] I'm sorry a two second window then in a room like this, most of the reverberation response [vocalsound] is included in the window and the [disfmarker] then it um [vocalsound] then things are l more linear. It is [disfmarker] it is more like the reverberation response is simply c convolved and um [disfmarker] [vocalsound] and you can use channel normalization techniques [vocalsound] like uh in his thesis he's assuming that the reverberation response is fixed. He just does um [vocalsound] mean subtraction, which is like removing the DC component of the modulation spectrum and [vocalsound] that's supposed to d um deal [disfmarker] uh deal pretty well with the um reverberation and um [vocalsound] the neat thing is you can't take these two second frames and feed them to a speech recognizer um [vocalsound] so he does this [vocalsound] um [vocalsound] method training trading the um [vocalsound] the spectral resolution for time resolution [vocalsound] and um [vocalsound] come ca uh synthesizes a new representation which is with say ten second frames but a lower s um [vocalsound] frequency resolution. So I don't really know the theory. I guess it's [disfmarker] these are called "time frequency representations" and h he's making the [disfmarker] the time sh um finer grained and the frequency resolution um less fine grained.
[speaker005:] Mm hmm.
[speaker003:] s so I'm [disfmarker] I guess my first stab actually in continuing [vocalsound] his work is to um [vocalsound] re implement this [disfmarker] this thing which um [vocalsound] changes the time and frequency resolutions cuz he doesn't have code for me. So that that'll take some reading about the theory. I don't really know the theory.
[speaker005:] Mm hmm.
[speaker003:] Oh, and um, [vocalsound] another f first step is um, so the [disfmarker] the way I want to extend his work is make it able to deal with a time varying reverberation response um [vocalsound] and um we don't really know [vocalsound] how fast the um [disfmarker] the reverberation response is varying the Meeting Recorder data um so um [vocalsound] we [disfmarker] we have this um block least squares um imp echo canceller implementation and um [vocalsound] I want to try [vocalsound] finding [vocalsound] the [disfmarker] the response, say, between a near mike and the table mike for someone using the echo canceller and looking at the echo canceller taps and then [vocalsound] see how fast that varies [vocalsound] from block to block.
[speaker005:] Mm hmm.
[speaker003:] That should give an idea of how fast the reverberation response is changing.
[speaker005:] Mm hmm.
[speaker001:] OK. Um. I think we're [vocalsound] sort of done.
[speaker005:] Yeah.
[speaker001:] So let's read our digits and go home.
[speaker003:] Um. S so um y you do [disfmarker] I think you read some of the [disfmarker] the zeros as O's and some as zeros.
[speaker001:] Yeah.
[speaker003:] Is there a particular way we're supposed to read them?
[speaker005:] There are only zeros here. Well.
[speaker001:] No. "O" [disfmarker] "O" [disfmarker] "O" "O" [disfmarker] "O" [disfmarker] "O" and "zero" are two ways that we say that digit.
[speaker005:] Eee. Yeah.
[speaker001:] So it's [disfmarker]
[speaker005:] But [disfmarker]
[speaker002:] Ha!
[speaker001:] so it's [disfmarker] i
[speaker005:] Perhaps in the sheets there should be another sign for the [disfmarker] if we want to [disfmarker] the [disfmarker] the guy to say "O" or
[speaker001:] No. I mean. I think people will do what they say.
[speaker005:] It's [disfmarker] Yeah.
[speaker001:] It's OK.
[speaker005:] OK.
[speaker003:] Alright.
[speaker001:] I mean in digit recognition we've done before, you have [disfmarker] you have two pronunciations for that value, "O" and "zero".
[speaker003:] OK.
[speaker005:] But it's perhaps more difficult for the people to prepare the database then, if [disfmarker] because here you only have zeros
[speaker001:] No, they just write [disfmarker]
[speaker005:] and [disfmarker] and people pronounce "O" or zero [disfmarker]
[speaker001:] they [disfmarker] they write down OH. or they write down ZERO a and they [disfmarker] and they each have their own pronunciation.
[speaker005:] Yeah but if the sh the sheet was prepared with a different sign for the "O".
[speaker001:] But people wouldn't know what that wa I mean [vocalsound] there is no convention for it.
[speaker005:] OK. Yeah. OK.
[speaker001:] See. I mean, you'd have to tell them [vocalsound] "OK when we write this, say it tha", you know, and you just [disfmarker] They just want people to read the digits as you ordinarily would
[speaker005:] Mm hmm. Yeah. Yep.
[speaker001:] and [disfmarker] and people say it different ways.
[speaker003:] OK. Is this a change from the last batch of [disfmarker] of um forms? Because in the last batch it was spelled out which one you should read.
[speaker005:] Yeah, it was orthographic, so.
[speaker001:] Yes. That's right. It was [disfmarker] it was spelled out, and they decided they wanted to get at more the way people would really say things.
[speaker003:] Oh. OK.
[speaker001:] That's also why they're [disfmarker] they're bunched together in these different groups.
[speaker003:] OK.
[speaker001:] So [disfmarker] so it's [disfmarker] Yeah. So it's [disfmarker] it's [disfmarker] Everything's fine.
[speaker003:] OK.
[speaker001:] OK. Actually, let me just s since [disfmarker] since you brought it up, I was just [disfmarker] it was hard not to be self conscious about that when it [vocalsound] after we [disfmarker] since we just discussed it. But I realized that [disfmarker] that um [vocalsound] when I'm talking on the phone, certainly, and [disfmarker] and saying these numbers, [vocalsound] I almost always say zero. And uh [disfmarker] cuz [disfmarker] because uh i it's two syllables. It's [disfmarker] it's more likely they'll understand what I said. So that [disfmarker] that [disfmarker] that's the habit I'm in, but some people say "O" and [disfmarker]
[speaker002:] Yeah I normally say "O" cuz it's easier to say.
[speaker001:] Yeah it's shorter. Yeah. So it's [disfmarker] So. [vocalsound] So uh.
[speaker002:] "O"
[speaker001:] Now, don't think about it.
[speaker002:] Oh, no!
[speaker001:] OK. We're done.