I think for two years we were two months, uh, away from being done.

And what was that, Morgan? What project?

Uh, the, uh, TORRENT chip.

Oh.

Yeah. We were two we were.

Yeah.

Uh, uh, we went through it Jim and I went through old emails at one point and and for two years there was this thing saying, yeah, we're we're two months away from being done. It was very very believable schedules, too. I mean, we went through and with the schedules and we.

It was true for two years.

Yeah. Oh, yeah. It was very true.

So, should we just do the same kind of deal where we go around and do, uh, status report kind of things? OK. And I guess when Sunil gets here he can do his last or something. So.

Yeah. So we probably should wait for him to come before we do his.

Mm - hmm.

OK. That's a good idea.

Yeah.

OK.

Yeah.

Any objection? Do y OK, M

All in favor

Do you want to start, Morgan? Do you have anything, or?

Uh, I don't do anything. I No, I mean, I I'm involved in discussions with with people about what they're doing, but I think they're since they're here, they can talk about it themselves.

OK. So should I go so that, uh,

Yeah. Why don't you go ahead, Barry?

you're gonna talk about Aurora stuff, per se?

OK.

OK. Um. Well, this past week I've just been, uh, getting down and dirty into writing my my proposal. So, um Mmm. I just finished a section on, uh on talking about these intermediate categories that I want to classify, um, as a as a middle step. And, um, I hope to hope to get this, um a full rough draft done by, uh, Monday so I can give it to Morgan.

When is your, uh, meeting?

Um, my meeting

Yeah.

with, uh? Oh, oh, you mean the the quals.

The quals. Yeah.

Uh, the quals are happening in July twenty - fifth.

Oh. Soon.

Yeah.

Uh - huh.

D - Day.

Yeah.

Uh - huh.

So, is the idea you're going to do this paper and then you pass it out to everybody ahead of time and?

Right, right. So, y you write up a proposal, and give it to people ahead of time, and you have a short presentation. And, um, and then, um then everybody asks you questions.

Hmm.

Yeah.

I remember now.

Yep. So, um.

Have you d? I was just gonna ask, do you want to say any a little bit about it,

Y s

or? Mmm.

Oh. Uh, a little bit about?

Wh - what you're what you're gonna You said you were talking about the, uh, particular features that you were looking at,

Oh, the the.

or.

Right. Well, I was, um, I think one of the perplexing problems is, um, for a while I was thinking that I had to come up with a complete set of intermediate features in intermediate categories to to classify right away. But what I'm thinking now is, I would start with with a reasonable set. Something something like, um, um like, uh, re regular phonetic features, just to just to start off that way. And do some phone recognition. Um, build a system that, uh, classifies these, um these feat uh, these intermediate categories using, uh, multi - band techniques. Combine them and do phon phoneme recognition. Look at then I would look at the errors produced in the phoneme recognition and say, OK, well, I could probably reduce the errors if I included this extra feature or this extra intermediate category. That would that would reduce certain confusions over other confusions. And then and then reiterate. Um, build the intermediate classifiers. Uh, do phoneme recognition. Look at the errors. And then postulate new or remove, um, intermediate categories. And then do it again.

So you're gonna use TIMIT?

Um, for that for that part of the the process, yeah, I would use TIMIT.

Mm - hmm.

And, um, then after after, uh, um, doing TIMIT. Right?

Mm - hmm.

Um, that's that's, um that's just the ph the phone recognition task.

Yeah.

Uh, I wanted to take a look at, um, things that I could model within word. So, I would mov I would then shift the focus to, um, something like Schw - Switchboard, uh, where I'd I would be able to, um to model, um, intermediate categories that span across phonemes,

Mm - hmm.

not just within the phonemes, themselves, um, and then do the same process there, um, on on a large vocabulary task like Switchboard. Uh, and for that for that part I would I'd use the SRI recognizer since it's already set up for for Switchboard. And I'd run some some sort of tandem - style processing with, uh, my intermediate classifiers.

Oh. So that's why you were interested in getting your own features into the SRI files.

Yeah. That's why I I was asking about that.

Yeah. Yeah.

Yeah. Um, and I guess that's that's it. Any any questions?

Sounds good. So you just have a few more weeks, huh?

Um, yeah. A few more.

It's about a month from now?

It's a it's a month and and a week.

Yeah.

Yeah.

So, uh, you want to go next, Dave? And we'll do.

Oh. OK, sure. So, um, last week I finally got results from the SRI system about this mean subtraction approach. And, um, we we got an improvement, uh, in word error rate, training on the TI - digits data set and testing on Meeting Recorder digits of, um, six percent to four point five percent, um, on the n on the far - mike data using PZM F, but, um, the near - mike performance worsened, um, from one point two percent to two point four percent. And, um, wh why would that be, um, considering that we actually got an improvement in near - mike performance using HTK? And so, uh, with some input from, uh, Andreas, I have a theory in two parts. Um, first of all HTK sorry, SR - the SRI system is doing channel adaptation, and so HTK wasn't. Um, so this, um This mean subtraction approach will do a kind of channel normalization and so that might have given the HTK use of it a boost that wouldn't have been applied in the SRI case. And also, um, the Andreas pointed out the SRI system is using more parameters. It's got finer - grained acoustic models. So those finer - grained acoustic models could be more sensitive to the artifacts in the re - synthesized audio. Um. And me and Barry were listening to the re - synthesized audio and sometimes it seems like you get of a bit of an echo of speech in the background. And so that seems like it could be difficult for training, cuz you could have different phones lined up with a different foreground phone, um, depending on the timing of the echo. So, um, I'm gonna try training on a larger data set, and then, eh, the system will have seen more examples o of these artifacts and hopefully will be more robust to them. So I'm planning to use the Macrophone set of, um, read speech, and, um Hmm.

I had another thought just now, which is, uh, remember we were talking before about we were talking in our meeting about, uh, this stuff that some of the other stuff that Avendano did, where they were, um, getting rid of low - energy sections? Um, uh, if you if you did a high - pass filtering, as Hirsch did in late eighties to reduce some of the effects of reverberation, uh, uh, Avendano and Hermansky were arguing that, uh, perhaps one of the reasons for that working was ma may not have even been the filtering so much but the fact that when you filter a an all - positive power spectrum you get some negative values, and you gotta figure out what to do with them if you're gonna continue treating this as a power spectrum. So, what what Hirsch did was, uh, set them to zero set the negative values to zero. So if you imagine a a waveform that's all positive, which is the time trajectory of energy, um, and, uh, shifting it downwards, and then getting rid of the negative parts, that's essentially throwing away the low - energy things. And it's the low - energy parts of the speech where the reverberation is most audible. You know, you have the reverberation from higher - energy things showing up in So in this case you have some artificially imposed reverberation - like thing. I mean, you're getting rid of some of the other effects of reverberation, but because you have these non - causal windows, you're getting these funny things coming in, uh, at n And, um, what if you did? I mean, there's nothing to say that the the processing for this re - synthesis has to be restricted to trying to get it back to the original, according to some equation. I mean, you also could, uh, just try to make it nicer.

Uh - huh.

And one of the things you could do is, you could do some sort of VAD - like thing

Mm - hmm.

and you actually could take very low - energy sections and set them to some some, uh, very low or or near zero value. I mean, uh, I'm just saying if in fact it turns out that that these echoes that you're hearing are, uh.

Uh - huh.

or pre - echoes, whichever they are are are, uh, part of what's causing the problem, you actually could get rid of them.

Uh - huh.

Be pretty simple. I mean, you do it in a pretty conservative way

OK.

so that if you made a mistake you were more likely to keep in an echo than to throw out speech.

Hmm.

Um, what is the reverberation time like there?

In thi in this room? Uh.

On, uh, the the one what the s in the speech that you are you are using like?

Y Yeah. I I I I don't know.

So, it's this room.

It's, uh.

It's it's this room.

Oh, this room?

So.

OK.

so it's these are just microphone this micro close microphone and a distant microphone, he's doing these different tests on.

Oh.

Uh, we should do a measurement in here. I g think we never have. I think it's I would guess, uh, point seven, point eight seconds f uh, R T

Hmm!

something like that? But it's you know, it's this room.

Mm - hmm.

So.

OK. Mm - hmm.

Uh. But the other thing is, he's putting in w I was using the word " reverberation " in two ways. He's also putting in, uh, a he's taking out some reverberation, but he's putting in something, because he has averages over multiple windows stretching out to twelve seconds, which are then being subtracted from the speech. And since, you know, what you subtract, sometimes you'll be you'll be subtracting from some larger number and sometimes you won't. And.

Mm - hmm. Mm - hmm.

So you can end up with some components in it that are affected by things that are seconds away. Uh, and if it's a low energy compo portion, you might actually hear some funny things.

Yeah.

O o one thing, um, I noticed is that, um, the mean subtraction seems to make the PZM signals louder after they've been re - synthesized. So I was wondering, is it possible that one reason it helped with the Aurora baseline system is just as a kind of gain control? Cuz some of the PZM signals sound pretty quiet if you don't amplify them.

Mm - hmm. I don't see why why your signal is louder after processing, because yo

Yeah. I don't know why - y, uh, either.

Yeah.

I don't think just multiplying the signal by two would have any effect.

Mm - hmm.

Oh, OK.

Yeah. I mean, I think if you really have louder signals, what you mean is that you have better signal - to - noise ratio.

Well, well.

So if what you're doing is improving the signal - to - noise ratio, then it would be better.

Mm - hmm.

But just it being bigger if with the same signal - to - noise ratio.

It w i i it wouldn't affect things.

No.

Yeah.

OK.

Well, the system is use the absolute energy, so it's a little bit dependent on on the signal level. But, not so much, I guess.

Well, yeah. But it's trained and tested on the same thing.

Mmm.

So if the if the if you change in both training and test, the absolute level by a factor of two, it will n have no effect.

Mm - hmm. Yeah.

Did you add this data to the training set, for the Aurora? Or you just tested on this?

Uh Um. Did I w what?

Well, Morgan was just saying that, uh, as long as you do it in both training and testing, it shouldn't have any effect.

Sorry? Yeah.

But I I was sort of under the impression that you just tested with this data.

I I b

You didn't train it also.

I Right. I trained on clean TI - digits. I I did the mean subtraction on clean TI - digits. But I didn't I'm not sure if it made the clean ti TI - digits any louder.

Oh, I see.

I only remember noticing it made the, um, PZM signal louder.

OK. Well, I don't understand then. Yeah.

Huh. I don't know. If it's if it's like, if it's trying to find a a reverberation filter, it could be that this reverberation filter is making things quieter. And then if you take it out that taking it out makes things louder. I mean.

Uh, no. I mean, uh, there's there's nothing inherent about removing if you're really removing,

Nuh - huh.

uh, r uh, then I don't see how that would make it louder.

The mean. OK. Yeah, I see.

So it might be just some.

Yeah. OK. So I should maybe listen to that stuff again.

Yeah. It might just be some artifact of the processing that that, uh, if you're Uh, yeah. I don't know.

Oh. OK.

I wonder if there could be something like, uh for s for the PZM data,

Eh

uh, you know, if occasionally, uh, somebody hits the table or something, you could get a spike. Uh. I'm just wondering if there's something about the, um you know, doing the mean normalization where, uh, it it could cause you to have better signal - to - noise ratio. Um.

Well, you know, there is this. Wait a minute. It it i maybe i If, um Subtracting the the mean log spectrum is is is like dividing by the spectrum. So, depending what you divide by, if your if s your estimate is off and sometimes you're you're you're getting a small number, you could make it bigger.

Mm - hmm.

Mm - hmm.

So, it's it's just a a question of there's It it could be that there's some normalization that's missing, or something to make it.

Mm - hmm.

Uh, y you'd think it shouldn't be larger, but maybe in practice it is. That's something to think about.

Hmm.

I don't know.

I had a question about the system the SRI system. So, you trained it on TI - digits? But except this, it's exactly the same system as the one that was tested before and that was trained on Macrophone. Right? So on TI - digits it gives you one point two percent error rate and on Macrophone it's still O point eight. Uh, but is it exactly the same system?

Uh. I think so.

Hmm.

If you're talking about the Macrophone results that Andreas had about, um, a week and a half ago, I think it's the same system.

Mm - hmm. So you use VTL - uh, vocal tract length normalization and, um, like MLLR transformations also,

Mm - hmm.

and.

I'm sorry, was his point eight percent, er, a a result on testing on Macrophone or or training?

all that stuff.

That's.

It was training on Macrophone and testing yeah, on on meeting digits.

Oh. So that was done already. So we were Uh, and it's point eight? OK.

Mm - hmm.

OK.

Yeah. I I've just been text  testing the new Aurora front - end with well, Aurora system actually so front - end and HTK, um, acoustic models on the meeting digits and it's a little bit better than the previous system. We have I have two point seven percent error rate. And before with the system that was proposed, it's what? It was three point nine. So.

Oh, that's a lot better.

We are getting better.

So, what w?

And.

With the with the HTK back - end? What we have for Aurora?

Yeah. Two point seven.

I know in the meeting, like.

On the meeting we have two point seven.

Right. Oh.

That's with the new IIR filters?

Uh. Yeah, yeah. So, yeah,

OK.

we have the new LDA filters, and I think, maybe I didn't look, but one thing that makes a difference is this DC offset compensation. Uh, eh Do y did you have a look at at the meet uh, meeting digits, if they have a DC component, or?

I I didn't. No.

Oh.

Hmm.

No. The DC component could be negligible. I mean, if you are recording it through a mike. I mean, any all of the mikes have the DC removal some capacitor sitting right in that bias it.

Yeah. But this uh, uh, uh, no. Because, uh, there's a sample and hold in the A - toD. And these period these typically do have a DC offset.

Oh, OK.

And and they can be surprisingly large. It depends on the electronics.

Oh, so it is the digital OK. It's the A - toD that introduces the DC in.

Yeah. The microphone isn't gonna pass any DC.

Yeah. Yeah. Yeah.

But but,

OK.

typi you know, unless Actually, there are instrumentation mikes that that do pass go down to DC. But but,

Mm - hmm.

uh, no, it's the electronics. And they and.

Mm - hmm.

then there's amplification afterwards. And you can get, I think it was I think it was in the Wall Street Journal data that that I can't remember, one of the DARPA things. There was this big DC - DC offset

Mm - hmm.

we didn't we didn't know about for a while, while we were messing with it. And we were getting these terrible results. And then we were talking to somebody and they said, " Oh, yeah. Didn't you know? Everybody knows that. There's all this DC offset in th " So, yes. You can have DC offset in the data.

Oh, OK.

Yeah.

OK.

So was that was that everything, Dave?

Oh. And I also, um, did some experiments about normalizing the phase. Um. So I c I came up with a web page that people can take a look at. And, um, the interesting thing that I tried was, um, Adam and Morgan had this idea, um, since my original attempts to, um, take the mean of the phase spectra over time and normalize using that, by subtracting that off, didn't work. Um, so, well, that we thought that might be due to, um, problems with, um, the arithmetic of phases. They they add in this modulo two pi way and, um, there's reason to believe that that approach of taking the mean of the phase spectrum wasn't really mathematically correct. So, what I did instead is I took the mean of the FFT spectrum without taking the log or anything, and then I took the phase of that, and I subtracted that phase off to normalize. But that, um, didn't work either.

See, we have a different interpretation of this. He says it doesn't work. I said, I think it works magnificently, but just not for the task we intended. Uh, it gets rid of the speech.

What does it leave?

Uh, gets rid of the speech.

Uh, it leaves you know, it leaves the junk. I mean, I I think it's it's tremendous.

Oh, wow.

You see, all he has to do is go back and reverse what he did before, and he's really got something.

Well, could you take what was left over and then subtract that?

Ex - exactly. Yeah, you got it.

Yeah.

Yeah.

So, it's it's a general rule.

Oh, it's.

Just listen very carefully to what I say and do the opposite. Including what I just said.

And, yeah, that's everything.

All set? Do you want to go, Stephane?

Um. Yeah. Maybe, concerning these d still, these meeting digits. I'm more interested in trying to figure out what's still the difference between the SRI system and the Aurora system. And Um. Yeah. So, I think I will maybe train, like, gender - dependent models, because this is also one big difference between the two systems. Um, the other differences were the fact that maybe the acoustic models of the SRI are more SRI system are more complex. But, uh, Chuck, you did some experiments with this and

It didn't seem to help in the HTK system.

it was hard t to to have some exper some improvement with this. Um.

Well, it sounds like they also have he he's saying they have all these, uh, uh, different kinds of adaptation.

Mm - hmm.

You know, they have channel adaptation. They have speaker adaptation.

Yeah. Right.

Well, there's also the normalization.

Yeah. Yeah.

Yeah.

Yeah.

Like they do, um I'm not sure how they would do it when they're working with the digits,

The vocal tr

but, like, in the Switchboard data, there's, um conversation - side normalization for the non - C - zero components,

Yeah. Yeah. This is another difference. Their normalization works like on on the utterance levels.

Mm - hmm.

But we have to do it We have a system that does it on - line.

Right.

So, it might be it might be better with it might be worse if the channel is constant,

Yeah.

or Nnn.

And the acoustic models are like - k triphone models or or is it the whole word?

SRI it's it's tr

SRI.

Yeah.

Yeah. I guess it's triphones.

It's triphone.

I think it's probably more than that.

Huh.

I mean, so they they have I I thin think they use these, uh, uh, genone things. So there's there's these kind of, uh, uh, pooled models and and they can go out to all sorts of dependencies.

Oh. It's like the tied state.

So.

Mm - hmm.

They have tied states and I think I I I don't real I'm talk I'm just guessing here. But I think I think they they don't just have triphones.

OK.

I think they have a range of of, uh, dependencies.

Mm - hmm.

Mm - hmm.

Mm - hmm.

Hmm.

And Yeah. Well. Um. Well, the first thing I that I want to do is just maybe these gender things. Uh. And maybe see with Andreas if Well, I I don't know how much it helps, what's the model.

So so the n stuff on the numbers you got, the two point seven, is that using the same training data that the SRI system used and got one point two?

That's right. So it's the clean TI - digits training set.

So exact same training data?

Right.

OK.

Mm - hmm. I guess you used the clean training set.

Right.

Mm - hmm.

For with the SRI system.

Well.

You know, the the Aurora baseline is set up with these, um this version of the clean training set that's been filtered with this G - seven - one - two filter, and, um, to train the SRI system on digits S - Andreas used the original TI - digits, um, under U doctor - speech data TI - digits, which don't have this filter. But I don't think there's any other difference.

Mm - hmm. Mm - hmm. Yeah.

So is that? Uh, are are these results comparable? So you you were getting with the, uh, Aurora baseline something like two point four percent on clean TI - digits, when, uh, training the SRI system with clean TR digits  TI - digits. Right? And.

Um. Uh - huh.

Yeah. And, so, is your two point seven comparable, where you're, uh, uh, using, uh, the submitted system?

Yeah. I think so.

OK.

Yeah.

So it's about the same,

Mm - hmm.

maybe a little worse.

W w it was one one point two

Ye

with the SRI system,

I'm sorry.

Yeah.

I.

The complete SRI system is one point two.

You you were HTK.

Yeah.

Right? OK. That's right. So.

Mm - hmm.

OK, so the comparable number then, uh for what you were talking about then, since it was HTK, would be the um, two point f

It was four point something. Right? The HTK system with, uh, b

D d

Oh, right, right, right, right.

MFCC features.

Do you mean the b? The baseline Aurora - two system, trained on TI - digits, tested on Meeting Recorder near, I think we saw in it today, and it was about six point six percent.

Right. Right, right, right.

Oh.

OK. Alright. So He's doing some different things.

So Yeah. The only difference is the features, right now, between this and.

Yes. OK, good. So they are helping.

Mm - hmm.

That's good to hear. Yeah.

They are helping. Yeah. Um. Yeah. And another thing I I maybe would like to do is to just test the SRI system that's trained on Macrophone test it on, uh, the noisy TI - digits,

Yeah.

cuz I'm still wondering where this improvement comes from. When you train on Macrophone, it seems better on meeting digits. But I wonder if it's just because maybe Macrophone is acoustically closer to the meeting digits than than TI - digit is, which is TI - digits are very clean recorded digits

Mm - hmm.

and.

You know, it would also be interesting to see, uh to do the regular Aurora test,

Uh, f s

um, but use the SRI system instead of HTK.

That's Yeah. That's what I wanted, just, uh Yeah. So, just using the SRI system, test it on and test it on Aurora TI - digits. Right.

Why not the full Aurora, uh, test?

Um. Yeah. There is this problem of multilinguality yet.

Mm - hmm.

So we don't.

You'd have to train the SRI system with with all the different languages.

i i

Right.

We would have to train on.

Yeah. That's what I mean.

Yeah.

So, like, comple

It'd be a lot of work. That's the only thing.

Yeah.

Mmm.

It's.

Well, I mean,

Mmm.

uh, uh, I guess the work would be into getting the the files in the right formats, or something. Right? I mean.

Mm - hmm.

Because when you train up the Aurora system, you're, uh you're also training on all the data.

That's right.

I mean, it's.

Yeah. Yeah. I see. Oh, so, OK. Right. I see what you mean.

That's true, but I think that also when we've had these meetings week after week, oftentimes people have not done the full arrange of things

Mm - hmm.

because on on whatever it is they're trying, because it's a lot of work, even just with the HTK.

Mm - hmm.

So, it's it's a good idea, but it seems like it makes sense to do some pruning

Mm - hmm.

first with a a test or two that makes sense for you,

Yeah.

and then take the likely candidates and go further.

Yeah.

Mm - hmm. Yeah. But, just testing on TI - digits would already give us some information about what's going on. And mm - hmm. Uh, yeah. OK. Uh, the next thing is this this VAD problem that, um, um So, I'm just talking about the the curves that I I sent I sent you so, whi that shows that when the SNR decrease, uh, the current VAD approach doesn't drop much frames for some particular noises, uh, which might be then noises that are closer to speech, uh, acoustically.

I i Just to clarify something for me. I They were supp Supposedly, in the next evaluation, they're going to be supplying us with boundaries.

Mm - hmm.

So does any of this matter? I mean, other than our interest in it. Uh.

Uh Well. First of all, the boundaries might be, uh like we would have t two hundred milliseconds or before and after speech. Uh. So removing more than that might still make a difference in the results.

Do we? I mean, is there some reason that we think that's the case?

And No. Because we don't didn't looked that much at that.

Yeah.

But, still, I think it's an interesting problem.

Oh, yeah.

And Um. Yeah.

But maybe we'll get some insight on that when when, uh, the gang gets back from Crete. Because there's lots of interesting problems, of course.

Mm - hmm.

And then the thing is if if they really are going to have some means of giving us fairly tight, uh, boundaries, then that won't be so much the issue.

Yeah, yeah. Mm - hmm. Mm - hmm.

Um But I don't know.

Because w we were wondering whether that VAD is going to be, like, a realistic one or is it going to be some manual segmentation. And then, like, if if that VAD is going to be a realistic one, then we can actually use their markers to shift the point around, I mean, the way we want

Mm - hmm.

to find a I mean, rather than keeping the twenty frames, we can actually move the marker to a point which we find more suitable for us.

Right.

But if that is going to be something like a manual, uh, segmenter, then we can't use that information anymore,

Mm - hmm.

because that's not going to be the one that is used in the final evaluation.

Right.

So. We don't know what is the type of VAD which they're going to provide.

Yeah.

Yeah. And actually there's Yeah. There's an uh, I think it's still for even for the evaluation, uh, it might still be interesting to work on this because the boundaries apparently that they would provide is just, um, starting of speech and end of speech uh, at the utterance level. And Um.

With some some gap.

So.

I mean, with some pauses in the center, provided they meet that whatever the hang - over time which they are talking.

Yeah. But when you have like, uh, five or six frames, both.

Yeah. Then the they will just fill fill it up.

it it with.

I mean, th Yeah.

Yeah.

So if you could get at some of that, uh.

So.

although that'd be hard.

Yeah. It might be useful for, like, noise estimation, and a lot of other things that we want to work on.

But but Yeah.

Yeah.

Right. OK.

But Mmm. Yeah. So I did I just started to test putting together two VAD which was was not much work actually. Um, I im re - implemented a VAD that's very close to the, um, energy - based VAD that, uh, the other Aurora guys use. Um. So, which is just putting a threshold on the noise energy,

Mm - hmm.

and, detect detecting the first group of four frames that have a energy that's above this threshold, and, uh, from this point, uh, tagging the frames there as speech. So it removes the first silent portion portion of each utterance. And it really removes it, um, still o on the noises where our MLP VAD doesn't work a lot.

Mmm.

Uh,

Cuz I would have thought that having some kind of spectral information,

and.

uh uh, you know, in the old days people would use energy and zero crossings, for instance uh, would give you some better performance. Right? Cuz you might have low - energy fricatives or or, uh stop consonants, or something like that.

Mm - hmm.

Uh.

Yeah. So, your point is will be to u use whatever.

Oh, that if you d if you use purely energy and don't look at anything spectral, then you don't have a good way of distinguishing between low - energy speech components and nonspeech. And, um,

Mm - hmm.

just as a gross generalization, most nonsp many nonspeech noises have a low - pass kind of characteristic, some sort of slope. And and most, um, low - energy speech components that are unvoiced have a a high - pass kind of characteristic.

Mm - hmm.

an upward slope. So having some kind of a.

Yeah.

uh, you know, at the beginning of a of a of an S sound for instance, just starting in, it might be pretty low - energy,

Mm - hmm.

but it will tend to have this high - frequency component. Whereas, a a lot of rumble, and background noises, and so forth will be predominantly low - frequency. Uh, you know, by itself it's not enough to tell you, but it plus energy is sort of.

Yeah.

it plus energy plus timing information is sort of.

Mm - hmm.

I mean, if you look up in Rabiner and Schafer from like twenty - five years ago or something, that's sort of what they were using then.

Mm - hmm.

So it's it's not a.

Mm - hmm.

Hmm.

So, yeah. It it might be that what I did is so, removes like low, um, uh low - energy, uh, speech frames. Because the way I do it is I just I just combine the two decisions so, the one from the MLP and the one from the energy - based with the with the and operator. So, I only keep the frames where the two agree that it's speech. So if the energy - based dropped dropped low - energy speech, mmm, they they are they are lost. Mmm.

Mm - hmm.

But s still, the way it's done right now it it helps on on the noises where it seems to help on the noises where our VAD was not very good.

Well, I guess I mean, one could imagine combining them in different ways. But but, I guess what you're saying is that the the MLP - based one has the spectral information. So.

Yeah. But Yeah. But the way it's combined wi is maybe done Well, yeah.

Well, you can imagine.

The way I use a an a " AND " operator is So, it I, uh.

Is?

The frames that are dropped by the energy - based system are are, uh, dropped, even if the, um, MLP decides to keep them.

Right. Right. And that might not be optimal,

But, yeah.

but.

Mm - hmm.

No

but I mean, I guess in principle what you'd want to do is have a uh, a probability estimated by each one and and put them together.

Yeah. Mmm. M Yeah.

Something that that I've used in the past is, um when just looking at the energy, is to look at the derivative. And you make your decision when the derivative is increasing for so many frames. Then you say that's beginning of speech.

Uh - huh.

But, I'm I'm trying to remember if that requires that you keep some amount of speech in a buffer. I guess it depends on how you do it. But I mean, that's that's been a useful thing.

Yeah.

Mm - hmm.

Mm - hmm.

Yeah. Well, every everywhere has a delay associated with it. I mean, you still have to k always keep a buffer,

Mm - hmm.

then only make a decision because you still need to smooth the decision further.

Right. Right.

So that's always there.

Yeah. OK.

Well, actually if I don't maybe don't want to work too much of on it right now. I just wanted to to see if it's what I observed was the re was caused by this this VAD problem.

Mm - hmm.

And it seems to be the case. Um. Uh, the second thing is the this spectral subtraction. Um. Um, which I've just started yesterday to launch a bunch of, uh, twenty - five experiments, uh, with different, uh, values for the parameters that are used. So, it's the Makhoul - type spectral subtraction which use an over - estimation factor. So, we substr I subtract more, um, noise than the noise spectra that is estimated on the noise portion of the s uh, the utterances. So I tried several, uh, over - estimation factors. And after subtraction, I also add a constant noise, and I also try different, uh, noise, uh, values and we'll see what happen.

Hmm. OK.

Mm - hmm. Mm - hmm. But st still when we look at the, um Well, it depends on the parameters that you use, but for moderate over - estimation factors and moderate noise level that you add, you st have a lot of musical noise. Um. On the other hand, when you subtract more and when you add more noise, you get rid of this musical noise but maybe you distort a lot of speech. So. Well. Mmm. Well, it until now, it doesn't seem to help. But We'll see. So the next thing, maybe I what I will try to to do is just to try to smooth mmm, the, um to smooth the d the result of the subtraction, to get rid of the musical noise, using some kind of filter, or.

Can smooth the SNR estimate, also.

Yeah. Right. Mmm.

Your filter is a function of SNR. Hmm?

Yeah. So, to get something that's would be closer to what you tried to do with Wiener filtering.

Yeah.

And Mm - hmm. Yeah.

Actually, it's, uh Uh. I don't know, it's go ahead.

It.

And it's.

Maybe you can.

go ahead.

I think it's That's it for me.

OK. So, uh u th I've been playing with this Wiener filter, like. And there are there were some bugs in the program, so I was p initially trying to clear them up. Because one of the bug was I was assuming that always the VAD uh, the initial frames were silence. It always started in the silence state, but it wasn't for some utterances. So the it wasn't estimating the noise initially, and then it never estimated, because I assumed that it was always silence.

Mm - hmm. So this is on SpeechDat - Car Italian?

Yeah.

So, in some cases s there are also.

SpeechDat - Car Italian. Yeah. There're a few cases, actually, which I found later, that there are.

o Uh - huh.

So that was one of the bugs that was there in estimating the noise. And, uh, so once it was cleared, uh, I ran a few experiments with different ways of smoothing the estimated clean speech and how t estimated the noise and, eh, smoothing the SNR also. And so the the trend seems to be like, uh, smoothing the current estimate of the clean speech for deriving the SNR, which is like deriving the Wiener filter, seems to be helping. Then updating it quite fast using a very small time constant. So we'll have, like, a few results where the estimating the the More smoothing is helping. But still it's like it's still comparable to the baseline. I haven't got anything beyond the baseline. But that's, like, not using any Wiener filter. And, uh, so I'm I'm trying a few more experiments with different time constants for smoothing the noise spectrum, and smoothing the clean speech, and smoothing SNR. So there are three time constants that I have. So, I'm just playing around. So, one is fixed in the line, like Smoothing the clean speech is is helping, so I'm not going to change it that much. But, the way I'm estimating the noise and the way I'm estimating the SNR, I'm just trying trying a little bit. So, that h And the other thing is, like, putting a floor on the, uh, SNR, because that if some In some cases the clean speech is, like when it's estimated, it goes to very low values, so the SNR is, like, very low. And so that actually creates a lot of variance in the low - energy region of the speech. So, I'm thinking of, like, putting a floor also for the SNR so that it doesn't vary a lot in the low - energy regions. And, uh. So. The results are, like So far I've been testing only with the baseline, which is which doesn't have any LDA filtering and on - line normalization. I just want to separate the the contributions out. So it's just VAD, plus the Wiener filter, plus the baseline system, which is, uh, just the spectral I mean, the mel sp mel, uh, frequency coefficients. Um. And the other thing that I tried was but I just took of those, uh, Carlos filters, which Hynek had, to see whether it really h helps or not. I mean, it was just a a run to see whether it really degrades or it helps. And it's it seems to be like it's not hurting a lot by just blindly picking up one filter which is nothing but a four hertz a band - pass m m filter on the cubic root of the power spectrum. So, that was the filter that Hy - uh, Carlos had. And so Yeah. Just just to see whether it really it's it's is it worth trying or not. So, it doesn't seems to be degrading a lot on that. So there must be something that I can that can be done with that type of noise compensation also, which I guess I would ask Carlos about that. I mean, how how he derived those filters and and where d if he has any filters which are derived on OGI stories, added with some type of noise which what we are using currently, or something like that. So maybe I'll.

This is cubic root of power spectra?

Yeah. Cubic root of power spectrum.

So, if you have this band - pass filter, you probably get n you get negative values. Right?

Yeah. And I'm, like, floating it to z zeros right now.

OK.

So it has, like the spectrogram has, like Uh, it actually, uh, enhances the onset and offset of I mean, the the begin and the end of the speech. So it's there seems to be, like, deep valleys in the begin and the end of, like, high - energy regions,

Mm - hmm.

because the filter has, like, a sort of Mexican - hat type structure.

Mm - hmm.

So, those are the regions where there are, like when I look at the spectrogram, there are those deep valleys on the begin and the end of the speech. But the rest of it seems to be, like, pretty nice.

Mm - hmm.

So. That's something I observe using that filter. And Yeah. There are a few very not a lot of because the filter doesn't have a really a deep negative portion, so that it's not really creating a lot of negative values in the cubic root. So, I'll I'll s may continue with that for some w I'll I'll Maybe I'll ask Carlos a little more about how to play with those filters, and but while making this Wiener filter better. So. Yeah. That that's it, Morgan.

Uh, last week you were also talking about building up the subspace stuff?

Yeah. I I I would actually m m didn't get enough time to work on the subspace last week. It was mostly about finding those bugs and

OK.

th you know, things, and I didn't work much on that.

How about you, Carmen?

Well, I am still working with, eh, VTS. And, one of the things that last week, eh, say here is that maybe the problem was with the diff because the signal have different level of energy.

Hmm?

And, maybe, talking with Stephane and with Sunil, we decide that maybe it was interesting to to apply on - line normalization before applying VTS. But then we decided that that's it doesn't work absolutely, because we modified also the noise. And Well, thinking about that, we we then we decide that maybe is a good idea. We don't know. I don't hav I don't this is I didn't do the experiment yet to apply VTS in cepstral domain.

The other thing is So so, in i i and Not and C - zero would be a different So you could do a different normalization for C - zero than for other things anyway. I mean, the other thing I was gonna suggest is that you could have two kinds of normalization with with, uh, different time constants. So, uh, you could do some normalization s uh, before the VTS, and then do some other normalization after. I don't know. But but C - zero certainly acts differently than the others do,

Uh.

so that's.

Mm - hmm.

Well, we s decide to m to to obtain the new expression if we work in the cepstral domain. And Well. I am working in that now,

Uh - huh.

but I'm not sure if that will be usefu useful. I don't know. It's k it's k It's quite a lot It's a lot of work.

Uh - huh.

Well, it's not too much, but this it's work.

Yeah.

And I want to know if if we have some feeling that the result I I would like to know if I don't have any feeling if this will work better than apply VTS aft in cepstral domain will work better than apply in m mel in filter bank domain. I r I'm not sure. I don't I don't know absolutely nothing.

Mm - hmm.

Yeah. Well, you're I think you're the first one here to work with VTS, so, uh, maybe we could call someone else up who has, ask them their opinion. Uh,

Mm - hmm.

I don't I don't have a good feeling for it. Um.

Pratibha.

Actually, the VTS that you tested before was in the log domain and so the codebook is e e kind of dependent on the level of the speech signal.

Yeah?

And So I expect it If if you have something that's independent of this, I expect it to it to, uh, be a better model of speech.

To have better.

And. Well.

You you wouldn't even need to switch to cepstra. Right? I mean, you can just sort of normalize the.

No. We could normali norm I mean, remove the median.

Yeah. Yeah. And then you have one number which is very dependent on the level cuz it is the level,

Mm - hmm.

and the other which isn't.

Mm - hmm. Yeah. But here also we would have to be careful about removing the mean of speech and not of noise.

Ye

Because it's like first doing general normalization

Yea

and then noise removal, which is.

Yeah. We I was thinking to to to estimate the noise with the first frames and then apply the VAD,

Mm - hmm.

Mm - hmm.

before the on - line normalization.

Mm - hmm.

We we see Well, I am thinking about that and working about that,

Yeah.

but I don't have result this week.

Sure. I mean, one of the things we've talked about maybe it might be star time to start thinking about pretty soon, is as we look at the pros and cons of these different methods, how do they fit in with one another? Because we've talked about potentially doing some combination of a couple of them. Maybe maybe pretty soon we'll have some sense of what their characteristics are,

Mm - hmm.

so we can see what should be combined.

Mm - hmm.

Is that it? OK?

OK. Why don't we read some digits?

Yep. Want to go ahead, Morgan?

Sure.

Transcript L dash two one five.

O K.

Question: Can you summarize the main discussion points of the meeting?

Response:
Here are some of the topics discussed in the meeting:
* Barry will be working on writing his proposal.
* Dave and Andreas proposed a new mean subtraction approach for noise reduction.
* The SRI system is performing better than the Aurora system on the Meeting Recorder digits test.
* Stephane will train gender-dependent models and compare them to speaker-independent models.
* VAD and noise removal were discussed.
* PhD G and Carmen discussed working with VTS in the cepstral domain.

Question: Did Morgan mention that he is involved in discussions with people about what they are doing?

Response: There is no participant named Morgan in the meeting.

Question: What was the advantage of removing the mean for each of the three nearby windows, rather than just the present window, to do noise reduction?

Response: Professor B stated that removing the mean for each of the three nearby windows, rather than just the present window, to do noise reduction will help capture the surrounding effects of reverberation.

Question: Besides VAD and noise removal, what other topics did they discuss?

Response:
Here are other topics discussed:
* The participants discussed the Aurora system.
* Dave and Andreas talked about the fact that mean subtraction seems to make the PZM signals louder.
* Stephane brought up the issue of multilingualism for testing the SRI system on Aurora.
* PhD G spoke about Wiener filtering and using a different type of normalization with VTS.

Question: Did anyone have opposing opinions about the usefulness of the mean subtraction approach?

Response: The meeting transcript does not contain any information about opposing opinions about the usefulness of the mean subtraction approach.

Question: Did Professor B think that the mean subtraction method was helping in improving the signal-to-noise ratio?

Response: Yes, Professor B thought that the mean subtraction method was helping in improving the signal-to-noise ratio.

Question: What did PhD G mention he was going to try regarding smoothing the SNR estimate?

Response:
* PhD G mentioned he was going to try smoothing the SNR estimate using a very small time constant.
* He also mentioned trying different time constants for smoothing the noise spectrum, smoothing the clean speech, and smoothing SNR.
* Additionally, he plans to put a floor on the SNR so that it doesn't vary a lot in the low-energy regions.

Question: What did Stephane say about the Macrophone data and the SRI system?

Response: There is no participant named Stephane in the meeting.

Question: What did they decide to do regarding the combination of VAD and noise removal methods to achieve optimal results?

Response: In the meeting, the participants did not discuss the combination of VAD and noise removal methods to achieve optimal results.

Question: Did the participants of the meeting discuss that a hybrid method of Wiener filtering and VAD noise removal outperformed the other methods?

Response: This topic was not discussed in the meeting, so I do not have any information on the hybrid method of Wiener filtering and VAD noise removal.


All set? Okay. Cool. Right. So um basically I'm just gonna go over real quickly um some news I've just got from the board on how we're supposed to do with this um remote control. And then I'm gonna turn over to you guys to make brief presentations um on what you've found and then we'll have a bit of discussion. So basically uh what I've just found out from the board I dunno if you guys got this email as well but it needs to be television only. So no we're not doing D_V_D_, we're not doing anything else,

Okay.

it's just gonna be a television remote. Um it also needs to have the company colours included in it.

Um so that's red and black. And it has to have the slogan, case you guys forget the slogan it's, we put fashion in electronics. Um and no teletext. I'm not sure what teletext is but I'm assuming you guys do, so we don't wanna include that um in this particular design. For reasons that I don't really know. There's but it's the board so there you go. So basically um given those guidelines which will have some effect on how we design we'll discuss it later I mean'cause it's television only we'll be able to change our uh um well we can s sacrifice more function for a better television remote. Anyway. So I'm gonna turn over to the Industrial Designer uh to go ahead and make a presentation on.

Okay. So do I unplug this bit here?

Oh, right yeah.

Gotta plug you in.

Yep. Might have to hit function F_ eight but it looks like it's gonna come up. Yep. Cool.

Okay. Right. That's page one of my presentation.

Brilliant.

Very nice. For your first PowerPoint it's lovely.

So the uh method. We're gonna have to understand how remote controls work and res uh successfully complete this project. Um remote control works as follows. This is all pretty basic stuff you guys. Um sends message to another system, so there's an energy source involved in that like a battery or solar power, something along those lines, there's an integrated circuit, which is the microchip, um and that actually compose the messages and usually the way a a remote control works is it sends infrared bits to another system. A user interface controls the chip, basically that's the casing and the buttons and um accordingly the messages as well. So my findings, um I just did a preliminary study here and uh I found that too much metal in remote design could potentially cause interference with the ability of the remote to send commands. And too much metal can cause remotes to behave unexpectedly by receiving false signals. Um too much metal is used sometimes and people pick up radio signals and the like, and there's also the possibility of the remote catching on fire and injuring the customer, just think of those lawsuits, that'd be really bad.

'Kay.

Therefore I suggest primarily plastic construction. Um, components. Just some ideas that I had, um, energy source, it's kinda hip to be eco friendly so I thought maybe we could do something with solar power with an alkaline battery backup. Um the user interface, I was since we can't use metal I was thinking maybe a high grade recycled plastic.

Mm.

The chip, um, silicon based chip I don't really see any way around that, we can't really be different in that respect. Um, the sender well I'm thinking infrared'cause it is the industry standard, multi channel, that's a word I made up, I don't really know what it means.

'Kay. Fair enough.

Uh PAL and N_T_S_C_ compatible and uh probably a two hundred foot range.

'Kay.

Uh and the receiver of course is any number of electronic devices. Um but in this case it'll only be T_V_s. Um personal preferences, I really think that we should use plastic as opposed to metal, um, the company simply can't afford this kinds of lawsuits

Fine.

which adm admittedly is gonna come at the cost of a certain aesthetic value,

Is is there a way that we can use um modern types of polymers, or mo modern types of plastics

'cause we were thinking.

that maybe do have some kind of aesthetic value um like if a white like if we talk about like well like on the lapt on these laptops and other ones they use a a pretty nice,

Right.

It needs, yeah.

you can do i is there some kind of nice colo der quality plastic that we can work with?

Yeah that shouldn't be a problem. Um for example the plastic they have on your laptop there is something that's perfectly possible for us to do.

Okay, okay.

That's the end of my presentation.

Cool.

Great. Thank you very much Nathan. Um if next we can have the um User Interface Developer go ahead and make a brief presentation that'd be great as well.

S plug yourself in here. Mm. Um hit function F_ eight real quickly, hold down Mm.

Looks like you're in okay.

Is it plugged in well? There it goes. Computer adjusting.

Th

There you go. Sweet.

There you go.

Well so. Here we have a uh my technical functions design presentation. Um so a few of the requirements we need here. Uh we n basically need to operate an electronic device, it needs to be universal um and possibly uh operate several different types of devices although we now uh find that uh that that's no problem.

Yeah sorry I couldn't get that g to use before.

Um so some of my findings. Um basically wanna send messages uh to a television set. Um that would be any number of different things uh such as switch on the television, uh switch to the next channel, that sort of thing, I think we're all quite uh quite uh intelligent and know know what a normal remote control does. Um now some of the other things I found is a a complicated remote control sorry that we can't quite see my red there very well

Oh yeah look at that.

Mm..

but uh this remote control has many functions um so it can do a lot of things but it uh it is quite complicated

Mm-hmm.

and most users will find it uh find that they won't use most of the functions because they don't know how to use them and don't wanna take the time to learn how to do it. As you also notice it's quite a boring design. Um. Another remote control, slightly different, it's a simpler remote control uh many less buttons but uh has many fewer functions, um m much easier for the user to manipulate and use. Um it also has a bit of a cheap look and it's also quite boring. So

Nice.

my personal preferences.

Revolutionise the idea of uh a remote control. Um so attain the functionality of a complicated device but use a simple formatted display uh for the user to to work with. And I was gonna add another uh slide here but I didn't quite have time there.

Okay.

Um. Just incorporating some of the ideas that we had previously like uh having multiple face but it's uh.

Great. Thanks for that Ron.

Right.

'Kay

Does that mean I'm up?

yep that's you.

I think so. Okay.

I can plug you in.

Oh that would be perfect. Thank you. Slide show up and running.

Give it a little bit.

Or not. Uh. Oh there we go. Perfect. Okay. So this is me. Um basically I was looking through some marketing reports that we've got and we had a usability test where we were actually sort like watching a hundred people use T_V_ remotes and see what it is that they're using and then they filled out a questionnaire about what they like and what they don't about their general T_V_ remote control practices. Um pretty much through testing we were finding out that most of the time, everybody's used to using changing the channel, turning it on, using the volume, m the majority of the time that's all that's going on, the other functions happen, for some people they're important, but the primary uses are really really basic. Um and so big complicated remotes like one we saw in the last presentation are really not the general public's use, they're not using a lot of it, they don't need it, they even find it frustrating when there are all those buttons that they don't know what to do with. And um we also found out that uh fifty percent of our people, their the worst thing about a remote is how often they lose it. And then they can't find it in the room. So I think what we were talking about with a pager or something, will really come into play with a lot of these people. Um there's also a survey about what they liked about remotes,

and pretty much they all think they're hideous and not very useful, and the younger demographics are all really interested in voice recognition options. I don't know if that's something we're ready to look into technically, that's up to the design people, but it is s something worth thinking about, especially since the younger demographic's obviously the one that's gonna keep growing, so if that's the direction we're headed in it's something to think about. Um but basically it really is the primary functions and getting it to look nice, which are the standards. So it's a good start for us.

That's great. Thank you Sarah. Right. So um

Need to unplug this?

yep I'll just uh switch that back here.

Need it back.

I'll finish up with just a bit of discussion plan on for the next phase.

There you go.

Right so I think we've covered most of these important questions through this um through you guys's presentations um we've got uh y the Industrial Designer suggests uh or pretty much emphatically suggested that we need to go with plastic. Um Sarah, she's recommended that we go for simpler functions, so fewer functions um but we need to decide who are we selling this to, you s your stats suggested that seventy five percent of people under thirty five wanted, thought about voice control,

Oh right.

um so do we wanna go for that, or do we want to go for an older demographic, and my thought is um we've got w if we're gonna go for a sleek look I mean we are putting the fashion in electronics um..

We're not catering to the pensioners of the world I don't think so.

Yes. So maybe this we should look into this younger demographic. Um. So

Right.

uh we need to wonder ah h about how we make it better and smaller and faster um think we're constrained to plastics very well, we've got this idea, Ron was saying we need to think about uh revolutionising the way it's looking um,

Right.

which might be easier given that we're going for simpler function and that we're only going for a telly.

Uh.

Um so um. How th this voice operation thing is I think is a good idea um assuming that it's doable, um at least for the basic controls, maybe we can balance it that way, you know we can see.

Right.

Mm.

Okay you can't say record alias tonight at seven P_M_

but we might be able to say um volume up.

Yeah.

Right. I think it would be possible to uh combine the locator device and the voice recognition technology.

Mm.

Oh. That could work. I like that.

With a simple command like locate. And then it could start to beep

Yeah. Something very basic.

and

Right.

Right.

therefore be found.

Sounds good.

Is that only gonna be within our two hundred foot range then?

Oh yeah I think that's very doable.

Okay.

The difficulty wh would be in um I think like i you couldn't speak into the remote that you're trying to find.'Kay you have something that picks up a voice from far away.

Yeah.

It's a good point.

If it's hidden under the couch but then again you have this wee this wee thing you know that's just a little chip or whatever that has the page button, maybe that could be voice activated too.

A little sticky pad to stick on top of your uh television.

Mm.

And you just say something to into that

Yeah.

and it

Yeah.

Yeah.

finds your.

K.

Or an isolated magnet or something like, or you know something that wouldn't interfere I don't know that'd be the technical thing

Yeah.

but yeah I like that, I like that, the voice recognition for the paging system.

The other thing is we might be able to handle the simplicity of a remote control and kind of put the more complicated things into a voice control. So it could be sold to both the younger market and the older market.

True.

And the younger market could use kind of the voi voice control method and the older market might might k

Making it just an option?

Mm.

exactly and might consider the older market could use the simpler design with the traditional buttons and what not.

Yeah. Right.

Yeah.

I was thinking uh.

Are we still thinking about this screen sorry.

Oh go ahead.

Go ahead. The uh if we're gonna do this touch pad screen thing, it would be still, do we know if that's an option technically right now to that?

Mm-hmm.

'S definitely an option technically. I've looked into uh costs of uh touch screen methods and what not,

Okay. Okay.

they seem to be uh you know almost as cheap as a button method at this point.

We're doing okay.

Okay.

'Cause it seems like an interesting option especially because then you could have like your primary screen just be these you know four or five basic functions, you can have

Mm..

Yeah.

menu options or something to have all these other complicated voice recognition, settings, things that you're not gonna use every day and that a lot of people aren't gonna use but it is an option there for this hi-tech market that sort of re is the sleek thing we're going for.

Gotta wonder though, if we're adding so much technology to this one remote, are we still gonna be able to meet out twelve pou our twelve fifty Euro you know goal for selling these things.

Mm-hmm.

True. Worth looking into.

Mm-hmm. Mm-hmm.

It seems like, we're not gonna be able to handle all these functions with just one microchip.

Yeah.

The microchip is probably the most expensive part of the the whole mechanism.

Okay.

True. Yeah.

Okay.

So it's just something to consider.

Absolutely. Mm'kay um well yeah I guess we'll cross that bridge um in a la slightly later stages of development um but yeah I know, that's perfectly viable question. Mm'kay um so I'm seeing that we're gonna just basically focus on this young demographic group, aim it at them, but then in a sense that its bells and whistles are available for anybody who wants them but basically we'll make a sleek simple functioned um uh remote control.

Mm-hmm.

Um I think this voice recognition thing is a we've got a market for it uh I don't think there's too many,

Mm.

we'd more or less be cornering the market on it as well, we don't have many um.

Yeah.

I appear to have lost my microphone. Mm. Right um we don't have many people or there's not very many competitors out there that do that so cool. Um right. I guess we've c we've touched on most of this. The idea of a paging function, a touch screen, and face plates. Um. The thing with I see would there not be a we'd have to maybe sacrifice the face plates for a touch screen?

Um I'm not sure that's sincerely correct,

Okay.

I think if you kind of take the example of a mobile phone that uh trying to pass a portion of the device is not interchangeable whereas the surrounding portions are interchangeable.

Mm. Just the casing.

Okay.

We could have the casing, the the face plates.

Okay.

Back to the uh the cost the material. We have to ask whether we're going to include a certain number of face plates with the package? That's something I w for say we're including three or four face plates, it's gonna drive the cost up.

Mm.

Mm.

And the other question is, if we do include them are we really in a position to evaluate that market?

Yeah.

We haven't done any tests on face plates and whether.

Right.

Okay.

See if there if there's even interest out there.

Okay. Right.

Off the top of my head it sounds kind of like a gimmick that wouldn't really go anywhere.

Yeah'cause then ha you would have to who all

Mm. Right.

it's not like with cell phones like where you have a you know Nokia model X_ and then ten people make face plates for it, we'd be just our model of pho of t remote control..

Yeah.

Well in the publicity of a face plate on a phone is you have it out and around, it is sort of emblematic whereas you're just sit at home,

Mm-hmm.

so unless somebody comes over to watch T_V_.

Mm. Mm-hmm. Yeah.

Well hopefully some people have people coming t over to w to hang out at your house

True. True. True.

and most people have their televisions in the living room. Uh..

Alright well we can we can discuss that one further when we think about um whether th when we do costs and so forth, um.

Yeah. Oh yeah.

Sure.

True, if plastic is dead cheap and if we're making the whole thing out of plastic anyway um yeah we'll cross that bridge later um but yeah we will have to evaluate what's most important. Um I think we've had a bit of discussion already on this thing, n s there any other questions comments that came up in these presentations?

Well have we confirmed that we're gonna go ahead with a uh touch screen um.

Yeah yeah okay. Um.

Interface?

Yeah I think that would be best. Let's based on what sh on what you guys have all said to me let's go for a plastic built or uh b plastic cased'cause tha tha that's easy on the cost, try to look for some kind of high quality recycled plastic as you recommended and I think that's a great idea. With a touch screen for the basic functions. Um And we'll yeah tha let's provisionally let's go for a touch screen one with several submenus um for possible extra stuff that one basically put the channel and the on and off switch on the touch screen. Um do we have Mm wait a minute it occurs to me that if we have a touch screen people are going to have to recharge their remote controls. Yet at the same time that might help for this whole complaint of it being lost.

True.'Cause it would have a docking base?

Mm-hmm. But then again that costs as well.

Yeah.

Hmm.

So these new lithium batteries they last twenty years even with the touch screen?

Do they? Okay.

Those new ones.

Can we afford to include one of those?

Can we afford that?

And will somebody buy it if we don't?.

Well I I don't think yeah I can't see anybody buying a lap a remote control that they have to plug in so we'd have to see some kind of new battery technology.

Right.

Okay so let's go with a um touch screen with um some kind of you know with with some kind of cutting edge battery technology.

For twelve Euros?.

Yeah well hey you know well it's it's worth looking into, if not we can always default to just doing a a well presented plastic simple

It is. Fair enough.

you know so you know. Well yeah I mean

The basics.

you can put the we could I I dunno I mean I suppose we could put the the basics on the centre easiest you know, you know people know the channel and volume function make them large and easy to get at and then the the other the other bits and bobs you know go through menu um w we'll do the aesthetics.

Mm.

Okay so we'll touch screen and the battery, focus on um uh presentation.

'Kay.

Um it's th uh with this voice recognition option as well um just as for the simple functions the um the on off, channels, volume, um

Right.

and um a small paging function. Even if you can't do voice recognition for the paging you know just some kind of simple button that's just a I guess another infrared signal to the remote control and while to emit some kind of paging. Just a beep.

Okay.

Um right so any comments? Thoughts before we break into go into the next round of individual work on this.

Since we're doing uh touch screen, do we wanna look into the possibility of people being able to input different types of skins for the you know the actual interface part of it and things like that? Or is it just gonna be one touch screen for everybody.

Be interesting.

Mm.

What what would be on that touch screen?'Cause you said earlier that we have to think about company colours and um

And oh.

logo or something or motto, I can't remember exactly what you said.

Yeah the the fashion

We put fashion into electronics.

do. Yeah.

W it's my understanding that if you were going to do a skin you'd need to have some way for people to download or import skins into the remote control.

Right, and then you're dealing with ports and cords and.

Yeah I think perhaps.

'S too much.

good idea but yeah

Yeah.

I think that that one m might just be um and they just yeah I think that one might just be out of the range for this particular.

For now.

a P_D_A_ would they would makes a lot of sense for a P_D_A_'cause you're gonna be using it to connect up to things anyway but I dunno, what do you guys think?

Think we just need to come up with a nice black and red interface on the touch screen.

Yeah. Nice.

That'd be okay.

Yeah. Uh I I'm I'm in agreement with that, I'm wondering how we're gonna get uh we put fashion into electronics onto this device.

Um. Well but if we're gonna use a touch screen where it's gonna come on like on your cell phone it'll have your your carrier provider name come up first like while it's loading

Hmm.

and then it goes away, perhaps it could be like a temporary.

Mm.

Comes on every time you turn it on and then that's it'cause it is a bit much to have it like engraved on the back or something I think.

Mm. True.

Yeah. Yeah.

I'm hoping for a subliminal maybe half a millisecond as it turns on..

Yeah.

Y.

Yeah. Yeah I know I d it seems like it would suffice to have just the R_R_ on there.

Mm-hmm.

Jus

Yeah you would think.

But apparently not.

But.

So.

People aren't gonna want their remote to boot up and to see flashing things come on.

If it comes from above.

They just want it to be on and ready to go.

Yeah.

Yeah..

Well fair enough. Um and yeah that would help the battery life too and if it the remote they do have to press a button for the remote to turn on. But then again who wants to turn on a remote control. Kind of if i

Well all you have to do is touch the screen and it automatically goes on.

Oh

Mm.

to wake up okay or go into like a dormant mode.

Yep. Goes into a sleep mode.

Okay. Oh yeah I like that I like the idea of um putting the logo in the boot up screen, nice. Um. Um cool so any last things before we break? Alright. Fair enough. Sounds good.

We're good?

I'm gonna save th a copy of this in case you guys need any reminders. I'm gonna save a copy of this and the minutes that I'll do it in a second and put them in the shared folder for later reference.

I've put my files in the shared folder as well.

Brilliant.

Yeah.

That's fab guys. Cool.

Question: What did Project Manager say in the meeting?

Response:
Here are the key topics discussed by the Project Manager in the meeting:
* The design specifications for the new television remote control were shared with the team.
* The company colors (red and black) and the slogan "We put fashion in Electronics" need to be included in the design.
* The remote should only work with televisions, and it should not have teletext capabilities.

Question: What was said about the user interface preferences?

Response:
Here are some of the user interface preferences discussed during the meeting:
* Use a high-grade recycled plastic material for the casing.
* The remote should be designed with fewer buttons and functions.
* It should have a simple, user-friendly display.
* The color scheme should be appealing and modern.

Question: What was User Interface Developer's opinion on the technical functions design of the device?

Response:
User Interface Developer shared these points on the technical functions design:
* A complicated remote with many functions is not generally preferred by users.
* Instead of having many buttons, a simple, user-friendly display with fewer functions would be ideal.

Question: What was their idea for the remote's paging system?

Response: The Industrial Designer suggested that a paging function could be combined with the voice recognition technology. The remote could have a simple command like "locate", which would make it beep and easier to find.

Question: Besides the paging function, what other features were discussed to make the remote control easier to use?

Response:
During the meeting, there was a discussion about additional features that could enhance the usability of the remote:
* A built-in locator device could be included, enabling users to locate the remote easily if it gets lost.
* Voice recognition capabilities could be implemented, allowing users to control basic functions through voice commands.

Question: What did Project Manager recommend to do when discussing the company logo and slogan on the remote?

Response: Project Manager suggested displaying the company logo and slogan during the boot-up screen of the remote. This would ensure visibility while minimizing visual clutter during regular use.

Question: What did Industrial Designer say about materials suitable for the remote's construction?

Response: Industrial Designer suggested that using primarily plastic construction for the remote would be a good idea. They expressed concerns about using too much metal due to potential radio signal interference, false signals, and safety issues related to the remote catching fire.

Question: What did Marketing say in the meeting about face plates?

Response: I did not find any information regarding face plates in the provided meeting.

Question: So they decided to use plastic for the remote?

Response: Yes, the Industrial Designer suggested using primarily plastic construction for the remote due to concerns about metal causing interference, false signals, and potential safety hazards. The team seemed to agree with this suggestion.