OK.

Oh, I don't.

I think I'm zero.

Wow! Unprecedented.

Hello, hello, hello, hello.

Ah

Wh - what causes the crash?

Did you fix something?

Hello.

Five, five.

Hello, hello.

Oh, maybe it's the turning turning off and turning on of the mike, right?

Uh, you think that's you? Oh.

Aaa - aaa - aaa.

Yeah, OK, mine's working.

OK. That's me.

OK. OK. So, um I guess we are um gonna do the digits at the end. Uh

Channel channel three, yeah.

Channel two.

OK.

Mmm, channel five? Doesn't work?

Yeah, that's the mike number there, uh Uh, mike number five, and channel channel four.

Two.

Is it written on her sheet, I believe.

No? Ah,

Mike four.

Watch this.

era el cuatro.

Yep, that's me.

Yeah.

But, channel

Yeah yeah yeah.

This is you.

OK. I saw that. Ah yeah, it's OK.

Yeah. And I'm channel uh two I think,

Ooo.

or channel.

I think I'm channel two.

Oh, I'm channel must be channel one. Channel one?

Channel I decided to talk about that.

Yes, OK. OK. So uh I also copied uh the results that we all got in the mail I think from uh from OGI and we'll go go through them also. So where are we on on uh our runs?

Uh so. uh We So As I was already said, we we mainly focused on uh four kind of features.

Excuse me.

The PLP, the PLP with JRASTA, the MSG, and the MFCC from the baseline Aurora.

Mm - hmm.

Uh, and we focused for the the test part on the English and the Italian. Um. We've trained uh several neural networks on so on the TI - digits English and on the Italian data and also on the broad uh English uh French and uh Spanish databases. Mmm, so there's our result tables here, for the tandem approach, and um, actually what we we @ @ observed is that if the network is trained on the task data it works pretty well.

OK. Our our uh There's a We're pausing for a photo.

Chicken on the grill. Try that corner.

How about over th from the front of the room?

Yeah, it's longer.

We're pausing for a photo opportunity here. Uh. Uh. So.

Oh wait wait wait wait wait. Wait.

Get out of the Yeah.

Hold on. Hold on.

OK.

Let me give you a black screen.

He's facing this way. What? OK, this this would be a good section for our silence detection.

OK.

Mm - hmm.

Um Oh.

Musical chairs everybody!

OK. So um, you were saying about the training data Yeah.

Yeah, so if the network is trained on the task data um tandem works pretty well. And uh actually we have uh, results are similar Only on,

Do you mean if it's trained only on On data from just that task,

yeah.

that language?

Just that task. But actually we didn't train network on uh both types of data I mean uh phonetically ba phonetically balanced uh data and task data.

Mmm.

We only did either task task data or uh broad data.

Mm - hmm.

Um Yeah. So,

So how I mean clearly it's gonna be good then

So what's th

but the question is how much worse is it if you have broad data? I mean, my assump From what I saw from the earlier results, uh I guess last week, was that um, if you trained on one language and tested on another, say, that the results were were relatively poor.

Mmm. Yeah.

But but the question is if you train on one language but you have a broad coverage and then test in another, does that is that improve things i c in comparison?

If we use the same language?

No, no, no. Different lang So um If you train on TI - digits and test on Italian digits, you do poorly, let's say.

Mm - hmm.

I don't have the numbers in front of me,

But Yeah but I did not uh do that.

so I'm just imagining. E So, you didn't train on TIMIT and test on on Italian digits, say?

We No, we did four four kind of of testing, actually. The first testing is with task data So, with nets trained on task data. So for Italian on the Italian speech @ @. The second test is trained on a single language um with broad database, but the same language as the t task data.

OK.

But for Italian we choose Spanish which we assume is close to Italian. The third test is by using, um the three language database

W which in.

and the fourth is

It has three languages. That's including the w the the.

This includes.

the one that it's.

Yeah.

In

But not digits. I mean it's.

The three languages is not digits,

Right.

it's the broad data. OK.

Yeah And the fourth test is uh excluding from these three languages the language that is the task language.

Oh, OK, yeah, so, that is what I wanted to know.

Yeah.

I just wasn't saying it very well, I guess.

Uh, yeah. So um for uh TI - digits for ins example uh when we go from TI - digits training to TIMIT training uh we lose uh around ten percent, uh. The error rate increase u of of of ten percent, relative.

Relative. Right.

So this is not so bad. And then when we jump to the multilingual data it's uh it become worse and, well Around uh, let's say, twenty perc twenty percent further.

Ab - about how much?

So. Yeah.

Twenty percent further?

Twenty to to thirty percent further. Yeah.

And so, remind me, the multilingual stuff is just the broad data. Right? It's not the digits.

Yeah.

So it's the combination of two things there. It's removing the task specific training and it's adding other languages.

Yeah. Yeah.

OK.

But the first step is al already removing the task s specific from from.

Already, right right right.

So.

So they were sort of building here?

And we lose.

OK?

Yeah. Uh So, basically when it's trained on the the multilingual broad data um or number so, the the ratio of our error rates uh with the baseline error rate is around uh one point one.

Yes. And it's something like one point three of of the uh.

So.

I i if you compare everything to the first case at the baseline, you get something like one point one for the for the using the same language but a different task, and something like one point three for three three languages broad stuff.

No no no. Uh same language we are at uh for at English at O point eight. So it improves, compared to the baseline. But So. Le - let me.

I I I'm sorry.

Tas - task data

I I I meant something different by baseline

we are u Yeah.

So let me let me Um, so, um.

Mmm.

OK, fine. Let's let's use the conventional meaning of baseline.

Hmm.

I I By baseline here I meant uh using the task specific data.

Oh yeah, the f Yeah, OK.

But uh uh, because that's what you were just doing with this ten percent.

Yeah.

So I was just I just trying to understand that.

Yeah. Sure.

So if we call a factor of w just one, just normalized to one, the word error rate that you have for using TI - digits as as training and TI - digits as test,

Mmm.

uh different words, I'm sure,

Mm - hmm.

but but uh, uh the same task and so on.

Mm - hmm.

If we call that " one ", then what you're saying is that the word error rate for the same language but using uh different training data than you're testing on, say TIMIT and so forth, it's one point one.

Mm - hmm. Yeah, it's around one point one.

Right. And if it's.

Yeah.

you do go to three languages including the English, it's something like one point three. That's what you were just saying, I think.

Ye Uh, more actually.

One point four?

If I Yeah.

So, it's an additional thirty percent.

What would you say? Around one point four

OK.

yeah.

And if you exclude English, from this combination, what's that?

If we exclude English, um there is not much difference with the data with English.

Aha!

So. Yeah.

That's interesting. That's interesting. Do you see? Because Uh,

Uh.

so No, that that's important. So what what it's saying here is just that " yes, there is a reduction in performance, when you don't um have the s when you don't have um

Task data.

Wait a minute, th th the.

Hmm.

No, actually it's interesting. So it's So when you go to a different task, there's actually not so different. It's when you went to these So what's the difference between two and three? Between the one point one case and the one point four case? I'm confused.

It's multilingual.

Yeah. The only difference it's is that it's multilingual Um

Cuz in both in both both of those cases, you don't have the same task.

Yeah. Yeah sure.

So is is the training data for the for this one point four case does it include the training data for the one point one case?

Uh yeah.

Yeah, a fraction of it.

A part of it, yeah.

How m how much bigger is it?

Um It's two times,

Yeah, um.

actually? Yeah. Um. The English data No, the multilingual databases are two times the broad English data. We just wanted to keep this, w well, not too huge. So.

So it's two times, but it includes the but it includes the broad English data.

I think so. Do you Uh, Yeah.

And the broad English data is what you got this one point one with. So that's TIMIT basically right?

Yeah.

Mm - hmm.

So it's band - limited TIMIT. This is all eight kilohertz sampling.

Mm - hmm.

Mm - hmm.

Yeah.

Downs Right.

So you have band - limited TIMIT, gave you uh almost as good as a result as using TI - digits on a TI - digits test. OK?

Hmm?

Um and um But, when you add in more training data but keep the neural net the same size, it um performs worse on the TI - digits. OK, now all of this is This is noisy TI - digits, I assume? Both training and test?

Yeah. OK. Um OK. Well. We we we may just need to uh So I mean it's interesting that h going to a different different task didn't seem to hurt us that much, and going to a different language um It doesn't seem to matter The difference between three and four is not particularly great, so that means that whether you have the language in or not is not such a big deal.

Mmm.

It sounds like um uh we may need to have more of uh things that are similar to a target language or I mean. You have the same number of parameters in the neural net, you haven't increased the size of the neural net, and maybe there's just just not enough complexity to it to represent the variab increased variability in the in the training set. That that could be. Um So, what about So these are results with uh th that you're describing now, that they are pretty similar for the different features or or uh.

Uh, let me check. Uh.

Yeah.

So. This was for the PLP,

Yeah.

Um. The Yeah. For the PLP with JRASTA the the we This is quite the same tendency, with a slight increase of the error rate, uh if we go to to TIMIT. And then it's it gets worse with the multilingual. Um. Yeah. There there is a difference actually with b between PLP and JRASTA is that JRASTA seems to perform better with the highly mismatched condition but slightly slightly worse for the well matched condition. Mmm.

I have a suggestion, actually, even though it'll delay us slightly, would would you mind running into the other room and making copies of this? Cuz we're all sort of If we c if we could look at it, while we're talking, I think it'd be

Yeah, yeah. OK.

uh Uh, I'll I'll sing a song or dance or something while you do it, too.

So um.

Alright.

Go ahead. Ah, while you're gone I'll ask s some of my questions.

Yeah.

Um.

Yeah. Uh, this way and just slightly to the left, yeah.

The um What was Was this number forty or It was roughly the same as this one, he said? When you had the two language versus the three language?

Um. That's what he was saying.

That's where he removed English,

Yeah.

right?

Right.

It sometimes, actually, depends on what features you're using.

Yeah. But but i it sounds like.

Um, but He Mm - hmm.

I mean. That's interesting because it it seems like what it's saying is not so much that you got hurt uh because you uh didn't have so much representation of English, because in the other case you don't get hurt any more, at least when it seemed like uh it it might simply be a case that you have something that is just much more diverse,

Mm - hmm.

but you have the same number of parameters representing it.

Mm - hmm. I wonder were um all three of these nets using the same output? This multi - language uh labelling?

He was using uh sixty - four phonemes from SAMPA.

OK, OK.

Yeah.

So this would From this you would say, " well, it doesn't really matter if we put Finnish into the training of the neural net, if there's gonna be, you know, Finnish in the test data. " Right?

Well, it's it sounds I mean, we have to be careful, cuz we haven't gotten a good result yet.

Yeah.

And comparing different bad results can be tricky.

Hmm.

But I I I I think it does suggest that it's not so much uh uh cross language as cross type of speech.

Mm - hmm.

It's it's um But we did Oh yeah, the other thing I was asking him, though, is that I think that in the case Yeah, you you do have to be careful because of com compounded results. I think we got some earlier results in which you trained on one language and tested on another and you didn't have three, but you just had one language. So you trained on one type of digits and tested on another. Didn - Wasn't there something of that? Where you, say, trained on Spanish and tested on on TI - digits, or the other way around? Something like that?

No.

I thought there was something like that, that he showed me last week. We'll have to wait till we get.

Yeah, that would be interesting.

Um, This may have been what I was asking before, Stephane, but but, um, wasn't there something that you did, where you trained on one language and tested on another? I mean no no mixture but just.

I'll get it for you.

Uh, no, no.

We've never just trained on one lang

Training on a single language, you mean, and testing on the other one?

Yeah.

Uh, no.

Not yet.

So the only task that's similar to this is the training on two languages, and  that.

But we've done a bunch of things where we just trained on one language. Right? I mean, you haven't you haven't done all your tests on multiple languages.

Uh, No. Either thi this is test with uh the same language but from the broad data, or it's test with uh different languages also from the broad data, excluding the So, it's it's three or three and four.

The early experiment that.

Did you do different languages from digits?

Uh. No. You mean training digits on one language and using the net to recognize on the other?

Digits on another language?

No.

See, I thought you showed me something like that last week. You had a you had a little.

Uh, No, I don't think so.

Um What.

These numbers are uh ratio to baseline?

So, I mean wha what's the.

So.

This this chart this table that we're looking at is um, show is all testing for TI - digits, or?

Bigger is worse.

So you have uh basically two uh parts.

This is error rate, I think.

Ratio.

No. No.

The upper part is for TI - digits

Yeah, yeah, yeah.

and it's divided in three rows of four four rows each.

Mm - hmm.

Yeah.

And the first four rows is well - matched, then the s the second group of four rows is mismatched, and finally highly mismatched. And then the lower part is for Italian and it's the same the same thing.

So, so the upper part is training TI - digits?

So. It's it's the HTK results, I mean. So it's HTK training testings with different kind of features

Ah.

and what appears in the uh left column is the networks that are used for doing this.

Hmm.

So. Uh Yeah.

Well, What was is that i What was it that you had done last week when you showed Do you remember? Wh - when you showed me the your table last week?

It - It was part of these results. Mmm. Mmm.

So where is the baseline for the TI - digits located in here?

You mean the HTK Aurora baseline?

Yeah.

It's uh the one hundred number. It's, well, all these numbers are the ratio with respect to the baseline.

Ah! Ah, OK, OK.

So this is word word error rate, so a high number is bad.

Yeah, this is a word error rate ratio.

Yeah.

OK, I see.

Yeah. So, seventy point two means that we reduced the error rate uh by thirty thirty percent.

OK, OK, gotcha.

So.

OK, so if we take

Hmm.

uh um let's see PLP uh with on - line normalization and delta - del so that's this thing you have circled here in the second column,

Yeah.

um and " multi - English " refers to what?

To TIMIT. Mmm. Then you have uh MF, MS and ME which are for French, Spanish and English. And, yeah. Actually I I uh forgot to say that the multilingual net are trained on uh features without the s derivatives uh but with increased frame numbers. Mmm. And we can we can see on the first line of the table that it it it's slightly slightly worse when we don't use delta but it's not not that much.

Right. So w w So, I'm sorry. I missed that. What's MF, MS and ME?

Multi - French, Multi - Spanish

So. Multi - French, Multi - Spanish, and Multi - English.

Uh OK. So, it's uh broader vocabulary. Then And.

Yeah.

OK so I think what I'm what I saw in your smaller chart that I was thinking of was was there were some numbers I saw, I think, that included these multiple languages and it and I was seeing that it got worse. I I think that was all it was. You had some very limited results that at that point

Yeah.

which showed having in these these other languages. In fact it might have been just this last category, having two languages broad that were where where English was removed. So that was cross language and the and the result was quite poor. What I we hadn't seen yet was that if you added in the English, it's still poor.

Yeah.

Uh Um now, what's the noise condition um of the training data.

Still poor.

Well, I think this is what you were explaining. The noise condition is the same It's the same uh Aurora noises uh, in all these cases for the training.

Yeah. Yeah.

So there's not a statistical sta a strong st statistically different noise characteristic between uh the training and test

No these are the s s s same noises,

and yet we're seeing some kind of effect.

yeah. At least at least for the first for the well - matched,

Well matched condition.

Right.

yeah.

So there's some kind of a a an effect from having these uh this broader coverage um Now I guess what we should try doing with this is try testing these on u this same sort of thing on you probably must have this lined up to do. To try the same t with the exact same training, do testing on the other languages.

Mmm.

On on um So. Um, oh I well, wait a minute. You have this here, for the Italian. That's right. OK, so, So.

Yeah. Yeah, so for the Italian the results are uh stranger um Mmm. So what appears is that perhaps Spanish is not very close to Italian because uh, well, when using the the network trained only on Spanish it's the error rate is almost uh twice the baseline error rate.

Mm - hmm.

Mmm. Uh.

Well, I mean, let's see. Is there any difference in So it's in the uh So you're saying that when you train on English and uh and and test on.

Yeah.

No, you don't have training on English testing.

There there is another difference, is that the noise the noises are different.

In in what?

Well, For for the Italian part I mean the uh the um networks are trained with noise from Aurora TI - digits,

Aurora - two.

mmm.

And the noise is different in th

Yeah. And perhaps the noise are quite different from the noises in the speech that Italian.

Do we have any um test sets uh in any other language that um have the same noise as in the Aurora?

And.

Mmm, no.

No.

Can I ask something real quick? In in the upper part in the English stuff, it looks like the very best number is sixty point nine? and that's in the uh the third section in the upper part under PLP JRASTA, sort of the middle column?

Yeah.

I is that a noisy condition?

Yeah.

So that's matched training? Is that what that is?

It's no, the third part, so it's uh highly mismatched. So. Training and test noise are different.

So why do you get your best number in Wouldn't you get your best number in the clean case?

Well, it's relative to the um baseline mismatching

Yeah.

Ah,

Yeah. Yeah.

OK so these are not OK, alright, I see.

Yeah.

OK. And then so, in the in the um in the non - mismatched clean case, your best one was under MFCC? That sixty - one point four?

Yeah. But it's not a clean case. It's a noisy case but uh training and test noises are the same.

Oh! So this upper third?

So Yeah.

Uh that's still noisy?

Yeah.

Ah, OK.

So it's always noisy basically,

Mm - hmm.

and, well, the.

I see.

Mmm.

OK? Um So uh, I think this will take some looking at, thinking about. But, what is uh what is currently running, that's uh, i that just filling in the holes here or or?  pretty much?

Uh, no we don't plan to fill the holes

OK.

but actually there is something important, is that um we made a lot of assumption concerning the on - line normalization and we just noticed uh recently that uh the approach that we were using was not uh leading to very good results when we used the straight features to HTK. Um Mmm. So basically d if you look at the at the left of the table, the first uh row, with eighty - six, one hundred, and forty - three and seventy - five, these are the results we obtained for Italian uh with straight mmm, PLP features using on - line normalization.

Mm - hmm.

Mmm. And the, mmm what's in the table, just at the left of the PLP twelve on - line normalization column, so, the numbers seventy - nine, fifty - four and uh forty - two are the results obtained by uh Pratibha with uh his on - line normalization uh her on - line normalization approach.

Where is that? seventy - nine, fifty

Uh, it's just sort of sitting right on the uh the column line.

So.

Fifty - one? This.

Oh I see, OK.

Uh. Yeah.

Just uh Yeah. So these are the results of OGI with on - line normalization and straight features to HTK. And the previous result, eighty - six and so on, are with our features straight to HTK.

Yes. Yes.

So what we see that is there is that um uh the way we were doing this was not correct, but still the networks are very good. When we use the networks our number are better that uh Pratibha results.

We improve.

So, do you know what was wrong with the on - line normalization, or?

Yeah. There were diff there were different things and basically, the first thing is the mmm, alpha uh value. So, the recursion uh part. um, I used point five percent, which was the default value in the in the programs here. And Pratibha used five percent.

Uh

So it adapts more quickly

Yes. Yeah.

Um, but, yeah. I assume that this was not important because uh previous results from from Dan and show that basically the both both values g give the same same uh results. It was true on uh TI - digits but it's not true on Italian.

Mm - hmm.

Uh, second thing is the initialization of the stuff. Actually, uh what we were doing is to start the recursion from the beginning of the utterance. And using initial values that are the global mean and variances measured across the whole database.

Right. Right.

And Pratibha did something different is that he uh she initialed the um values of the mean and variance by computing this on the twenty - five first frames of each utterance. Mmm. There were other minor differences, the fact that she used fifteen dissities instead s instead of thirteen, and that she used C - zero instead of log energy. Uh, but the main differences concerns the recursion. So. Uh, I changed the code uh and now we have a baseline that's similar to the OGI baseline.

OK.

We It it's slightly uh different because I don't exactly initialize the same way she does. Actually I start, mmm, I don't wait to a fifteen twenty - five twenty - five frames before computing a mean and the variance to e to to start the recursion.

Mm - hmm.

Yeah.

I I use the on - line scheme and only start the re recursion after the twenty - five twenty - fifth frame. But, well it's similar. So uh I retrained the networks with these well, the the the networks are retaining with these new features.

Mm - hmm.

And, yeah.

OK.

So basically what I expect is that these numbers will a little bit go down but perhaps not not so much

Right.

because I think the neural networks learn perhaps to.

Right.

even if the features are not normalized. It it will learn how to normalize and.

OK, but I think that given the pressure of time we probably want to draw because of that especially, we wanna draw some conclusions from this, do some reductions in what we're looking at,

Yeah.

and make some strong decisions for what we're gonna do testing on before next week. So do you are you w did you have something going on, on the side, with uh multi - band or on on this,

Yeah I

or?

No, I we plan to start this uh so, act actually we have discussed uh @ @ um, these what we could do more as a as a research and and we were thinking perhaps that uh the way we use the tandem is not Uh, well, there is basically perhaps a flaw in the in the the stuff because we trained the networks If we trained the networks on the on a language and a t or a specific task,

Mm - hmm.

um, what we ask is to the network is to put the bound the decision boundaries somewhere in the space.

Mmm.

And uh mmm and ask the network to put one, at one side of the for for a particular phoneme at one side of the boundary decision boundary and one for another phoneme at the other side. And so there is kind of reduction of the information there that's not correct because if we change task and if the phonemes are not in the same context in the new task, obviously the decision boundaries are not should not be at the same place.

I di

But the way the feature gives The the way the network gives the features is that it reduce completely the it removes completely the information a lot of information from the the features by uh uh placing the decision boundaries at optimal places for one kind of data but this is not the case for another kind of data.

It's a trade - off,

So.

right? Any - anyway go ahead.

Yeah. So uh what we were thinking about is perhaps um one way to solve this problem is increase the number of outputs of the neural networks. Doing something like, um um phonemes within context and, well, basically context dependent phonemes.

Maybe. I mean, I I think you could make the same argument, it'd be just as legitimate, for hybrid systems as well. Right.

Yeah but, we know that.

And in fact, th things get better with context dependent versions. Right?

Ye - yeah but here it's something different. We want to have features

Yeah.

uh well, um.

Yeah, but it's still true that what you're doing is you're ignoring you're you're coming up with something to represent, whether it's a distribution, probability distribution or features, you're coming up with a set of variables that are representing uh, things that vary w over context.

Mm - hmm.

Uh, and you're putting it all together, ignoring the differences in context. That that's true for the hybrid system, it's true for a tandem system. So, for that reason, when you in in in a hybrid system, when you incorporate context one way or another, you do get better scores.

Yeah.

OK? But I it's it's a big deal to get that. I I'm I'm sort of And once you the other thing is that once you represent start representing more and more context it is uh much more um specific to a particular task in language. So um Uh, the the acoustics associated with uh a particular context, for instance you may have some kinds of contexts that will never occur in one language and will occur frequently in the other, so the qu the issue of getting enough training for a particular kind of context becomes harder. We already actually don't have a huge amount of training data um

Yeah, but mmm, I mean, the the way we we do it now is that we have a neural network and basically the net network is trained almost to give binary decisions.

Right.

And uh binary decisions about phonemes. Nnn Uh It's.

Almost. But I mean it it it does give a distribution.

Yeah.

It's and and it is true that if there's two phones that are very similar, that uh the i it may prefer one but it will give a reasonably high value to the other, too.

Yeah. Yeah, sure but uh So basically it's almost binary decisions and um the idea of using more classes is to get something that's less binary decisions.

Oh no, but it would still be even more of a binary decision. It it'd be even more of one. Because then you would say that in that this phone in this context is a one, but the same phone in a slightly different context is a zero.

But yeah, but.

That would be even even more distinct of a binary decision. I actually would have thought you'd wanna go the other way and have fewer classes.

Yeah, but if.

Uh, I mean for instance, the the thing I was arguing for before, but again which I don't think we have time to try, is something in which you would modify the code so you could train to have several outputs on and use articulatory features

Mmm. Mm - hmm.

cuz then that would that would go that would be much broader and cover many different situations. But if you go to very very fine categories, it's very binary.

Mmm. Yeah, but I think Yeah, perhaps you're right, but you have more classes so you you have more information in your features. So, Um You have more information in the uh

Mm - hmm. True.

posteriors vector um which means that But still the information is relevant

Mm - hmm.

because it's it's information that helps to discriminate,

Mm - hmm.

if it's possible to be able to discriminate among the phonemes in context.

Well it's it's it's an interesting thought.

But the.

I mean we we could disagree about it at length

Mmm.

but the the real thing is if you're interested in it you'll probably try it

Mmm.

and and we'll see. But but what I'm more concerned with now, as an operational level, is uh, you know,

Mmm.

what do we do in four or five days? Uh, and so we have to be concerned with Are we gonna look at any combinations of things, you know once the nets get retrained so you have this problem out of it.

Mmm.

Um, are we going to look at multi - band? Are we gonna look at combinations of things? Uh, what questions are we gonna ask, uh now that, I mean, we should probably turn shortly to this O G I note. Um, how are we going to combine with what they've been focusing on? Uh, Uh we haven't been doing any of the L D A RASTA sort of thing.

Mm - hmm.

And they, although they don't talk about it in this note, um, there's um, the issue of the um Mu law business uh versus the logarithm, um, so.

Mm - hmm.

So what i what is going on right now? What's right you've got nets retraining, Are there is there are there any H T K trainings testings going on?

N

I I I'm trying the HTK with eh, PLP twelve on - line delta - delta and MSG filter together.

The combination, I see.

The combination, yeah. But I haven't result at this moment.

MSG and and PLP.

Yeah..

And is this with the revised on - line normalization?

Ye - Uh, with the old older,

Yeah.

Old one. So it's using all the nets for that

yeah.

but again we have the hope that it We have the hope that it maybe it's not making too much difference,

Yeah. But We can know soon.

but but

Maybe.

yeah.

I don't know.

Yeah.

Uh, OK.

Uh so there is this combination, yeah. Working on combination obviously.

Mm - hmm.

Um, I will start work on multi - band. And we plan to work also on the idea of using both features and net outputs.

Um. And we think that with this approach perhaps we could reduce the number of outputs of the neural network. Um, So, get simpler networks, because we still have the features. So we have um come up with um different kind of broad phonetic categories. And we have Basically we have three types of broad phonetic classes. Well, something using place of articulation which which leads to nine, I think, broad classes. Uh, another which is based on manner, which is is also something like nine classes. And then, something that combine both, and we have twenty f twenty - five?

Twenty - seven.

Twenty - seven broad classes. So like, uh, oh, I don't know, like back vowels, front vowels.

So what you do um I just wanna understand

Um For the moments we do not don't have nets,

so You have two net or three nets? Was this? How many how many nets do you have? No nets.

I mean, It's just Were we just changing the labels to retrain nets with fewer out outputs.

Begin to work in this. We are @ @.

Right. But but I didn't understand.

And then Mm - hmm.

Uh. the software currently just has uh a allows for I think, the one one hot output. So you're having multiple nets and combining them, or? Uh, how are you how are you coming up with If you say uh If you have a place characteristic and a manner characteristic, how do you.

It - It's the single net,

I think they have one output.

yeah.

Oh, it's just one net.

It's one net with um twenty - seven outputs

Yeah.

mm - hmm

if we have twenty - seven classes,

I see. I see, OK.

yeah. So it's Well, it's basically a standard net with fewer classes.

So you're sort of going the other way of what you were saying a bit ago instead of yeah.

Yeah, but I think Yeah. B b including the features, yeah.

But including the features.

Yeah.

I don't think this will work alone. I think it will get worse because Well, I believe the effect that of of too reducing too much the information is basically basically what happens

Uh - huh.

and.

But you think if you include that plus the other features,

but Yeah, because there is perhaps one important thing that the net brings, and OGI show showed that, is the distinction between sp speech and silence Because these nets are trained on well - controlled condition. I mean the labels are obtained on clean speech, and we add noise after. So this is one thing And But perhaps, something intermediary using also some broad classes could could bring so much more information. Uh.

So so again then we have these broad classes and well, somewhat broad. I mean, it's twenty - seven instead of sixty - four, basically. And you have the original features.

Yeah.

Which are PLP, or something.

Yeah.

And then uh, just to remind me, all of that goes into uh, that all of that is transformed by uh, uh, K - KL or something, or?

Mm - hmm. There will probably be,

Mu.

yeah, one single KL to transform everything

Right.

or uh,

No transform the PLP

per

and only transform the other I'm not sure.

Well no,

This is still something that

I think I see.

yeah, we don't know.

So there's a question of whether you would.

Two e @ @ it's one.

Yeah.

Right. Whether you would transform together or just one. Yeah. Might wanna try it both ways. But that's interesting. So that's something that you're you haven't trained yet but are preparing to train, and.

Yeah.

Yeah. Um Yeah, so I think Hynek will be here Monday.

Mmm.

Monday or Tuesday. So

Uh, yeah.

So I think, you know, we need to choose the choose the experiments carefully, so we can get uh key key questions answered uh before then

Mm - hmm.

and leave other ones aside even if it leaves incomplete tables someplace, uh uh, it's it's really time to time to choose.

Mm - hmm.

Um, let me pass this out, by the way. Um These are Did did did I interrupt you?

Yeah, I have one.

Were there other things that you wanted to.

Uh, no. I don't think so.

Yeah, I have one.

Oh, thanks.

Ah! OK. OK, we have lots of them.

We have one..

OK, so um, Something I asked So they're they're doing the the VAD I guess they mean voice activity detection So again, it's the silence So they've just trained up a net which has two outputs, I believe. Um I asked uh Hynek whether I haven't talked to Sunil I asked Hynek whether they compared that to just taking the nets we already had and summing up the probabilities.

Mm - hmm.

Uh. To get the speech voice activity detection, or else just using the silence, if there's only one silence output. Um And, he didn't think they had, um. But on the other hand, maybe they can get by with a smaller net and maybe sometimes you don't run the other, maybe there's a computational advantage to having a separate net, anyway.

Mm - hmm.

So um Their uh the results look pretty good. Um, I mean, not uniformly.

Yeah.

I mean, there's a an example or two that you can find, where it made it slightly worse, but uh in in all but a couple examples.

Mmm.

Uh.

But they have a question of the result. Um how are trained the the LDA filter? How obtained the LDA filter?

Mmm.

I I'm sorry. I don't understand your question.

Yes, um the LDA filter needs some training set to obtain the filter. Maybe I don't know exactly how they are obtained.

It's on training.

Training, with the training test of each You understand me?

No.

Yeah, uh for example, LDA filter need a set of a set of training to obtain the filter.

Yes.

And maybe for the Italian, for the TD TE on for Finnish, these filter are are obtained with their own training set.

Yes, I don't know. That's that's so that's a that's a very good question, then now that it I understand it. It's " yeah, where does the LDA come from? " In the In earlier experiments, they had taken LDA from a completely different database, right?

Yeah. Yeah, because maybe it the same situation that the neural network training with their own

Mmm.

set.

So that's a good question. Where does it come from? Yeah, I don't know. Um, but uh to tell you the truth, I wasn't actually looking at the LDA so much when I I was looking at it I was mostly thinking about the the VAD. And um, it ap it ap Oh what does what does ASP? Oh that's.

The features, yeah. Yeah.

I don't understand also

It says " baseline ASP ".

what is what is the difference between ASP and uh baseline over?

ASP.

Yeah, I don't know.

This is.

Anybody know any.

Oh. There it is.

Um Cuz there's " baseline Aurora " above it.

Mm - hmm.

And it's This is mostly better than baseline, although in some cases it's a little worse, in a couple cases.

Well, it says baseline ASP is twenty - three mill minus thirteen.

Yeah.

Yeah, it says what it is. But I don't how that's different from.

From the baseline.  OK.

I think this was I think this is the same point we were at when when we were up in Oregon.

Yeah.

I think I think it's the C - zero using C - zero instead of log energy.

Ah, OK, mm - hmm.

Yeah, it's this.

Oh. OK.

yeah.

It should be that, yeah.

They s they say in here that the VAD is not used as an additional feature.

Shouldn't it be.

Because.

Does does anybody know how they're using it?

Yeah. So so what they're doing here is, i

Yeah.

if you look down at the block diagram, um, they estimate they get a they get an estimate of whether it's speech or silence,

But that.

and then they have a median filter of it.

Mm - hmm.

And so um, basically they're trying to find stretches. The median filter is enforcing a i it having some continuity.

Mm - hmm.

You find stretches where the combination of the frame wise VAD and the the median filter say that there's a stretch of silence. And then it's going through and just throwing the data away.

Hmm.

Right? So um.

So it's it's I don't understand. You mean it's throwing out frames? Before.

It's throwing out chunks of frames, yeah. There's the the median filter is enforcing that it's not gonna be single cases of frames, or isolated frames.

Yeah.

So it's throwing out frames and the thing is um, what I don't understand is how they're doing this with H T

Yeah, that's what I was just gonna ask.

This is.

How can you just throw out frames?

Yeah. Well, you you can,

i

right? I mean y you you.

Yeah.

it stretches again. For single frames I think it would be pretty hard.

Yeah.

But if you say speech starts here, speech ends there.

Mm - hmm.

Right?

Huh.

Yeah. Yeah, you can basically remove the the frames from the feature feature files.

Yeah. Yeah, so I mean in the i i in the in the decoding, you're saying that we're gonna decode from here to here.

I t

Mm - hmm.

I think they're they're they're treating it, you know, like uh well, it's not isolated word, but but connected, you know, the the.

In the text they say that this this is a tentative block diagram of a possible configuration we could think of. So that sort of sounds like they're not doing that yet.

Well. No they they have numbers though, right? So I think they're they're doing something like that. I think that they're they're I think what I mean by tha that is they're trying to come up with a block diagram that's plausible for the standard. In other words, it's uh I mean from the point of view of of uh reducing the number of bits you have to transmit it's not a bad idea to detect silence anyway.

Yeah. Yeah. I'm just wondering what exactly did they do up in this table if it wasn't this.

Um. But it's the thing is it's that that that's that's I I Certainly it would be tricky about it intrans in transmitting voice, uh uh for listening to, is that these kinds of things uh cut speech off a lot.

Mm - hmm.

Right? And so um

Plus it's gonna introduce delays.

It does introduce delays but they're claiming that it's it's within the the boundaries of it.

Mmm.

And the LDA introduces delays, and b what he's suggesting this here is a parallel path so that it doesn't introduce uh, any more delay. I it introduces two hundred milliseconds of delay but at the same time the LDA down here I don't know Wh what's the difference between TLDA and SLDA?

Temporal and spectral.

Ah, thank you.

Temporal LDA.

Yeah, you would know that.

Yeah

So um. The temporal LDA does in fact include the same so that I think he well, by by saying this is a b a tentative block di diagram I think means if you construct it this way, this this delay would work in that way

Ah.

and then it'd be OK. They they clearly did actually remove silent sections in order because they got these word error rate results. So um I think that it's it's nice to do that in this because in fact, it's gonna give a better word error result and therefore will help within an evaluation. Whereas to whether this would actually be in a final standard, I don't know. Um. Uh, as you know, part of the problem with evaluation right now is that the word models are pretty bad and nobody wants has has approached improving them. So it's possible that a lot of the problems with so many insertions and so forth would go away if they were better word models to begin with. So this might just be a temporary thing. But But, on the other hand, and maybe maybe it's a decent idea. So um The question we're gonna wanna go through next week when Hynek shows up I guess is given that we've been if you look at what we've been trying, we're uh looking at uh, by then I guess, combinations of features and multi - band Uh, and we've been looking at cross - language, cross task issues. And they've been not so much looking at the cross task uh multiple language issues. But they've been looking at uh at these issues. At the on - line normalization and the uh voice activity detection. And I guess when he comes here we're gonna have to start deciding about um what do we choose from what we've looked at to um blend with some group of things in what they've looked at And once we choose that, how do we split up the effort? Uh, because we still have even once we choose, we've still got uh another month or so, I mean there's holidays in the way, but but uh I think the evaluation data comes January thirty - first so there's still a fair amount of time to do things together it's just that they probably should be somewhat more coherent between the two sites in that that amount of time.

When they removed the silence frames, did they insert some kind of a marker so that the recognizer knows it's knows when it's time to back trace or something?

Well, see they, I I think they're Um. I don't know the the specifics of how they're doing it. They're they're getting around the way the recognizer works because they're not allowed to um, change the scripts for the recognizer, I believe.

Oh, right. Maybe they're just inserting some nummy frames or something?

So. Uh. Uh, you know that's what I had thought. But I don't I don't think they are.

Hmm.

I mean that's sort of what the way I had imagined would happen is that on the other side, yeah you p put some low level noise or something. Probably don't want all zeros.

Hmm.

Most recognizers don't like zeros but but you know, put some epsilon in or some rand

Yeah.

sorry epsilon random variable in or something.

Some constant vector. I mean i w Or something.

Maybe not a constant but it doesn't, uh don't like to divide by the variance of that, but I mean it's

That's right. But something that what I mean is something that is very distinguishable from speech.

Mm - hmm.

So that the the silence model in HTK will always pick it up.

Yeah. So I I that's what I thought they would do. or else, uh uh maybe there is some indicator to tell it to start and stop, I don't know.

Hmm.

But whatever they did, I mean they have to play within the rules of this specific evaluation.

Yeah.

We c we can find out.

Cuz you gotta do something. Otherwise, if it's just a bunch of speech, stuck together.

No they're.

Yeah.

It would do badly

Yeah, right.

and it didn't so badly, right? So they did something.

Yeah, yeah.

Yeah. Uh. So, OK, So I think this brings me up to date a bit. It hopefully brings other people up to date a bit. And um Um I think Uh, I wanna look at these numbers off - line a little bit and think about it and and talk with everybody uh, outside of this meeting. Um, but uh No I mean it sounds like I mean there there there are the usual number of of little little problems and bugs and so forth but it sounds like they're getting ironed out. And now we're seem to be kind of in a position to actually uh, look at stuff and and and compare things. So I think that's that's pretty good. Um I don't know what the One of the things I wonder about, coming back to the first results you talked about, is is how much, uh things could be helped by more parameters. And uh And uh how many more parameters we can afford to have, in terms of the uh computational limits. Because anyway when we go to twice as much data and have the same number of parameters, particularly when it's twice as much data and it's quite diverse, um, I wonder if having twice as many parameters would help.

Mm - hmm.

Uh, just have a bigger hidden layer. Uh But I doubt it would help by forty per cent. But but uh

Yeah.

Just curious. How are we doing on the resources? Disk, and.

I think we're alright,

OK.

um, not much problems with that.

Computation?

It's OK.

We.

Well this table took uh more than five days to get back.

Yeah. Yeah, well.

But Yeah.

Are were you folks using Gin? That's a that just died, you know?

Mmm, no. You were using Gin  perhaps, yeah? No.

No.

No? Oh, that's good.

It just died.

OK. Yeah, we're gonna get a replacement server that'll be a faster server, actually.

Yes.

That'll be It's a seven hundred fifty megahertz uh SUN

Hmm.  Mm - hmm.

uh But it won't be installed for a little while.

Tonic.

U Go ahead.

Do we Do we have that big new IBM machine the, I think in th

We have the little tiny IBM machine that might someday grow up to be a big IBM machine. It's got s slots for eight, uh IBM was donating five, I think we only got two so far, processors. We had originally hoped we were getting eight hundred megahertz processors. They ended up being five fifty. So instead of having eight processors that were eight hundred megahertz, we ended up with two that are five hundred and fifty megahertz. And more are supposed to come soon and there's only a moderate amount of dat of memory. So I don't think anybody has been sufficiently excited by it to spend much time uh with it, but uh Hopefully, they'll get us some more parts, soon and Uh, yeah, I think that'll be once we get it populated, that'll be a nice machine. I mean we will ultimately get eight processors in there. And uh and uh a nice amount of memory. Uh so it'll be a pr pretty fast Linux machine.

And if we can do things on Linux, some of the machines we have going already, like Swede?

Mm - hmm.

Um It seems pretty fast.

Mm - hmm.

But I think Fudge is pretty fast too.

Yeah, I mean you can check with uh Dave Johnson. I mean, it it's I think the machine is just sitting there. And it does have two processors, you know and Somebody could do you know, uh, check out uh the multi - threading libraries. And I mean i it's possible that the I mean, I guess the prudent thing to do would be for somebody to do the work on on getting our code running on that machine with two processors even though there aren't five or eight. There's there's there's gonna be debugging hassles and then we'd be set for when we did have five or eight, to have it really be useful. But. Notice how I said somebody and turned my head your direction. That's one thing you don't get in these recordings. You don't get the don't get the visuals but.

I is it um mostly um the neural network trainings that are um slowing us down or the HTK runs that are slowing us down?

Uh, I think yes. Uh, Isn't that right? I mean I think you're you're sort of held up by both, right? If the if the neural net trainings were a hundred times faster you still wouldn't be anything running through these a hundred times faster because you'd be stuck by the HTK trainings,

Mmm.

right?

Yeah.

But if the HTK I mean I think they're both It sounded like they were roughly equal? Is that about right?

Yeah.

Yeah.

Because, um I think that'll be running Linux, and Sw - Swede and Fudge are already running Linux so, um I could try to get um the train the neural network trainings or the HTK stuff running under Linux, and to start with I'm wondering which one I should pick first.

Uh, probably the neural net cuz it's probably it it's it's um Well, I I don't know. They both HTK we use for um this Aurora stuff Um Um, I think It's not clear yet what we're gonna use for trainings uh Well, there's the trainings uh is it the training that takes the time, or the decoding? Uh, is it about equal between the two? For for Aurora?

For HTK?

For Yeah. For the Aurora?

Uh Training is longer.

OK.

Yeah.

OK. Well, I don't know how we can I don't know how to Do we have HTK source? Is that Yeah.

Mmm.

You would think that would fairly trivially the training would, anyway, th the testing uh I don't I don't think would parallelize all that well. But I think that you could certainly do d um, distributed, sort of Ah, no, it's the each individual sentence is pretty tricky to parallelize. But you could split up the sentences in a test set.

They have a they have a thing for doing that and th they have for awhile, in H T And you can parallelize the training.

Yeah?

And run it on several machines

Aha!

and it just basically keeps counts. And there's something a final thing that you run and it accumulates all the counts together.

I see.

Mmm.

I don't what their scripts are set up to do for the Aurora stuff, but.

Yeah.

Something that we haven't really settled on yet is other than this Aurora stuff, uh what do we do, large vocabulary training slash testing for uh tandem systems. Cuz we hadn't really done much with tandem systems for larger stuff. Cuz we had this one collaboration with CMU and we used SPHINX. Uh, we're also gonna be collaborating with SRI and we have their have theirs. Um So I don't know Um. So I I think the the advantage of going with the neural net thing is that we're gonna use the neural net trainings, no matter what, for a lot of the things we're doing,

OK.

whereas, w exactly which HMM Gaussian - mixture - based HMM thing we use is gonna depend uh So with that, maybe we should uh go to our digit recitation task. And, it's about eleven fifty. Canned. Uh, I can I can start over here. Great, uh, could you give Adam a call. Tell him to He's at two nine seven seven.

Oh.

OK. I think we can @ @ You know Herve's coming tomorrow, right? Herve will be giving a talk, yeah, talk at eleven. Did uh, did everybody sign these consent Er everybody Has everyone signed a consent form before, on previous meetings? You don't have to do it again each time Yes. microphones off

Question: What were the main conclusions and future tasks that were agreed upon at the end of the meeting?

Response:
* The participants agreed to focus on reducing the number of outputs from neural networks by choosing from the categories of broad phonetic classes.
* They decided to test combinations of features, e.g., PLP with MSG.
* They agreed to look at multi-band for future work.
* The participants discussed the potential benefits of using silence detection for reducing word error rate, but had questions about how it was being implemented.

Question: Did the participants agree to use only Silence Detection for the final standard?

Response: The meeting notes do not specify whether the participants agreed to use only Silence Detection for the final standard. The participants did discuss the potential benefits of using silence detection to reduce the number of insertions and word error rates, but they had questions about how it was being implemented.

Question: Yes/No: Was LDA used for feature transformation in the tandem system?

Response: Yes, LDA was used for feature transformation in the tandem system.

Question: Why was there a decision to use LDA for feature transformation in the tandem system?

Response: The meeting notes do not include the specific reasons for using LDA for feature transformation in the tandem system.

Question: Besides using Silence Detection, what other techniques or features were discussed for reducing word error rate?

Response:
* The participants discussed combining features, such as PLP and MSG, in order to reduce word error rate.
* They also discussed the possibility of using a different number of neural network outputs, as well as using broad phonetic classes.

Question: In summary, can you repeat some of the key topics that were discussed toward the end of the meeting?

Response:
Some of the key topics discussed toward the end of the meeting were:
* Participants agreed to focus on reducing the number of outputs from neural networks by using broad phonetic classes.
* They also discussed using silence detection for reducing word error rates.
* Lastly, the participants discussed potential future work, including using combinations of features and multi-band.


I'll wait until you're all um hooked up.

Oh good grief.

'Kay.

Okay..

Oh.

Put it on in that way.

Oops.

Thanks. Okay.

Mm.

Welcome back everybody, hope you've had fun.

After lunch. Yeah.

Right um this is our conceptual design meeting,

Mm-hmm.

um I think we're slightly ahead of ourselves from the last meeting,

Mm-hmm.

Yeah, yeah I was getting that impression as well.

I th I I think yeah um.

Mm.

This is where we talk about um properties, materials, user-interface and trend-watching.

Etcetera.

Mm-hmm.

So I think we've touched on a few things to do with that already, but we'll just go over it. Um the minutes from the last time. Um we had a couple of um changes in our plans in that we couldn't use teletext, it wasn't gonna be a control for everything um and that we had to incorporate the image of the company into it somehow. Um we have decided on a leaving out the voice recognition, we've decided on there being a flip design and um a different shape from what's normal. We were thinking a shell, but something along those lines, just a different shape from what's normal, um. You were going to look into the rechargeable batteries

Yeah.

and we were gonna think about the port and an alarm for getting it lost,

things like that,

Yeah.

um and our market was going to be young, business, kind of range.

'Kay.

Okay, so shall we start with the first presentation?

Shall I?

Yep.

Yes if you feel.

Okay. We just connect up.

Thank you. There we go. Okay um so we decided on our market and so this feedback from the marketing department is really about trend watching. Um the methods we used to decide on uh current trends and so on, market trends, were that we did our traditional uh our usual market research study uh with a hundred subjects and the the general feedback from them is that the most im uh w I'm sorry I'm slightly tongue-tied after lunch,

It's okay.

sorry Project Manager. Um we decided on the most important aspect i uh required in a remote control device and we'll come to that later. So that was one of the first things we did. Also with our company um being forerunners in uh putting fashion into technology, we also looked at a fashion update um using our consultants on fashion and design in Paris and Milan. Uh so the general findings from that was uh in the market trends the most important aspect for remote controls were people want a fancy look and feel, rather than the the current functional look and feel of remote controls. They also also want a remote control to be technologically innovative, and of course, as we predicted, that it should be easy to use. Now I should point out that the first of those findings, fancy look and feel, is the most important, is twice as important as the second, technologically innovative, which is in turn twice as important as being easy to use. So possibly that the feature that we put first is actually third is still important, but it's third in order of preference for the the subject group we looked at.

Yeah.

Okay, um, now the fashion update which relates to very personal preferences among our subject group, um we found from our um consultants in Paris and Milan who des attended all the design and fashion fairs there, that fruit and vegetables are going to be the theme for clothes, shoes and furniture. So should we be thinking of using something like that in our remote control design too? There also seems to be um a trend towards a spongy feel to materials, uh again in contrast to last year. So a lot of interesting feedback there, both from our market research departments and from our people in Paris and Milan. Be interesting to see what our design people make of that.

'Kay.

Okay thank you very much. Um

Yep.

let's start from the inside and work our way out.

Fine. It's okay with me.

Yeah, okay..

Unless anyone has any questions about that?

I don't think so, not yet.

Not yet?'Kay.

Um, yes, thank you.

That screwed in?.

Okay.

I hate those little things

Yeah.

especially if you do them on too tight and you can't get the leverage on them to undo them.

I know.

'Kay,.

Okay.

Okay. Um this is uh all about the design of the actual components and the availability of the components we have in stock at the moment. Okay, first uh the Into the method. The the main idea of the whole thing is just so you sh you just press buttons and it should activate things on the T_V_ and we discussed last meeting that it should be easy to find. Um also in this study I've looked into the availability of some of the materials,'cause some of the things that we looked at last time aren't un unfortunately aren't available.

'Kay..

Um, findings, I've got my nice little picture there. This is uh the chip called the T_A_ double one eight three five which is what's used in pretty much every remote control'cause it sends out standard signals, based on your input and um it's pretty much used for all all T_V_ remotes at the moment.

'Kay.

Um and then we we're loo looking into battery options.

There's um actually no rechargeable option available, so we I saw the um the standard double A_ and triple A_ which we thought were a bit bit too bulky at the moment, dynamo charging, I thought that that might be a bit silly to be to be honest,

Wa can you explain that?

people won't it's it's basically like wind-up radio.

Like a right, okay.

So you wind up your remote control before you use it.

How what kind of how l long can you get out of that,

It might.

I mean can you pick it up and then wind it for two minutes and then that's it for the night? Or.

You Yeah, yeah, oh yeah I presu you wouldn't have to wind it for very long, but I don't I don't think it's really sort of necessary when you th you think of the next two options, like the the solar charging,'cause most people have the light on in the room anyway so they could get when when you got T_V_.

That doesn't count though does it?

Does does light charge as as sunlight does?

I thought it was U_V_ like.

No.

Artificial light?

Is it? Alright i

Any, any.

Has to be solar.

Yep. Regarding those sizes, which one you think will be light because we we have to take into consideration size also, so maybe a standard triple A_ might take lesser space or and dynamo might take more space.

Yeah it would the d yeah the dynamo would take more space'cause you actually need a physical sort of handle to wind up.

I'm pretty sure that solar is from the sun.

Yeah, okay.

Uh, I don't think it counts electric lights

Mm. Artificial light, no.

no, but I mean not many you don't want to limit your market.

That's going to.

I mean people who live in basement flats there's not that many people, but there are people.

Uh.

I know,

But.

different parts of the world too, if we're if we're marketing internationally.

Yeah

Right.

and most people most people also watch T_V_ in the in the night anyway.

Night.

Mm, but then it would be charging through the day, I think the point is that it charges through the day and then you've got it charged for the evening.

But I I think I think the the next one's the best anyway.

Okay.

The the kinetic charging which is like you get it in r you get it in wrist watches

Yeah I've seen.

and you d you don't even notice it. I mean the amount the amount of probably moving around you'd be doing all the time would would charge it up, I don't think you'd ever need to actually physically start shaking it up to make it work.

And are these like uh what are the life of the kinetic battery, it like it runs for long time?

Um yeah it's it charges into um some form of it's a smaller cell which it charges into and uh the si the size sort of a watch a watch battery,

Mm-hmm.

'cause they use them quite frequently in watches.

Okay.

And that'll that would l would last for um well I do I d I'm not entirely sure how long it would last but I've never seen one run out.

We c

But then if you think about a watch, it's on your wrist so it's on your whole bod it's on your body the whole time

Yeah.

so you're walking around, you're doing things, it is moving a lot of the time. If you'd.

Yeah and.

I mean you switch the T_V_ on, then you put it on the side, then you pick it up to change it and then you put it on the side.

Mm-hmm.

Yeah but then again

Is it really gonna be enough?

I I think it is because if you think about it, the watch, although it's only a tiny amount it's it's um it's always moving for the whole day and they don't run out over night when you leave them on the side.

Okay.

And th for the same the same reason, you're only using it for a v incredibly short amount of time just to send the signal and then you're finished with it

So it's not the draw on it isn't.

and you put it.

Hmm.

no no I do I don't think the the draw on it would be.

Mm-hmm. Mm.

Okay.

Could I just ask referring back to solar charging, is that compatible with um standard batteries?

Mm.

I mean, could people put standard batteries in but with a s you know they could leave it in the sunlight for solar charging

Ye yeah I think I th uh g y you could have a dual um power thing

or the two things not compatible?

Like a dual kind of.

but the the thing with the solar cells is you would need to put them on the case and they because, if you lie on a calculator they they're qui they're quite big and they all they all look i identical.

Mm. So that affects the exterior design.

Solar would be slightly expensives.

Expensive as well. What kind of price are we looking at for.

They're they're expensive, they don't.

It's twelve point f

I presume the normal batteries are the cheapest?

Yeah the normal batteries would be the cheapest I'd presume then it would actually be the the solar charging ones, um but the It's i I think they're not very resistant to dropping,

Solar.

'cause if you drop stuff yeah if you if you have y

Well they're not designed.

the size you'd need to charge a remote wouldn't be that big but I mean if remotes always get thrown around and stuff, so.

Practical-wise okay.

Y I think yeah, practically. I mean calculators you don't really throw around a lot whereas remotes you do,

You do get a bit of wear

they can t

but don't calculators have a battery in them as well?

Yeah they do, they yeah they've got dual things, but they're the batteries are smaller I think.

Mm.

Mm.

Another question is like.

Again it de

sorry. W w which one would last the longest, because we don't want customers to be like you know charging like a mobile phone every day.

Mm.

A remote control, like,

W m yeah

so we have to s look at the life also.

Mm.

so the Yeah the the s if you if you had something du uh using the standard batteries and the solar charging, um, I don't think you'd I think the it would d well you know how long the standard double A_s would last in or triple A_s would last.

It would just detract from the attractiveness of the of the whole feature,

Yeah I think i I think it would, yeah.

i it's not gonna add anything, okay.

Okay, can we add in uh an attachment to closing feature?

Okay.

Can we think about that?'Cause if we're doing the kinetic thing and it's.

Yeah.

Shouldn't we do some market research on that first before we add it in because I I personally.

Well add it in to think about um

right, okay.

because, where am I? If it is the kinetic thing and it is small and it is portable and it is a different shape and the kinetic is something people don't do kind of.

Yeah.

we're doing something original and different um but if you wore it if it's something you could just clip on your pocket then you would have that less, you you wouldn't lose it so much.

Yeah.

But then maybe that's looking at someone who's just sitting on their own rather than

Yeah.

the eternal battle for control of the controls.

It's not something that's come up in any of our uh focus groups and market research,

No.

it's not a thing that people are looking for

Okay.

So.

when we threw it open to the field yeah.

Okay.

But it's something to put on the side to think about maybe.

Okay, right.

Okay, well I'll move on.

Mm-hmm.

Um, my second part of my findings uh the mo most current remotes use this silicone uh P_C_B_ board which pr printed circuit board, which basically has these contacts that are really close together and then when you press down on the rubber button, it'll connect the circuit and each each switch is connected to two uh different legs on the chip and so sends a different message and that then uh gets translated by the chip into a code and then it's fired out of this L_E_D_ in the sequence of l on and off bursts.

What kind of things do we have to consider there? Can we what kind of size, does it come in varying sizes or is it just one size and we would have to fit the design of.

Well well this the thi the thing about is um they they can be as big or as small as you want them to be because you can you can print circuit board like that that it's it's simply spaced like that so you can have fit the n the size of the nine buttons in. If you if you see how thin the tracks are, you could you could s you could put them virtually right next to each other and have a much smaller switch on each one,

Mm-hmm. Okay.

if you if you wanted to but um the there is an option to do to do it like that, or you could have some sort of array of switches which I'll speak about in the next bit

Okay.

so that And then to uh yeah, so to conserve battery life the remote should be in a standby mode no while not being used. Now what I was thinking about that was'cause sometimes when y when you have these things, they've got little lights on behind the buttons, so you can see what all the buttons are, like on a mobile phone, they do it more often than on a than on a T_V_ remote,

Mm-hmm.

Mm-hmm.

but you could you could have lights behind the buttons and um after like five or ten seconds of not being used I'd have that turned off, if we decided to go for buttons that could light up.

Okay.

Um, the case material, I've a been sent what the factory can actually give us, the there's the plastic which I think we were gonna go for anyway as the main case case housing

Mm-hmm.

and the the main problems with the well you could go for wood but I think it would be a bit impractical. Titanium um very expensive just to process,

Mm.

which to make it is expensive, and rubber, well you're saying that people like this uh spongy feel this year, so perhaps uh some something made of rubber,

Mm.

but I was thinking more of the buttons, because the buttons which they've they've said that they've they've put across are what is used in some stress ball manufacture and it's meant to be anti R_S_I_.

Okay.

Mm-hmm.

I'm no I'm not sure how that would work because surely your fingers would still be moving over the short distances but that's what they said. Um the problem with the casing is that uh there's quite um there's quite a few design restrictions that they've got on the shape of the case. If you go for titanium they can only do a standard box shape, whereas if you come to plastic they can they can be a little more they can they can do sort of curved shapes. But whether whether or not because we'd have it in two separate units

Second thing is.

Hinged, yeah.

for the flip phone.

yeah and second question is like, a mobile you can change the cover, you call it a skin or whatever.

Yeah.

So in this case if you're looking at like a customer can change the colour like from green, parrot green to chilli red or something like that.

Mm.

So is that feature available in like uh titanium,

Yeah I th

or it's like only specific to plastic or.

Uh. Yeah in in titanium I don't I don't think it would be available at all really, the ju just.

Mm-hmm.

it w well you could make it available in the titanium, it was just it would be so expensive to buy a new case for it, because of the expense of how much titanium is is.

Mm-hmm.

it's light and strong but I think it should be left for aircraft design rather than for for a remote.

It does mark quite easily too if you let it fall. I've got a mobile phone myself which is titanium and it does mark very easily if you drop it.

Yeah, yeah.

I was just wondering if we make the basic mould out of plastic but have like a rubber cover that goes over,

Rubber, yeah.

that would give the spongy feel, that also allows us to kind of have different fasciae for the phone.

And you can peel them off yeah.

Yeah.

So instead of the fascia that comes off being plastic, the fascia that comes off would be the rubber,

Like a rubber sleeve almost, yeah.

Something like.

like those pens that you get with the grip, that you can you can pull that off.

Yeah.

Alright.

Yeah.

Mm.

That could be a good idea.

Hmm.

Mm.

It could it would be comfortable to hold on also.

Okay. Mm.

T

Very cheap way of changing the look of it and people can just buy a new one if they want to.

Yeah.

Well that's been really popular with mobile phones so I don't see why not.

Mm. Yeah.

Also the.

'Kay.

just just going back to the interface designs with the buttons, the I kno we were planning to do some sort of touch screen.

Mm-hmm.

Now what I was saying before about instead of having you could have just a bare P_C_B_ circuit board and I'm sure you could probably get it in different colours and so just by touching it with your finger it'd make the connection over if you had them close enough, that would be one option. Um the second option th they offer rubber buttons, but I thought that an L_C_D_ type of screen'cause one they're incredibly thin um and don't take up much space, two they you can have them in a sort of array and you can arrange the buttons on the screen in a sort of set thing and you could have them like a touch

Sorry I didn't get the last part, you're talking of.

display. Uh what what.

Just what you said I I didn't get the uh meaning of it completely, you're saying like.

Oh on the on the L_C_D_ screen you could becau you could fit it.

Uh-huh.

the problem with it basically is that it's flat and so you can't do lots of curve curved things with it,

Okay.

Mm-hmm.

but um you can you wouldn't have with the L_C_D_ you'd have the wires coming off, you wouldn't have that with the with the L_C_D_, you'd only have that with the printed circuit board.

Mm.

With W also with the.

I don't s sorry to interrupt, I don't see why the curved thing is a problem, if we for example had a shell, once we open that.

Yeah.

Oh it would be flat inside.

You could have a flat screen inside, yeah,

yeah, so it'd be f yeah, yeah.

Mm.

but I'm just wondering whether we want an L_C_D_ screen inside.

Have I misunderstood you?

It wouldn't be like full colour, it would just be black and white,

Mm-hmm.

but there'd be touch touch buttons,

'Kay.

so you wouldn't be pressing down on'em, w or we could have rubber buttons which are made of this material which is anti-R_S_I_.

I think it would be good to have a contrast between, if the whole thing is gonna be this rubber thing it would be good to open it up and see something quite fancy looking inside.

Mm-hmm.

Is rubber. Mm.

Yeah.

Now how would you distinguish, if you had it bare, how would you distinguish where you had to press, I mean.

Yeah I hadn't I hadn't really thought of that to be honest.

Uh.

I just had another idea, I don't know if it helps with that, but just to do with the R_S_I_. Is it possible, just as an option, when we open it up, people can use their fingers to press the button, or we have inside like a small pointer thing when people want to.

Yeah you could, you could have some sort of stylus that you could pull out

Like one of the palm pop thing.

but I I think they could get a bit easily lost,

Mm.

Absolutely, f for somebody who very often, if he would.

'cause I had.

Yeah.

It would have to be attached.

They're easy to replace as well, cheap.

Hmm.

a person who switches channels very often or does use a particular function very often will find it very irritating to use a.

Yeah. Yeah.

and he might spoil the touch-pad very fast compared to a button like, if you keep punching with an pointer or whatever.

Mm-hmm.

Yeah.

Mm.

Yeah okay.

Yeah just a thought they wouldn't actually need one and they could use anything they've got, couldn't they? A pencil or a pen, so they wouldn't really need a.

Okay, we'll talk about that so if you finish your.

Yeah that's.

and we'll come back to that.

yeah that that's the end of m my.

That's you, right okay.

And just one small question before like you are, regarding the circuit, since we are hav having a flip-top, we can customise the two circuits for different type of buttons

Yeah.

like we are keeping the standard buttons on the top and the more complex buttons under this thing, so we can divide the circuit like you know.

Uh yeah. Yeah yeah that that would be fairly simple,

Mm-hmm.

I mean you'd you'd actually have two separate you'd have two separate circuit boards but they'd be joined by wires or like some cabling between them,

Okay. Okay.

Mm-hmm.

'cause in the in the actual flip bit you'd have some linking.

I'll just raise another point while it occurs to me, it kind of applies to both our designers here, so I'm not sure how it would fit in.

Mm.

If we flip open, now you know how you've got some mirrors in nightclubs that are mirrors when you turn to them and you turn away and it gives like an advertising display, you seen those?

No.

And they kind of respond to the turn of your body. Is it possible that when we open our flip-top shell it's a little compact mirror and when you press a button it then goes onto the phone display th uh the remote control display thing.

We're marketing to guys as much as we are to women.

Um.

They don't look at themselves? Just a thought.

Well it's a remote control, you were sitting watching T_V_ are you gonna want to sort of open it and say oh shit I should go and have a shower and do my hair before I put the T_.

Yeah. I know what you mean, it's with us using the ideas for a mobile ph.

it's with us using the ideas for mobile phones I kinda got sidetracked onto that I think, okay.

Yeah, no, no.

trying to think of other features we can build in that wouldn't cost too much

Mm.

but maybe we'll leave that one on the side.

Yeah.

Okay.

'Kay.

I mean you can you could do it, you could have a'cause if you if you just put the full charge through an L_C_D_ display it'll completely blank it out,

Mm.

but I don't know if you could get any mirror effect on it, I'm not sure about.

Oh I think forget about the mirror

Okay, okay.

that was just a very quick passing thought yeah.

Okay, uh would like to share something which I did here. First thing is uh basically on design we just took the input from the previous meeting, especially from the marketing and industrial design, to check on the customer needs and feasibility. Second is we checked into competitors, the picture here shows one of the standard models offered by competitors here. So y you generally see there's not much of variety and like marketing team said uh, people need trendy, they are bored of black and white.

Mm.

So you generally see rectangular shape, very monotonous kind of designs here.

Yeah.

And uh second thing is there's too much of confusion here. No particular remote is standard.

Mm.

Like, some some people have a Here you see this? This is on a I I I found th that uh only common feature is the ch uh channel control and uh volume control, rest other buttons, they are in a very disorganized and they are not consistent th with other models and all.

Okay.

Okay, and second as already discussed with William, we are going to have m maybe a G_ G_U_I_ interface in the f in the middle of the flip-top

Yeah.

and g graphic user interface

Sorry what does that stand for?

Which means.

basically which is what we d do in computer, have icons or touch pad or whatever,

Okay.

Yeah.

which is.

If you if yeah if you have like buttons that appear on the screen in this L_C_D_ screen.

Like you have on a l uh icons or something y you have is a good example of G_U_I_ graphic user interface.

Yeah.

Okay.

So basically not point or click Press any particular device, he just has to click on that particular icon to simplify.

Okay.

And on the top m repetitive buttons which are like volume or channel changing and all could be on the pointing device, that means the use of button. So he need not use a pointer to keep changing the channel if a person is frequent surfer.

Mm-hmm.

So we are having a combination of boards, so f on the s simpler board, on the top we have this button, rubber buttons, to keep frequently changing the channels.

Can we swap that round so th that the ones that we use all the time are on the bottom part?

'Cause if you're thinking about holding it you would be using your thumbs to press, just like a mobile phone.

Yeah.

No you'd hold it you don't if you flip it open then you'd be Wouldn't you? Isn't that the idea? You us if we just use the shell as an example again, you open that, you've got your L_C_D_ display there and you've got the buttons there so you're holding it in the palm of your hand, yeah.

Yeah. That's what I was just saying,

Yeah, yeah but you can do it with your thumb li

But.

and then have the and then have the L_C_D_ at the top

Mm-hmm.

and then be able to touch that for the other controls,

Mm-hmm. Mm-hmm.

Okay and you mean to the.

so have the um the volume and the programme, things like that,

And the lower distance.

on the lower side.

Oh f perfect.

Okay.

Hmm.

So the findings are too many cluttered buttons. Repetition of certain but buttons which I already explained, example the volume and channel control buttons. All are confusing and in inconsistent. Okay we had a latest finding of voice recognition, there was a mail which mentions that our division has developed a new speech recognition s feature. We have to check into the for financial f feasibility whether we can incorporate it this at a low cost, but for like we had g um s response from the customers that they would like to have the feature of finding it.

Yeah.

So it could be like, where is the remote, and the remote answers I am here.

Yeah.

Some kind of thing or it gives a b bleep sound or some kind of sound

Mm.

and if this can be incorporated this would be more uh you can say trendy also? And technologically innovative also.

Yeah.

So we we can check into the financial feasibility of incorporating this. My personal preferences would be like, as already uh marketing department, they want something to do with fruit so I wouldn't say the design should be like a fruit, but yeah we can take inspiration from fruit colours, like the vibrant colours, uh red chilli uh

Mm.

Yeah.

tomato red or whatever and second thing is cer certain standard buttons we should have, like for example, i if you see the previous slide uh

Well, yeah we.

I think most of the users would now be well acquainted with this cer central pattern, here, so we we we would not change h that particular pattern

Yeah.

because i this is the most consistent thing in all remote controls.

I can't see that, is that play and stop and things?

This is central one, the one you.

Or is that volume and channel?

yeah volume and channel. So keep that m that standardised because that's the most common feature across all models, if you look at all the models, it's here. This and voice recognition.

Okay. Right. Okay. Um I'm not sure how long we've got left, but we need to make a decision about um the things we've discussed. So, we agree on Do we agree on the battery?

The kinetic.

Kinetic?

Yeah.

Yeah.

Um, that means that there's no function for li the port, you know that it sits in, then pressing the button then having.

Mm.

so we could incorporate voice recognition for the finding it.

Yeah I think.

It's a bi i it's like a g it's a gadget, it's a

I mean if Yeah it is it is qui it's quite a cool feature to have

selling point.

and also if they've got it if they've got these parts already in stock, then it won't be cheap to re-manufacture them,

Mm-hmm.

uh or it will be cheap sorry to re-manufacture them. So do you reckon that's a good idea that, where's the remote, I'm here thing? I think that would be quite fun.

I think the only

Mm.

I think the only pitfall that we would have would be how much it's going to cost if that means we have to cut down somewhere else,

Yeah.

but I think pretty much we've used cheap relatively cheap and simple things.

Yeah.

Mm.

The L_C_D_'s not cheap.

Yeah to s th well it's it's not as expensive as it would be if it was full colour'cause if we've just got a black and white one,

Hmm.

I mean they use them in calculators so.

Yeah, okay.

Maybe we could start with the black and white.

Yeah.

That that way we could upgrade later.

And you could yeah. Okay.

Yeah, we talked about kinetic charging,

Mm.

we've deci seem to have decided on that, did we decide on double A_ or triple A_ batteries?

Mm.

Well you cou um.

Do you want like a back-up?

No, one of the decisions we had to make was whether we had double A_ or triple A_ batteries'cause they've still got to be charged this way haven't they?

K no the kinetic ones come come with um a sort of w watch a battery that goes in a watch.

I oh I see. Right, okay, got you.

So it's a lot smaller, so it would.

Got you on that okay, didn't realise.

Um, okay so we've got battery. The inside components is pretty standardised across the board isn't it?

Yeah.

Yeah.

So there's not a really a decision to be made there, um. The buttons what did you give us as our The bare-board L_C_D_ or.

Yeah the well I think were we're going for the L_C_D_ on that one, on the buttons,

on the on the on on the top one we're gonna

On the top one okay you've got the touch

yeah.

okay and then.

On the bottom we were gonna have the rubber, the rubber ones, the anti-R_S_I_ ones.

Okay, okay.

Sorry could you repeat that last part?

Um, okay on we've got the flip the flip-screen, the top one is gonna be the L_C_D_ and the bottom one is gonna be the rubberised buttons.

L_C_D_ screen. Mm-hmm. Okay.

And for the sorry.

No, it's fine.

For the body design I think plastic, uh w yeah

Plastic,

we could use the body,

For the inside.

okay.

for the inside and uh rubber as a padding or for the grip, something like to add to the design.

Yeah.

Mm-hmm.

Mm-hmm,

so we decided on a rubber casing for the plastic shell, a variety of designs,

Plast right.

Oh I think so, I think so.

okay. and it is just uh although it's rubberised and spongy, apart from that we're just going to go for sort of vegetable and fruit colours, we're not gonna try and make it actually a vegetable or fruit design or anything else, like a shell that we discussed,

Mm no.

No I think I.

just go for the colours.

It's.

we don't wanna be tacky if we've got a kind of different shape anyway

Okay.

um the fact the m the material that we would be using would be cheap, so we could make it red, we could make it um psychedelic, you know, we could make it black and white zebra stripes,

Mm-hmm.

but that's not really what we're focusing on, what we're focusing on is the m you know.

The feel.

yes. So we could just pick anything.

'Kay. Mm-hmm. Is that going to be this image I've got in my head of something kind of shaped like that, maybe about that size, made of plastic, fits into the palm of the hand, rubberised cover that's spongy. Is that really going to fit fancy look and feel which was the major thing that people wanted, market research,

Uh I it's different.

It's just different

is that fancy?

it's just different from everything else and I mean, I'm trying to imagine um clean looking houses,

Mm.

sort of beige and black

Mm-hmm.

um so you either want something that goes with that, which is what's on the market anyway,

Mm-hmm. Mm-hmm.

or you want something that contrasts as you know like you get clocks now that are more of a talking point than an actual clock because they're so interesting and.

Mm.

Mm-hmm. Yeah. I'm just kind of pushing at that to see if you came out with anything else, but I mean I totally agree. We looked at those remotes, I mean they are kind of anonymous, very similar looking things.

Mm.

This would definitely be different enough, I just wondered if anybody could come up with something even more.

And you would just have an across the b you would have so many different options that you could do, you could have um a plain black one, you could have sparkly pink glittery ones for your little.

Well the thing is the rubberised covers are going to be s relatively cheap to produce, that if in a year's time we we get feedback from the design fairs that show something else is coming I mean it's so easy for us to just produce that and it can be slipped on,

Mm.

Right.

And ones tha ones that have rubbery spikes.

which is another beauty of it.

y you know, you could just go so far with it,

You can you can just.

Acupressure, you could talk of acupressures.

Yeah.

like a puffer fish, you could just, you could take it wherever,

so I think that's quite a flexible thing.

Yeah.

And finally the body should be retouchable, may maybe ch you can change the body or the case casing the case outside,

Yes.

it should be moulded it with the design in such a way you can change it every time.

Yeah, yeah j with these rubber these rubberised ca

Mm.

not the actual uh plastic outside case, just the rubber thing that goes round the outside.

Okay. The rubber.

Yeah. Okay we've got five minutes, um, or that might've been up for a while.

Fine.

Mm, so can I just recap uh Sarah,

Yeah.

for the decisions that we've made, kinetic charging, the watch-type batteries, um L_C_D_ display on th the top side of the flip top, rubberised buttons on the bottom side, we're gonna use fruit and vegetable colours for the rubber cover, the case itself is plastic. That's how far we've got, what else do we have to add to our decisions here?

Fine, uh we were talking of voice recognition also

Mm.

because that we are not for how to look it uh remote control if it's lost.

Mm-hmm.

So, are we looking at voice?

Yes, it was just, there was just a cost issue with that, but it's a good idea we just need to check on the cost,

Or maybe like uh William was suggesting in the last thing some devices you put on key chains.

is that right?

For l yeah, the whistle.

Oh yeah, yeah the whistle ones, yeah.

So we can if financially voice recognition is not feasible we could go for a whistle.

And incorporating the company logo?

Yep.

Have you.

Uh sorry I didn't mention this, but we'll be incorporating in the design.

Mm.

Okay.

'Cause I took it from the feedback you gave in the last meeting from y from y your report, it's not just a case of having a little R_R_ hidden somewhere, they do want it to be obvious that it's our product

Well they do,

don't they?

but I think we can you could.

Since it's the only one of its kind on the market it's obviously gonna be ours.

well it is, it is, I think you just address that with um advertising.

Yeah.

Um, you associate the name with the individual product that it is

Yeah.

and that does the work for you.

Okay.

Obviously it'll have a logo on it just like everything, but everything else has a logo on it. The point is that it's got to stand out somehow differently but I think it does, without that

Okay.

so I'm not worried about that.

Okay. Did we I know at the last meeting we spoke about a beeper, I think that was you talked about a beeper for a location, have we just rejected that in favour of voice recognition then?

Depending on how i.

Yeah, depending on the expense of it.

I mean we've got this memo saying that it's something that the company's looking into, so

And they've got in stock, so yeah.

um that seems to make sense, but if it turns out that it's hugely expensive we'd have to cut down somewhere else, then it's worth thinking about. But I think we'll find out more about cost afterwards.

Yeah.

Right I'm going to wrap it up there.

Okay.

Fine.

I got a end meeting now message on my mo

Mm-hmm.

yeah

Did it?

Yeah.

so. Um

Again a questionnaire huh?

so I think we've probably got it says, closing we have forty minutes

You got to go through.

so I um think we've probably got about that time before we come back.

It's either that or we only had forty minutes to discuss what we just discussed and don't know how long that took.

I think we are pretty going in a clear direction now.

Is everyone happy? Okay.

Question: What was the outcome of the conceptual design meeting?

Response: The participants discussed the properties, materials, user-interface, and trend-watching for their new remote design. They agreed to use a kinetic charging system with a watch-type battery, an LCD display on the top side of the flip top, and rubberized buttons on the bottom side. They also decided to use fruit and vegetable colors for the rubber cover and plastic for the case. They also discussed the possibility of incorporating voice recognition for finding the remote, but this decision was dependent on the cost.

Question: What was the main objective of this meeting?

Response: The main objective of the meeting was to discuss the properties, materials, user-interface, and trend-watching for a new remote design. The participants wanted to create a product that was technologically innovative, easy to use, and had a unique look and feel. They also discussed the possibility of incorporating voice recognition for finding the remote.

Question: Was the remote design inspired by the shape of any real-world objects?

Response: No, the meeting participants did not mention any inspiration from real-world objects for the shape of the remote design.

Question: What did William say about the lighting and sound system in the conference room?

Response: The transcript of the meeting does not include any information about William's comments on the lighting and sound systems in the conference room.

Question: What were the advantages of using a kinetic charging system with a watch-type battery?

Response:
The meeting participants mentioned several advantages of using a kinetic charging system with a watch-type battery:

* It eliminates the need for standard batteries or a charging port.
* It uses the movement of the remote to generate power, making it more convenient and eco-friendly.
* It utilizes a smaller, watch-type battery, which is less expensive to re-manufacture.

Question: Did the participants decide to use solar charging for the remote design?

Response: No, the meeting participants did not decide to use solar charging for the remote design. They discussed the disadvantages of solar charging, such as the need for large solar cells and the reduced effectiveness of solar charging in low-light conditions.

Question: Did the participants discuss incorporating a whistle device for locating the remote control if voice recognition was not financially feasible?

Response: Yes, the participants discussed incorporating a whistle device as a backup option for locating the remote control if voice recognition was not financially feasible.

Question: Did they discuss any other backup options for locating the remote if both voice recognition and the whistle device were found to be financially infeasible?

Response: No, there was no discussion about other backup options for locating the remote control if both voice recognition and the whistle device were found to be financially infeasible.

Question: What did William recommend that the team do when discussing the remote control's body design?

Response: The transcript does not include any information about William's recommendations to the team regarding the body design of the remote control.

Question: Why did the team choose a kinetic charging system with a watch-type battery over a solar charging system?

Response:
The team chose a kinetic charging system with a watch-type battery over a solar charging system for several reasons:

* Solar cells would need to be placed on the case, which would affect the exterior design, and they are not very resistant to dropping.
* Solar charging is less effective in low-light conditions.
* Kinetic charging is more convenient and eco-friendly, as it uses the movement of the remote to generate power, eliminating the need for batteries or a charging port.