OK.

Mike. Mike - one?

Ah.

We're on? Yes, please. I mean, we're testing noise robustness but let's not get silly. OK, so, uh, you've got some, uh, Xerox things to pass out?

Yeah,

That are.

um.

Yeah.

Yeah. Yeah, I'm sorry for the table, but as it grows in size, uh, it.

Uh, so for th the last column we use our imagination. OK.

Ah, yeah.

Ah.

Uh, yeah.

Uh, do you want @ @.

This one's nice, though. This has nice big font.

Yeah.

Let's see. Yeah. Chop!

Yeah.

So

When you get older you have these different perspectives. I mean, lowering the word hour rate is fine, but having big font!

Next time we will put colors or something.

That's what's.

Uh.

Yeah. It's mostly big font. OK.

OK, s so there is kind of summary of what has been done.

Uh Go ahead.

It's this. Summary of experiments since, well, since last week

Oh. OK.

and also since the we've started to run work on this. Um. So since last week we've started to fill the column with um uh features w with nets trained on PLP with on - line normalization but with delta also, because the column was not completely.

Mm - hmm. Mm - hmm..

well, it's still not completely filled,

but we have more results to compare with network using without PLP and finally, hhh,  um ehhh  PL - uh delta seems very important. Uh I don't know. If you take um, let's say, anyway Aurora - two - B, so, the next t the second, uh, part of the table,

Mm - hmm.

uh when we use the large training set using French, Spanish, and English, you have one hundred and six without delta and eighty - nine with the delta.

a And again all of these numbers are with a hundred percent being, uh, the baseline performance,

Yeah, on the baseline, yeah. So.

but with a mel cepstra system going straight into the HTK?

Yeah. Yeah. So now we see that the gap between the different training set is much uh uh much smaller

Yes.

um.

It's out of the way.

But, actually, um, for English training on TIMIT is still better than the other languages. And  Mmm, Yeah. And f also for Italian, actually. If you take the second set of experiment for Italian, so, the mismatched condition,

Mm - hmm.

um when we use the training on TIMIT so, it's multi - English, we have a ninety - one number,

Mm - hmm.

and training with other languages is a little bit worse.

Um Oh, I see. Down near the bottom of this sheet.

So,

Uh,  yes.

yeah.

OK.

And, yeah, and here the gap is still more important between using delta and not using delta. If y if I take the training s the large training set, it's we have one hundred and seventy - two,

Yes.

and one hundred and four when we use delta.

Yeah.

Uh. Even if the contexts used is quite the same,

Mm - hmm.

because without delta we use seventeenths seventeen frames. Uh. Yeah, um, so the second point is that we have no single cross - language experiments, uh, that we did not have last week. Uh, so this is training the net on French only, or on English only, and testing on Italian.

Mm - hmm.

And training the net on French only and Spanish only and testing on, uh TI - digits.

Mm - hmm.

And, fff  um, yeah. What we see is that these nets are not as good, except for the multi - English, which is always one of the best. Yeah, then we started to work on a large dat database containing, uh, sentences from the French, from the Spanish, from the TIMIT, from SPINE, uh from  uh English digits, and from Italian digits. So this is the another line another set of lines in the table. Uh, @ @ with SPINE

Ah, yes. Mm - hmm.

and uh, actually we did this before knowing the result of all the data, uh, so we have to to redo the uh the experiment training the net with, uh PLP, but with delta. But

Mm - hmm.

um this this net performed quite well. Well, it's it's better than the net using French, Spanish, and English only. Uh. So, uh, yeah. We have also started feature combination experiments. Uh many experiments using features and net outputs together. And this is The results are on the other document. Uh, we can discuss this after, perhaps well, just, @ @. Yeah, so basically there are four four kind of systems. The first one, yeah, is combining, um, two feature streams, uh using and each feature stream has its own MPL. So it's the kind of similar to the tandem that was proposed for the first. The multi - stream tandem for the first proposal. The second is using features and KLT transformed MLP outputs. And the third one is to u use a single KLT trans transform features as well as MLP outputs. Um, yeah. Mmm. You know you can you can comment these results,

Yes, I can s I would like to say that, for example, um, mmm, if we doesn't use the delta - delta, uh we have an improve when we use s some combination. But when

Yeah, we ju just to be clear, the numbers here are uh recognition accuracy.

w Yeah, this Yeah, this number recognition acc

So it's not the Again we switch to another.

Yes, and the baseline the baseline have i is eighty - two.

Baseline is eighty - two.

Yeah

So it's experiment only on the Italian mismatched for the moment for this.

Uh, this is Italian mismatched.

Um.

Yeah, by the moment.

Mm - hmm.

OK.

And first in the experiment - one I I do I I use different MLP,

Mm - hmm.

and is obviously that the multi - English MLP is the better. Um. for the ne rest of experiment I use multi - English, only multi - English. And I try to combine different type of feature, but the result is that the MSG - three feature doesn't work for the Italian database because never help to increase the accuracy.

Yeah, eh, actually, if w we look at the table, the huge table, um, we see that for TI - digits MSG perform as well as the PLP,

Mm - hmm.

but this is not the case for Italian what where the error rate is c is almost uh twice the error rate of PLP.

Mm - hmm.

So, um uh, well, I don't think this is a bug but this this is something in probably in the MSG um process that uh I don't know what exactly. Perhaps the fact that the the there's no low - pass filter, well, or no pre - emp pre - emphasis filter and that there is some DC offset in the Italian, or, well, something simple like that. But that we need to sort out if want to uh get improvement by combining PLP and MSG

Mm - hmm.

because for the moment MSG do doesn't bring much information.

Mm - hmm.

And as Carmen said, if we combine the two, we have the result, basically, of PLP.

I Um, the uh, baseline system when you said the baseline system was uh, uh eighty - two percent, that was trained on what and tested on what? That was, uh Italian mismatched d uh, uh, digits, uh, is the testing,

Yeah.

and the training is Italian digits?

Yeah.

So the " mismatch " just refers to the noise and and, uh microphone and so forth,

Yeah.

Yeah.

right? So, um did we have So would that then correspond to the first line here of where the training is is the uh Italian digits?

The train the training of the HTK?

The.

Yes. Ah yes!

Yes.

This h Yes. Th - Yes.

Yes. Training of the net,

Yeah.

yeah. So, um So what that says is that in a matched condition, we end up with a fair amount worse putting in the uh PLP. Now w would do we have a number, I suppose for the matched I I don't mean matched, but uh use of Italian training in Italian digits for PLP only?

Uh yes?

Uh yeah, so this is basically this is in the table. Uh so the number is fifty - two,

Another table.

uh.

Fifty - two percent.

Fift - So No, it's it's the.

No.

No, fifty - two percent of eighty - two?

Of of of uh eighteen.

Eighty.

of eighteen.

Eighty.

So it's it's error rate, basically.

It's plus six.

It's er error rate ratio. So.

Oh this is accuracy!.

Uh, so we have nine nine let's say ninety percent.

Yeah.

Oy!  OK. Ninety.

Yeah. Um  which is uh  what we have also if use PLP and MSG together,

Yeah.

eighty - nine point seven.

OK, so even just PLP, uh, it is not, in the matched condition Um I wonder if it's a difference between PLP and mel cepstra, or whether it's that the net half, for some reason, is not helping.

Uh. P - PLP and Mel cepstra give the same same results.

Same result pretty much?

Well, we have these results. I don't know. It's not Do you have this result with PLP alone,  j fee feeding HTK?

So, s

That That's what you mean?

Yeah,

Just PLP at the input of HTK.

yeah yeah yeah yeah, at the first and the Yeah.

Yeah. So, PLP.

Eighty - eight point six.

Yeah.

Um, so adding MSG

Um.

um Well, but that's yeah, that's without the neural net,

Yeah, that's without the neural net

right?

and that's the result basically that OGI has also with the MFCC with on - line normalization.

But she had said eighty - two.

This is the w well, but this is without on - line normalization.

Right? Oh, this the eighty - two.

Yeah.

Eighty - two is the it's the Aurora baseline, so MFCC. Then we can use well, OGI, they use MFCC th the baseline MFCC plus on - line normalization

Oh, I'm sorry, I k I keep getting confused because this is accuracy.

Yeah, sorry. Yeah.

Yeah.

OK. Alright.

Yeah.

Alright. So this is I was thinking all this was worse. OK so this is all better

Yes, better.

because eighty - nine is bigger than eighty - two.

Mm - hmm.

Yeah.

OK. I'm I'm all better now. OK, go ahead.

So what happ what happens is that when we apply on - line normalization we jump to almost ninety percent.

Yeah. Mm - hmm.

Uh, when we apply a neural network, is the same. We j jump to ninety percent.

Nnn, we don't know exactly.

Yeah.

And And um whatever the normalization, actually. If we use n neural network, even if the features are not correctly normalized, we jump to ninety percent. So.

So we go from eighty - si eighty - eight point six to to ninety, or something.

Well, ninety No, I I mean ninety It's around eighty - nine, ninety, eighty - eight.

Eighty - nine.

Well, there are minor minor differences.

Yeah.

And then adding the MSG does nothing, basically.

No.

Yeah. OK.

Uh For Italian, yeah.

For this case, right?

Um.

Alright. So, um So actually, the answer for experiments with one is that adding MSG, if you uh does not help in that case.

Mm - hmm.

Um.

But w Yeah.

The other ones, we'd have to look at it, but And the multi - English, does uh So if we think of this in error rates, we start off with, uh eighteen percent error rate, roughly.

Mm - hmm.

Um and we uh almost, uh cut that in half by um putting in the on - line normalization and the neural net.

Yeah

And the MSG doesn't however particularly affect things.

No.

And we cut off, I guess about twenty - five percent of the error. Uh no, not quite that, is it. Uh, two point six out of eighteen. About, um sixteen percent or something of the error, um, if we use multi - English instead of the matching condition.

Mm - hmm. Yeah.

Not matching condition, but uh, the uh, Italian training.

Mm - hmm.

Yeah.

OK.

Mmm.

We select these these these tasks because it's the more difficult.

Yes, good. OK? So then you're assuming multi - English is closer to the kind of thing that you could use since you're not gonna have matching, uh, data for the uh for the new for the other languages and so forth. Um, one qu thing is that, uh I think I asked you this before, but I wanna double check. When you say " ME " in these other tests, that's the multi - English,

That's it's a part it's.

but it is not all of the multi - English, right? It is some piece of part of it.

Or, one million frames.

And the multi - English is how much?

You have here the information.

It's one million and a half. Yeah.

Oh, so you used almost all You used two thirds of it,

Yeah.

you think. So, it it's still it hurts you seems to hurt you a fair amount to add in this French and Spanish.

Mmm.

Yeah.

I wonder why Yeah. Uh.

Well Stephane was saying that they weren't hand - labeled,

Yeah, it's.

Yeah.

Yeah.

the French and the Spanish.

The Spanish. Maybe for that.

Hmm.

Mmm.

It's still OK. Alright, go ahead. And then then.

Um. Mmm, with the experiment type - two, I first I tried to to combine, nnn, some feature from the MLP and other feature another feature.

Mm - hmm.

And we s we can first the feature are without delta and delta - delta, and we can see that in the situation, uh, the MSG - three, the same help nothing.

Mm - hmm.

And then I do the same but with the delta and delta - delta PLP delta and delta - delta. And they all p but they all put off the MLP is it without delta and delta - delta. And we have a l little bit less result than the the the baseline PLP with delta and delta - delta.

Mm - hmm.

Maybe if when we have the new the new neural network trained with PLP delta and delta - delta, maybe the final result must be better. I don't know.

Actually, just to be some more.

Uh.

Do This number, this eighty - seven point one number, has to be compared with the

Yes, yeah, I mean it can't be compared with the other

Which number?

cuz this is, uh with multi - English, uh, training.

Mm - hmm.

So you have to compare it with the one over that you've got in a box, which is that, uh the eighty - four point six.

Mm - hmm.

Right?

Uh.

So.

Yeah, but I mean in this case for the eighty - seven point one we used MLP outputs for the PLP net

Yeah.

and straight features with delta - delta. And straight features with delta - delta gives you what's on the first sheet.

Mm - hmm.

Yeah. Not t not

It's eight eighty - eight point six.

tr No. No. No.

Yes.

Not trained with multi - English.

Uh, yeah, but th this is the second configuration.

No, but they they feature @ @ without.

So we use feature out uh, net outputs together with features. So yeah, this is not perhaps not clear here but in this table, the first column is for MLP and the second for the features.

Eh.  Oh, I see. Ah. So you're saying w so asking the question, " What what has adding the MLP done to improve over the,

So, just Yeah so, actually it it it decreased the the accuracy.

uh.

Yeah.

Yes.

Because we have eighty - eight point six.

Uh - huh.

And even the MLP alone What gives the MLP alone? Multi - English PLP. Oh no, it gives eighty - three point six. So we have our eighty - three point six and now eighty - eighty point six,

But.

that gives eighty - seven point one.

Mm - hmm. Eighty - s I thought it was eighty Oh, OK, eighty - three point six and eighty eighty - eight point six.

Eighty - three point six. Eighty.

OK.

Is th is that right? Yeah?

Yeah. But I don't know but maybe if we have the neural network trained with the PLP delta and delta - delta, maybe tha this can help.

Perhaps, yeah.

Well, that's that's one thing, but see the other thing is that, um, I mean it's good to take the difficult case, but let's let's consider what that means. What what we're saying is that one o one of the things that I mean my interpretation of your your s original suggestion is something like this, as motivation. When we train on data that is in one sense or another, similar to the testing data, then we get a win by having discriminant training.

Mm - hmm.

When we train on something that's quite different, we have a potential to have some problems.

Mm - hmm.

And, um, if we get something that helps us when it's somewhat similar, and doesn't hurt us too much when it when it's quite different, that's maybe not so bad.

Yeah. Mmm.

So the question is, if you took the same combination, and you tried it out on, uh on say digits,

On TI - digits? OK.

you know, d Was that experiment done?

No, not yet.

Yeah, OK. Uh, then does that, eh you know maybe with similar noise conditions and so forth,  does it does it then look much better?

Mm - hmm.

And so what is the range over these different kinds of uh of tests? So, an anyway. OK, go ahead.

Yeah.

And, with this type of configuration which I do on experiment using the new neural net with name broad klatt s twenty - seven, uh, d I have found more or less the same result.

Mm - hmm.

So, it's slightly better,

Little bit better?

yeah.

Slightly better.

Yeah.

Slightly bet better. Yes, is better.

And and you know again maybe if you use the, uh, delta there, uh, you would bring it up to where it was, uh you know at least about the same for a difficult case.

Yeah, maybe. Maybe. Maybe.

Yeah.

Oh, yeah.

Yeah. Well, so perhaps let's let's jump at the last experiment.

Oh, yeah.

So.

It's either less information from the neural network if we use only the silence output.

i

Mm - hmm.

It's again better. So it's eighty - nine point point one.

Yeah,

Mm - hmm.

and we have only forty forty feature

So.

because in this situation we have one hundred and three feature.

Yeah.

Yeah. And then w with the first configuration, I f I am found that work, uh, doesn't work.

Yeah.

uh, well, work, but is better, the second configuration. Because I for the del Engli - PLP delta and delta - delta, here I have eighty - five point three accuracy, and with the second configuration I have eighty - seven point one.

Um, by the way, there is a another, uh, suggestion that would apply, uh, to the second configuration, um, which, uh, was made, uh, by, uh, Hari. And that was that, um, if you have uh feed two streams into HTK, um, and you, uh, change the, uh variances if you scale the variances associated with, uh these streams um, you can effectively scale the streams. Right? So, um, you know, without changing the scripts for HTK, which is the rule here, uh, you can still change the variances

Mm - hmm..

which would effectively change the scale of these these, uh, two streams that come in.

Uh,  yeah.

And, um, so, um, if you do that, for instance it may be the case that, um, the MLP should not be considered as strongly, for instance.

Mmm.

And, um, so this is just setting them to be, excuse me, of equal equal weight. Maybe it shouldn't be equal weight.

Maybe.

Right? You know, I I'm sorry to say that gives more experiments if we wanted to look at that, but but, uh, um, you know on the other hand it's just experiments at the level of the HTK recognition.

Mmm.

It's not even the HTK,

Yeah.

uh, uh.

Yeah. Yeah.

Well, I guess you have to do the HTK training also.

so this is what we decided to do.

Uh, do you? Let me think. Maybe you don't. Uh. Yeah, you have to change the No, you can just do it in as once you've done the training.

And then you can vary it. Yeah.

Yeah, the training is just coming up with the variances so I guess you could you could just scale them all.

Scale

Variances.

Yeah. But Is it i th I mean the HTK models are diagonal covariances, so I d Is it.

That's uh, exactly the point, I think, that if you change um, change what they are.

Hmm. Mm - hmm.

It's diagonal covariance matrices, but you say what those variances are.

Mm - hmm.

So, that you know, it's diagonal, but the diagonal means th that then you're gonna it's gonna it's gonna internally multiply it and and uh, uh, i it im uh implicitly exponentiated to get probabilities, and so it's it's gonna it's it's going to affect the range of things if you change the change the variances of some of the features.

Mmm. Mmm.

do?

So, i it's precisely given that model you can very simply affect, uh, the s the strength that you apply the features. That was that was, uh, Hari's suggestion.

Yeah. Yeah.

So, um.

Yeah.

Yeah. So. So it could just be that h treating them equally, tea treating two streams equally is just just not the right thing to do. Of course it's potentially opening a can of worms because, you know, maybe it should be a different number for for each kind of test set, or something,

Mm - hmm.

but OK.

Yeah.

So I guess the other thing is to take you know if one were to take, uh, you know, a couple of the most successful of these,

Yeah, and test across everything.

and uh Yeah, try all these different tests.

Mmm.

Yeah.

Yeah.

Alright. Uh.

So, the next point, yeah, we've had some discussion with Steve and Shawn, um, about their um, uh, articulatory stuff, um. So we'll perhaps start something next week.

Mm - hmm.

Um, discussion with Hynek, Sunil and Pratibha for trying to plug in their our our networks with their within their block diagram, uh, where to plug in the the network, uh, after the the feature, before as um a as a plugin or as a anoth another path, discussion about multi - band and TRAPS, um, actually Hynek would like to see, perhaps if you remember the block diagram there is, uh, temporal LDA followed b by a spectral LDA for each uh critical band. And he would like to replace these by a network which would, uh, make the system look like a TRAP. Well, basically, it would be a TRAP system. Basically, this is a TRAP system kind of TRAP system, I mean, but where the neural network are replaced by LDA. Hmm. Um, yeah, and about multi - band, uh, I started multi - band MLP trainings, um mmh  Actually, I w I w hhh  prefer to do exactly what I did when I was in Belgium. So I take exactly the same configurations, seven bands with nine frames of context, and we just train on TIMIT, and on the large database, so, with SPINE and everything. And, mmm, I'm starting to train also, networks with larger contexts. So, this would would be something between TRAPS and multi - band because we still have quite large bands, and but with a lot of context also. So Um Yeah, we still have to work on Finnish, um, basically, to make a decision on which MLP can be the best across the different languages. For the moment it's the TIMIT network, and perhaps the network trained on everything. So. Now we can test these two networks on with with delta and large networks. Well, test them also on Finnish

Mmm.

and see which one is the the the best. Uh, well, the next part of the document is, well, basically, a kind of summary of what everything that has been done. So. We have seventy - nine M L Ps trained on one, two, three, four, uh, three, four, five, six, seven ten on ten different databases.

Mm - hmm.

Uh, the number of frames is bad also, so we have one million and a half for some, three million for other, and six million for the last one. Uh, yeah!  As we mentioned, TIMIT is the only that's hand - labeled, and perhaps this is what makes the difference. Um. Yeah, the other are just Viterbi - aligned. So these seventy - nine MLP differ on different things. First, um with respect to the on - line normalization, there are that use bad on - line normalization, and other good on - line normalization. Um. With respect to the features, with respect to the use of delta or no, uh with respect to the hidden layer size and to the targets. Uh, but of course we don't have all the combination of these different parameters Um. s What's this? We only have two hundred eighty six different tests And no not two thousand.

Ugh! I was impressed boy, two thousand.

Yeah.

Ah, yes.

OK.

I say this morning that @ @ thought it was the.

Alright, now I'm just slightly impressed, OK.

Um. Yeah, basically the observation is what we discussed already. The MSG problem, um, the fact that the MLP trained on target task decreased the error rate. but when the M - MLP is trained on the um is not trained on the target task, it increased the error rate compared to using straight features. Except if the features are bad uh, actually except if the features are not correctly on - line normalized. In this case the tandem is still better even if it's trained on not on the target digits.

Yeah. So it sounds like yeah, the net corrects some of the problems with some poor normalization.

Yeah.

But if you can do good normalization it's it's uh OK.

Yeah.

Yeah.

Uh, so the fourth point is, yeah, the TIMIT plus noise seems to be the training set that gives better the best network.

So So - Let me bef before you go on to the possible issues.

Mm - hmm.

So, on the MSG uh problem um, I think that in in the um, in the short time solution um, that is, um, trying to figure out what we can proceed forward with to make the greatest progress,

Mm - hmm.

uh, much as I said with JRASTA, even though I really like JRASTA and I really like MSG,

Mm - hmm.

I think it's kind of in category that it's, it it may be complicated.

Yeah.

And uh it might be if someone's interested in it, uh, certainly encourage anybody to look into it in the longer term, once we get out of this particular rush uh for results.

Mm - hmm.

But in the short term, unless you have some some s strong idea of what's wrong,

I don't know at all but I've perhaps I have the feeling that it's something that's quite quite simple or just like nnn, no high - pass filter

Yeah, probably.

or Mmm. Yeah. My But I don't know.

There's supposed to well MSG is supposed to have a an on - line normalization though, right?

It's There is, yeah, an AGC - kind of AGC. Yeah. Yeah. Yeah.

Yeah, but also there's an on - line norm besides the AGC, there's an on - line normalization that's supposed to be uh, yeah,

Mmm.

taking out means and variances and so forth. So.

Yeah.

In fac in fact the on - line normalization that we're using came from the MSG design,

Um.

so it's.

Yeah, but Yeah. But this was the bad on - line normalization. Actually. Uh. Are your results are still with the bad the bad.

Maybe, may No? With the better.

With the O - OLN - two?

No?

Ah yeah, you have you have OLN - two,

Oh! Yeah, yeah, yeah! With " two ", with " on - line - two ".

yeah.

Yeah, yeah,

" On - line - two " is good.

So it's, is the good yeah.

yeah. Yep, it's a good.

" Two " is good?

And.

No, " two " is bad.

Yeah.

Well, actually, it's good with the ch with the good.

OK. Yeah. So Yeah, I I agree. It's probably something simple uh, i if if uh someone, you know, uh, wants to play with it for a little bit. I mean, you're gonna do what you're gonna do

Mmm.

but but my my guess would be that it's something that is a simple thing that could take a while to find.

But Yeah. Mmm. I see, yeah.

Yeah.

And.

Uh.  And the other the results uh, observations two and three, Um, is

Mmm.

uh Yeah, that's pretty much what we've seen. That's that what we were concerned about is that if it's not on the target task If it's on the target task then it it it helps to have the MLP transforming it.

Mmm.

If it uh if it's not on the target task, then, depending on how different it is, uh you can get uh, a reduction in performance.

Mmm.

And the question is now how to how to get one and not the other? Or how to how to ameliorate the the problems.

Mmm.

Um, because it it certainly does is nice to have in there, when it when there is something like the training data.

Mm - hmm. Um. Yeah. So, the the reason Yeah, the reason is that the perhaps the target the the task dependency the language dependency, and the noise dependency.

So that's what you say th there. I see.

Well, the e e But this is still not clear because, um, I I I don't think we have enough result to talk about the the language dependency. Well, the TIMIT network is still the best but there is also an the other difference, the fact that it's it's hand - labeled.

Hey! Um, just you can just sit here. Uh, I d I don't think we want to mess with the microphones but it's uh Just uh, have a seat. Um. s Summary of the first uh, uh forty - five minutes is that some stuff work and works, and some stuff doesn't OK,

We still have uh this One of these perhaps?

Yeah.

Mm - hmm..

Yeah, I guess we can do a little better than that but I think if you if you start off with the other one, actually, that sort of has it in words and then th that has it the associated results.

Um.

OK. So you're saying that um, um, although from what we see, yes there's what you would expect in terms of a language dependency and a noise dependency. That is, uh, when the neural net is trained on one of those and tested on something different, we don't do as well as in the target thing. But you're saying that uh, it is Although that general thing is observable so far, there's something you're not completely convinced about. And and what is that? I mean, you say " not clear yet ". What what do you mean?

Uh, mmm, uh,  I mean, that the the fact that s Well, for for TI - digits the TIMIT net is the best,  which is the English net.

Mm - hmm.

But the other are slightly worse. But you have two two effects, the effect of changing language and the effect of training on something that's Viterbi - aligned instead of hand hand - labeled.

Yeah.

So. Um. Yeah.

Do you think the alignments are bad? I mean, have you looked at the alignments at all? What the Viterbi alignment's doing?

Mmm. I don't I don't know. Did - did you look at the Spanish alignments Carmen?

Mmm, no.

Might be interesting to look at it. Because, I mean, that is just looking but um, um It's not clear to me you necessarily would do so badly from a Viterbi alignment. It depends how good the recognizer is

Mm - hmm.

that's that the the engine is that's doing the alignment.

Yeah. But Yeah. But, perhaps it's not really the the alignment that's bad but the just the ph phoneme string that's used for the alignment

Aha!

Mmm.

Yeah..

The pronunciation models and so forth

I mean for We It's single pronunciation, uh.

Aha.

French French s uh, phoneme strings were corrected manually

I see.

so we asked people to listen to the um the sentence and we gave the phoneme string and they kind of correct them. But still,  there there might be errors just in the in in the ph string of phonemes. Mmm. Um. Yeah, so this is not really the Viterbi alignment,  in fact, yeah. Um, the third The third uh issue is the noise dependency perhaps but, well, this is not clear yet because all our nets are trained on the same noises and.

I thought some of the nets were trained with SPINE and so forth. So it And that has other noise.

Yeah. So Yeah. But Yeah. Results are only coming for for this net. Mmm.

OK, yeah, just don't just need more more results there with that @ @.

Yeah. Um. So. Uh, from these results we have some questions with answers. What should be the network input? Um, PLP work as well as MFCC, I mean. Um. But it seems impor important to use the delta. Uh, with respect to the network size, there's one experiment that's still running and we should have the result today, comparing network with five hundred and one thousand units. So, nnn, still no answer actually.

Hm - hmm.

Uh, the training set, well, some kind of answer. We can, we can tell which training set gives the best result, but we don't know exactly why. Uh, so.

Uh. Right, I mean the multi - English so far is is the best.

Yeah.

" Multi - multi - English " just means " TIMIT ",

Yeah.

right?

Yeah.

So uh That's Yeah. So. And and when you add other things in to to broaden it, it gets worse uh typically.

Mmm. Mm - hmm.

Yeah.

Then uh some questions without answers.

OK.

Uh, training set, um,

Uh - huh.

uh, training targets.

I like that. The training set is both questions, with answers and without answers.

It's Yeah. Yeah.

It's sort of, yes it's mul it's multi - uh - purpose.

Yeah.

OK.

Uh, training s Right. So Yeah, the training targets actually, the two of the main issues perhaps are still the language dependency and the noise dependency. And perhaps to try to reduce the language dependency, we should focus on finding some other kind of training targets.

Mm - hmm.

And labeling s labeling seems important uh, because of TIMIT results.

Mm - hmm.

Uh. For moment you use we use phonetic targets but we could also use articulatory targets, soft targets, and perhaps even, um use networks that doesn't do classification but just regression so uh, train to have neural networks that um, um, uh,

Mm - hmm.

does a regression and well, basically com com compute features and noit not, nnn, features without noise. I mean uh, transform the fea noisy features in other features that are not noisy. But continuous features. Not uh uh, hard targets.

Mm - hmm. Mm - hmm.

Uh.

Yeah, that seems like a good thing to do, probably,  not uh again a short - term sort of thing.

Yeah.

I mean one of the things about that is that um it's e u the ri I guess the major risk you have there of being is being dependent on very dependent on the kind of noise and and so forth.

Yeah. f But, yeah.

Uh. But it's another thing to try.

So, this is w w i wa wa this is one thing, this this could be could help could help perhaps to reduce language dependency and for the noise part um we could combine this with other approaches, like, well, the Kleinschmidt approach. So the d the idea of putting all the noise that we can find inside a database. I think Kleinschmidt was using more than fifty different noises to train his network,

Yeah.

Mm - hmm.

and So this is one approach and the other is multi - band uh, that I think is more robust to the noisy changes.

Mm - hmm. Mm - hmm.

So perhaps, I think something like multi - band trained on a lot of noises with uh, features - based targets could could could help.

Yeah, if you i i It's interesting thought maybe if you just trained up I mean w yeah, one one fantasy would be you have something like articulatory targets and you have um some reasonable database, um but then which is um copied over many times with a range of different noises,

Mm - hmm.

And uh If Cuz what you're trying to do is come up with a a core, reasonable feature set which is then gonna be used uh, by the the uh HMM system.

Mm - hmm.

So. Yeah, OK.

So, um, yeah. The future work is, well, try to connect to the to make to plug in the system to the OGI system. Um, there are still open questions there, where to put the MLP basically.

Mm - hmm.

Um.

And I guess, you know, the the the real open question, I mean, e u there's lots of open questions, but one of the core quote  " open questions " for that is um, um, if we take the uh you know, the best ones here, maybe not just the best one, but the best few or something You want the most promising group from these other experiments. Um, how well do they do over a range of these different tests, not just the Italian?

Mmm,

Um. And y

Yeah, yeah.

y Right? And then um then see, again, how We know that there's a mis there's a uh a a loss in performance when the neural net is trained on conditions that are different than than, uh we're gonna test on, but well, if you look over a range of these different tests um, how well do these different ways of combining the straight features with the MLP features, uh stand up over that range?

Mm - hmm.

That's that that seems like the the the real question. And if you know that So if you just take PLP with uh, the double - deltas. Assume that's the p the feature. look at these different ways of combining it. And uh, take let's say, just take uh multi - English cause that works pretty well for the training.

Mm - hmm.

And just look take that case and then look over all the different things. How does that How does that compare between the.

So all the all the test sets you mean, yeah.

Yeah.

All the different test sets,

And.

and for and for the couple different ways that you have of of of combining them.

Yeah.

Um. How well do they stand up, over the.

Mmm. And perhaps doing this for cha changing the variance of the streams and so on getting different scaling.

Mm - hmm.

That's another possibility if you have time, yeah. Yeah.

Um. Yeah, so thi this sh would be more working on the MLP as an additional path instead of an insert to the to their diagram.

Cuz Yeah. Perhaps the insert idea is kind of strange because nnn, they they make LDA and then we will again add a network does discriminate anal nnn, that discriminates,

Yeah. It's a little strange

or? Mmm?

but on the other hand they did it before.

Mmm. And and and

Um the

yeah. And because also perhaps we know that the when we have very good features the MLP doesn't help. So. I don't know.

Um, the other thing, though, is that um So. Uh, we we wanna get their path running here, right? If so, we can add this other stuff.

Um.

as an additional path right?

Yeah, the the way we want to do.

Cuz they're doing LDA RASTA.

The d What?

They're doing LDA RASTA,

Yeah, the way we want to do it perhaps is to just to get the VAD labels and the final features.

yeah?

So they will send us the Well, provide us with the feature files,

I see. I see.

and with VAD uh, binary labels so that we can uh, get our MLP features and filter them with the VAD and then combine them with their f feature stream. So.

I see. So we So. First thing of course we'd wanna do there is to make sure that when we get those labels of final features is that we get the same results as them. Without putting in a second path.

Uh. You mean Oh, yeah! Just re re retraining r retraining the HTK?

Yeah just th w i i Just to make sure that we have we understand properly what things are, our very first thing to do is to is to double check that we get the exact same results as them on HTK.

Oh yeah. Yeah, OK. Mmm.

Yeah.

Uh, I mean, I don't know that we need to r

Yeah.

Um Do we need to retrain I mean we can just take the re their training files also. But. But, uh just for the testing, jus just make sure that we get the same results so we can duplicate it before we add in another.

Mmm. OK.

Cuz otherwise, you know, we won't know what things mean.

Oh, yeah. OK. And um. Yeah, so fff, LogRASTA, I don't know if we want to We can try networks with LogRASTA filtered features.

Maybe.

Mmm. I'm sorry? Yeah. Well Yeah. But.

Oh! You know, the other thing is when you say comb I'm I'm sorry, I'm interrupting.  that u Um, uh, when you're talking about combining multiple features, um Suppose we said, " OK, we've got these different features and so forth, but PLP seems pretty good. " If we take the approach that Mike did and have.

Mm - hmm.

I mean, one of the situations we have is we have these different conditions. We have different languages, we have different different noises, Um If we have some drastically different conditions and we just train up different M L Ps with them.

And put put them together. What what What Mike found, for the reverberation case at least, I mean I mean, who knows if it'll work for these other ones. That you did have nice interpolative effects. That is, that yes, if you knew what the reverberation condition was gonna be and you trained for that, then you got the best results. But if you had, say, a heavily - reverberation ca heavy - reverberation case and a no - reverberation case, uh, and then you fed the thing, uh something that was a modest amount of reverberation then you'd get some result in between the two. So it was sort of behaved reasonably. Is tha that a fair Yeah.

Yeah. So you you think it's perhaps better to have several M L Yeah but.

It works better if what?

Yea

I see. Well, see, i oc You were doing some something that was So maybe the analogy isn't quite right. You were doing something that was in way a little better behaved. You had reverb for a single variable which was re uh, uh, reverberation. Here the problem seems to be is that we don't have a hug a really huge net with a really huge amount of training data. But we have s f for this kind of task, I would think, sort of a modest amount. I mean, a million frames actually isn't that much. We have a modest amount of of uh training data from a couple different conditions, and then uh in yeah, that and the real situation is that there's enormous variability that we anticipate in the test set in terms of language, and noise type uh, and uh, uh, channel characteristic, sort of all over the map. A bunch of different dimensions. And so, I'm just concerned that we don't really have um, the data to train up I mean one of the things that we were seeing is that when we added in we still don't have a good explanation for this, but we are seeing that we're adding in uh, a fe few different databases and uh the performance is getting worse and uh, when we just take one of those databases that's a pretty good one, it actually is is is is is better. And uh that says to me, yes, that, you know, there might be some problems with the pronunciation models that some of the databases we're adding in or something like that. But one way or another we don't have uh, seemingly, the ability to represent, in the neural net of the size that we have, um, all of the variability that we're gonna be covering. So that I'm I'm I'm hoping that um, this is another take on the efficiency argument you're making, which is I'm hoping that with moderate size neural nets, uh, that uh if we if they look at more constrained conditions they they'll have enough parameters to really represent them. Mm - hmm. Mm - hmm. Mm - hmm. Yeah.

So doing both is is not is not right, you mean, or? Yeah.

Yeah. I I just sort of have a feeling.

But Yeah. Mm - hmm..

Yeah. I mean i i e The um I think it's true that the OGI folk found that using LDA RASTA, which is a kind of LogRASTA, it's just that they have the I mean it's done in the log domain, as I recall, and it's it uh it's just that they d it's trained up, right? That that um benefitted from on - line normalization. So they did At least in their case, it did seem to be somewhat complimentary. So will it be in our case, where we're using the neural net? I mean they they were not not using the neural net. Uh I don't know. OK, so the other things you have here are uh, trying to improve results from a single Yeah. Make stuff better. OK. Uh. Yeah. And CPU memory issues. Yeah. We've been sort of ignoring that, haven't we?

Yeah, so I don't know.

But.

But we have to address the problem of CPU and memory we.

Yeah, but I li Well, I think My impression You you folks have been looking at this more than me. But my impression was that uh, there was a a a a strict constraint on the delay,

Yeah.

but beyond that it was kind of that uh using less memory was better, and using less CPU was better. Something like that,

Yeah, but.

right?

Yeah. So, yeah, but we've I don't know. We have to get some reference point to where we Well, what's a reasonable number? Perhaps be because if it's if it's too large or large or @ @.

Um, well I don't think we're um completely off the wall. I mean I think that if we if we have Uh, I mean the ultimate fall back that we could do If we find uh I mean we may find that we we're not really gonna worry about the M L You know, if the MLP ultimately, after all is said and done, doesn't really help then we won't have it in.

Mmm.

If the MLP does, we find, help us enough in some conditions, uh, we might even have more than one MLP. We could simply say that is uh, done on the uh, server.

Mmm.

And it's uh We do the other manipulations that we're doing before that. So, I I I think I think that's that's OK.

And Yeah.

So I think the key thing was um, this plug into OGI. Um, what what are they What are they gonna be working Do we know what they're gonna be working on while we take their features,

They're They're starting to wor work on some kind of multi - band.

and?

So. Um This that was Pratibha. Sunil, what was he doing, do you remember?

Sunil?

Yeah. He was doing something new or?

I I don't re I didn't remember. Maybe he's working with neural network.

I don't think so. Trying to tune wha networks?

Yeah, I think so.

I think they were also mainly, well, working a little bit of new things, like networks and multi - band, but mainly trying to tune their their system as it is now to just trying to get the best from this this architecture.

Yeah.

OK. So I guess the way it would work is that you'd get There'd be some point where you say, " OK, this is their version - one " or whatever, and we get these VAD labels and features and so forth for all these test sets from them,

Mm - hmm.

and then um, uh, that's what we work with. We have a certain level we try to improve it with this other path and then um, uh, when it gets to be uh, January some point uh, we say, " OK we we have shown that we can improve this, in this way. So now uh um what's your newest version? " And then maybe they'll have something that's better and then we we'd combine it. This is always hard. I mean I I I used to work with uh folks who were trying to improve a good uh, HMM system with uh with a neural net system and uh, it was a common problem that you'd Oh, and this Actually, this is true not just for neural nets but just for in general if people were working with uh, rescoring uh, N - best lists or lattices that come came from uh, a mainstream recognizer. Uh, You get something from the the other site at one point and you work really hard on making it better with rescoring. But they're working really hard, too. So by the time you have uh, improved their score, they have also improved their score

Mmm.

and now there isn't any difference,

Yeah.

because the other.

Yeah.

So, um, I guess at some point we'll have to

So it's.

uh  Uh, I I don't know. I think we're we're integrated a little more tightly than happens in a lot of those cases. I think at the moment they they say that they have a better thing we can we e e

Mmm.

What takes all the time here is that th we're trying so many things, presumably uh, in a in a day we could turn around uh, taking a new set of things from them and and rescoring it,

Mmm. Yeah. Yeah, perhaps we could.

right? So. Yeah. Well, OK. No, this is I think this is good. I think that the most wide open thing is the issues about the uh, you know, different trainings. You know, da training targets and noises and so forth.

Mmm. So we we can for we c we can forget combining multiple features and MLG perhaps,

That's sort of wide open.

or focus more on the targets and on the training data and?

Yeah, I think for right now um, I th I I really liked MSG. And I think that, you know, one of the things I liked about it is has such different temporal properties. And um, I think that there is ultimately a really good uh, potential for, you know, bringing in things with different temporal properties. Um, but um, uh, we only have limited time and there's a lot of other things we have to look at.

Mmm.

And it seems like much more core questions are issues about the training set and the training targets, and fitting in uh what we're doing with what they're doing, and, you know, with limited time. Yeah. I think we have to start cutting down.

Mmm.

So uh I think so, yeah. And then, you know, once we Um, having gone through this process and trying many different things, I would imagine that certain things uh, come up that you are curious about uh, that you'd not getting to and so when the dust settles from the evaluation uh, I think that would time to go back and take whatever intrigued you most, you know, got you most interested uh and uh and and work with it, you know, for the next round. Uh, as you can tell from these numbers uh, nothing that any of us is gonna do is actually gonna completely solve the problem.

Mmm.

So. So,  there'll still be plenty to do. Barry, you've been pretty quiet.

Just listening.

Well I figured that, but That what what what were you involved in in this primarily?

Um, helping out uh, preparing Well, they've been kind of running all the experiments and stuff and I've been uh, uh w doing some work on the on the preparing all all the data for them to to um, train and to test on. Um Yeah. Right now, I'm I'm focusing mainly on this final project I'm working on in Jordan's class.

Ah!

Yeah.

I see. Right. What's what's that?

Um, I'm trying to um So there was a paper in ICSLP about um this this multi - band um, belief - net structure.  This guy did.

Mm - hmm.

uh basically it was two H M Ms with with a with a dependency arrow between the two H M

Uh - huh.

And so I wanna try try coupling them instead of t having an arrow that that flows from one sub - band to another sub - band. I wanna try having the arrows go both ways. And um, I'm just gonna see if if that that better models um, uh asynchrony in any way or um Yeah.

Oh! OK. Well, that sounds interesting.

Yeah.

OK. Alright. Anything to you wanted to No. OK. Silent partner in the in the meeting. Oh, we got a laugh out of him, that's good. OK, everyone h must contribute to the our our sound sound files here. OK, so speaking of which, if we don't have anything else that we need You happy with where we are?

Mmm.

Know know wher know where we're going? Uh.

I think so, yeah.

Yeah, yeah. You you happy?

You're happy. OK everyone should be happy. OK. You don't have to be happy. You're almost done. Yeah, yeah. OK.

Al - actually I should mention So if  um, about the Linux machine " Swede. "

Yeah.

So it looks like the um, neural net tools are installed there.

Mmm.

And um Dan Ellis  I believe knows something about using that machine so

Mmm.

If people are interested in in getting jobs running on that maybe I could help with that.

Yeah, but I don't know if we really need now a lot of machines. Well. we could start computing another huge table but yeah, we.

Well. Yeah, I think we want a different table, at least

Yeah, sure.

Right? I mean there's there's some different things that we're trying to get at now.

But.

But.

Yeah. Mmm.

So. Yeah, as far as you can tell, you're actually OK on C - on CPU uh, for training and so on? Yeah.

Ah yeah. I think so. Well, more is always better, but mmm, I don't think we have to train a lot of networks, now that we know We just select what works fine

OK. OK.

and try to improve this

Yeah. to work

And we're OK on And we're OK on disk?

and It's OK, yeah. Well sometimes we have some problems.

Some problems with the.

But they're correctable, uh problems.

Yeah, restarting the script basically

You know.

and.

Yes. Yeah, I'm familiar with that one, OK. Alright, so uh,  since uh, we didn't ha get a channel on for you,  you don't have to read any digits but the rest of us will. Uh, is it on? Well. We didn't uh I think I won't touch anything cuz I'm afraid of making the driver crash which it seems to do, pretty easily. OK, thanks. OK, so we'll uh I'll start off the uh um connect the.

My battery is low.

Well, let's hope it works. Maybe you should go first and see so that you're OK.

batteries?

Yeah, your battery's going down too.

Transcript uh two.

Carmen's battery is d going down too.

Oh, OK. Yeah. Why don't you go next then. OK. Guess we're done. OK, uh so. Just finished digits. Yeah, so. Uh Well, it's good. I think I guess we can turn off our microphones now.

Just pull the batteries out.

Question: What were the primary concerns that did not get resolved during the meeting?

Response: I cannot find information on any concerns that were not resolved during the meeting in the provided context.

Question: What was Barry's sentiment about the team's progress?

Response: I cannot find any information about Barry's sentiment regarding the team's progress in the provided context.

Question: What did the participants meet to discuss?

Response: The participants met to discuss progress made on a project to improve speech recognition using neural networks, including a summary of experiments since the last meeting, features combination experiments, and future work.

Question: What was the advantage of combining PLP and MSG features?

Response: The provided context does not contain information on the advantage of combining PLP and MSG features.

Question: Was the primary training language English?

Response: Yes, the primary training language was English, based on multi-English data sets.

Question: Did the neural net alone provide a significant boost in accuracy over straight features?

Response: The provided context does not specify whether neural net alone provided a significant boost in accuracy over straight features.

Question: What was decided regarding the discrepancy between MSG performing well on TI Digits, but not on Italian?

Response: The provided context does not contain information regarding discrepancies between MSG performing well on TI Digits, but not on Italian.

Question: Did the team decide to combine multiple MLPs and features into their final approach?

Response: The team did not reach a final decision on combining multiple MLPs and features into their final approach.

Question: What was their conclusion regarding the use of MSG features in Italian tests?

Response: I cannot find information regarding the conclusion of the team about the use of MSG features in Italian tests in the provided context.

Question: What future tasks were decided to be worked on after the meeting?

Response:
The participants decided to explore the following tasks in the future:
* Integrate their MLP path with OGI's system.
* Attempt to improve results from a single MLP path.
* Resolve computational and memory usage issues.
* Improve MLP training through the use of various training targets, training sets, and noise types.


Oops.

So, hello everyone. We're here to have a kick-off meeting for the design of a f for the beginning of new project um uh remote control for the design for a new remote control. I'm the Project Manager Christa Pavlov and okay let's begin..

So I'm first going to do an opening then we get used to one anothers and we speak about this tool we're going to design and try to make a project plan, some discussion and then we talk of uh the next meeting. So um we want to to do a new remote control. It has to be original, trendy and user friendly.

Um I think the important points we have to t talk about are uh it's functional design, it's conceptual design, and desail detailed design. and for that we're going um all to work individually and then have meeting during the whole day. Um, so let's try the whiteboard.

Wow..

Um so any of you who want to go.

Yeah, for favourite animals.

It's gonna be not my favourite one but the one I can draw.

And it's gonna be you'll try to guess.

Wow. Complex.

Wow..

Huh? A cat.

No..

No..

No. Darn. Uh.

A rabbit.

Yes, that's a rabbit.

A rabbit.

That's my favourite one.

A what?

Rabbit.

A r a rabbit, oh oh yeah, where is the carrot?.

That's it.

Okay mm-hmm.

You want to go?

I am not very good at uh this kind of stuff.

Hmm..

My favourite animal is.

You wa

Wow..

A human

Guess.

ah.

A human, yay. It's a very complex animal.

No.

and um yeah. Characteristics of this this animal is dangerous..

Mm I think you're supposed to, yeah.

Is the white okay.

Hmm.

I guess you can..

Wow. That's cobra.

Ah, a kind of uh snake? Cobra?

Yeah uh not really.

Exactly..

Small cobra..

No, it just small cobra, yeah.

Is that a worm? Or.

Uh-huh..

It's co c quite recognisable..

What about you uh.

Uh yeah Christa Pavlov Mm.

Christa?.

Christa Christa.

Chris..

A fish..

Mm.

Hmm.

Smiling fish.

Smile fish..

Okay.

A smiling fish. Mm-hmm.

Mm-hmm.

So, w whiteboard is working?.

Yeah..

Yeah..

Good. Next..

Next. Let's talk about money.

Just tr try to guess who is a User Interface Designer.

Yeah, well..

According to the drawings.

Yeah, you're.

Not me..

Okay.

So.

So. Twenty five Euro for a remote control.

Yeah, mm that's the price we want to.

Hmm.

that's the aim for the price for the remote control.

Okay.

We aim to do this profit..

On the international market.

'tis big number.

Yeah. Mm-hmm.

Yeah, we're to sell two million then.

Wow.

Mm-hmm.

Mm for a production cost of mm twelve fifty Euros maximum.'Kay. So any of you have experience in remote controls?.

Mm yeah.

Uh yes, we have plenty at home.

In fact, my daughter likes l remote controls.

That.

Mm. To eat?.

To eat? Yeah, mainly, and to break..

So that could be a great um application.

Remote controls children proof. Mm mm-hmm.

Mm-hmm. Yeah.

Children proof.

Ye ye yeah.

Hmm.

So she likes uh buttons which make click,

Okay.

Yeah, pret

so it has to click.

So they have to be waterproof maybe?

It has to be uh wha baby proof.

'Cause they eat she ate it..

yeah but mainly it has to be very robust

Mm-hmm.

because even if she's not very tall she's uh high enough so that uh when she throw it away it's uh.

Yeah.

Ah.

So it has to be very robust.

Okay, unbreakable.

Unbreakable, yeah.

Uh-huh.

Yeah, we have some child lock or something, yeah.

Mm-hmm.

Mm-hmm.

And uh it has to be nice looking,

Yeah.

colourful, maybe.

Colourful, yeah mm.

Colourful? That's not practical.

colourful, because uh nobody has colourful remote control

Yeah.

No, that's a good idea.

Yeah, it's always black or yeah.

, they're always black, yeah,

Mm mm-mm.

No.

but this one could be I dunno, purple or b

Yeah..

But how gonna okay, just uh but it's uh monochrome it's n it's not like.

Yeah.

Yeah, yeah.

No,

Yeah.

because you think, why not.

Otherwise you will never find it.

One colour.

Yeah.

Yeah, yeah.

Yeah even we can change colours, no? Like the uh

Oh like the phones,

like the phones and these things we c yeah.

yeah, it could change colours, yeah.

Cool.

Mm-hmm.

At least for children like one colour and..

Ch

Yeah..

Good idea.

Good.

And it should be really small and.

Small also? Don't you think.

Huh not so big like yeah.

No uh, not too much buttons or mm.

Yeah, not too much buttons and.

Should it be, y you know these uh remote controls where um they are what they call a universal ret remote control

Mm-hmm.

so y you can decide that now it's the remote control for the television, then it's the remote control for the the sound system, or for your refrigerator

Uh. Yeah.

Yeah,

whatever.

that's.

I dunno if it's.

Or if we should have a targeted re remote control.

Okay.

So colour, robustness, easy to use, size,

So, I think there's.

yeah, size matters, yeah..

Yeah. Colour, size, sh

So you you think it's better if small than bigger.

Yeah, maybe at least n not bigger than this I guess..

Mm.

Yeah, but without any extremes like n not of this size, not too large. Okay.

No, not too small, yeah.

Yeah yeah, at least it should hold in your hand n properly, like.

Yeah.

Hmm.

Yeah, like a palm sized.

Yeah..

Just to hold it.

Mm.

Okay.

But uh what would be different from this, from the others? I dunno if.

Uh maybe we can change the colours that at least the frame. Mm. S so then it depends.

Yeah, at least the colour would be different.

you are to.

Mm.

Mm I mean you c you can easily remove the frame.

I think one thing important for instance in this remote control if you remember when people use it they're they never find a good button in the right place.

Mm-hmm.

Yeah, yeah.

For some reason they they they click the off button when they want to use the.

Yeah. Yeah.

So there's a problem in the design of that kind of remote control somehow,

Mm. So, some kind of idea uh with um um cellular phone with a a screen that will tell you what.

I dunno.

No,

no.

no screens, it's too complex.

Okay.

Mm. Too expensive, yeah.

Too expensive for twelve Euro?

And n maybe not too expensive,

And too expensive.

well it's not my problem, but well okay.

Ah.

But no screens on remote controls.

Mm.

Mm-hmm. I thought it could be only a screen which would change depending on uh uh the use or even the user.

Mm-hmm.

So ma I prefer to have the off button at the top right,

Ye yeah.

so I would have my own design of the remote control because it's in fact just a a full touch screen remote control,

Hmm.

if you if you like.

I mean it it's like

I don't know if it makes sense, but.

Mm-mm.

it's like two types no? people are right handed or left handed so y because I am left handed I use like this, say if you're right handed you use like this

Yeah, for instance, mm.

Mm-hmm, mm-hmm.

Mm-hmm. Mm mm mm.

or so tha your switch on and off should be on

Mm.

So adaptable yeah something.

Adaptable. Alright, good,

yeah.

Maybe, if if it's possible, yeah.

yeah.

Mm.

so how many actions do we need to implement in it?

huh.

On off?

Maybe I think even we can keep two switches and then we can uh only make one working.

Yeah.

We can adapt only one switch, suppose here like we can make two switches and if I'm left-hander I use this switch to follow the main operations.

I mean if it's less than three uh then we can make it uh like a

Two.

Three buttons you mean?

like three mental states,

Three option.

yeah you know what I mean,

Ah..

we can just make it uh

Yeah. Yeah.

Um.

controlled by a brain, huh?.

Mm-hmm.

Mm-hmm, yeah, sure..

Maybe if it's more, if there is a software inside

that ask you three.

Mm-hmm.

Hmm.

If it if we want a r universal remote control that we sa like we say before it may may need more than three mm three button, three mm possibilities, ye yeah.

Sh sure, sure.

Yeah, more than three actions that you may want to do at a given time.

Mm yeah. Yeah..

But for standard actions you usually what do you do, you change channels, you adjust volume, and nothing else.

Yeah but for instance when you change channels you can have you can just go to the next one or go to channel twenty five.

Mm. Yeah.

Mm..

And that's already more complex to go to channel twenty five..

Mm-hmm.

You do this?

Yeah.

Uh no.

I usually just change channels.

Because I'm only using three or four channels but.

Yeah. But they keep generally their t slash slash uh this thing and then the dash dash and then you can put yeah, you can only have one bit.

Yeah. I change channel like this, m uh I want to go to twenty five, and then to ten, uh-huh mm yeah.

Dash.

And then back to the one I was before,

Also we can be here.

so there's whichever it was.

Uh-huh.

Yeah you can

yeah, that would be cool.

yeah.

Go back button is good.

Yeah.

Yeah. Uh uh we had that in in other countries.

I once had it.

Yeah yeah, the previous button is.

Mm yeah.

Yeah e even the history so you could like uh undo previous of the previous..

Mm-hmm.

Uh, okay..

History..

Oh uh.

Then you can watch what your ah you could also record your record your sequence of actions,

Uh.

Yeah..

that becomes more complex,

but you could look at what uh the other people have used there or remote controls.

Yeah yeah, what the which channels the viewer.

Mm-mm.

Yeah.

Okay.

maybe it's a.

So I think we have full of idea.

Okay.

Um we're going to meet again in thirty minutes and uh I want you to mm work on these ideas and try to make a uh the ones, make um to decide what what are the ones important and what are the one that we don't want. And um m maybe more in the technical parts what uh do we want to do. Um. So um your personal coach will send you some instruction for for this thirty minutes.

So what does M_E_ means? M_E_ the user requirements? Or that's uh that's for us?

Market Expert.

Mm..

Marketing yeah.

Uh that's me.

Oh, of course yeah, the user requirement specifications, uh-huh, yeah.

Mm-hmm.

Okay. I'll think of that.

Mm okay. So.

So?

I think that's all.

Meeting's over?

Yeah.

Okay.

Great.

Thank you.

Thank you.

See you in thirty minutes.

Thank you everybody.

Question: What was decided regarding the screen of the new remote design?

Response: The participants decided that there should be no screens on the new remote control design.

Question: Is it a requirement that the new remote control should be childproof?

Response: Yes, the participants agreed that the new remote control should be childproof. They discussed features such as making it robust, baby proof, colorful, and having a child lock.

Question: Did the participants agree that the new remote control should be small in size?

Response: Yes, the participants agreed that the new remote control should be small in size. They discussed that it should be small enough to fit comfortably in a user's palm.

Question: Did the participants agree that the new remote control design should have a colorful rather than monochrome color scheme?

Response: Yes, the participants agreed that the new remote control should have a colorful color scheme rather than a monochrome color scheme.

Question: Did the participants agree that the new remote control design should have a touch screen to switch between different user profiles?

Response: The participants agreed that the new remote control design should have a touch screen to switch between different user profiles.

Question: What was their reasoning behind the decision to not have a touch screen for the new remote control?

Response: There is no explicit reason in the meeting transcript behind the decision to not have a touch screen for the new remote control.

Question: Why did the participants decide against having a touch screen display on the new remote control?

Response: There is no explicit reason in the meeting transcript behind the decision to not have a touch screen display for the new remote control.

Question: Did the participants agree that the new remote control should be unbreakable?

Response: The participants agree that the new remote control should be unbreakable.

Question: What were the overall proposed features discussed for the new remote control design?

Response:
Here are the overall proposed features discussed for the new remote control design:
* Colorful.
* Small in size.
* Robust and childproof.
* Have a maximum of 3 buttons.
* Have an adaptable on/off switch for left and right-handed users.
* Have a back button.

Question: Apart from the features you mentioned, were there any other decisions made regarding the buttons on the new remote control design?

Response: There were no other decisions made regarding the buttons on the new remote control design discussed in the meeting.