I just forgot their name, so uh you're i sorry, I just forgot them all. So I have to write it down.

Okay.

So.

Fine.

Do you know them or.

The names?

Yeah.

For for for my sur um

Yeah.

Jens.

Yeah, no, but your b your surname.

Uh Damman. D_ A_ W_.

W_O_ da. Okay.

Uh uh M_ M_. I mean M_. Double M_.

Okay. And what's your name?

Paul Wiezer. Paul Wiezer.

W_I_E_S_ z Z_ or S_?

A_ E_ Z_ zee zee

Uh uh zee. Okay.

E_ R_.

What's your name?

Uh Martijn.

Yeah, but your surname. Your surname.

What? Uh Abbing. A_ B_ B_ I_ N_ G_.

Okay, thanks.

Uh.

I was a little short on time,

Yeah, me too, so that's not.

but.

Yeah, same here.

No no no, I just fi first my.

Oh. Sorry.

Uh let's see. Which one was mine?

So let's have a look, we have forty minutes, so it's it's more than enough. Okay, perfect. So we have Oh no, what's that? So so we have uh forty minutes for this uh for this second meeting, and we have to make uh sure that we going t that we are sure, that we are,

Good.

that we know what we're going to make uh th what the product is going to like look like. Uh first I have the notes of the last meeting, so I showed uh show them to you. Oh, sorry about that, I just escape this one. How do I escape this? How do I I escape this s uh presentation?

What?

Uh left.

Uh.

Ah okay.

So.

Just Yeah.

And show, sorry. Okay, so let's have a look s at this one. Okay, so the f the f the points we had last meeting was the um Should be a univ uh universal remote control No, that's I uh s I just got a email from the from the personal coach and it should be a T_V_ remote control only. So have you changed that part?

Okay.

Okay..

Um so yeah, it still has to be uh f a r a remote control for kids and elderly. It's it's still the same. Um All these points uh we have to look at. You all know them. But uh there's another point. The um uh the main uh people of interest of this company are forty plus people. So they're old and not younger people. So we have to look at that as well.'Specially old people, maybe bi bigger buttons or something, I dunno.

Yeah, okay.

Uh so So yeah, that's it, so just you can do your presentation for uh.

Which one first?

Okay.

Oh it doesn't matter, just start with the.

Okay.

Mm. Uh.

Functional requirements, yeah

Okay. Well my name is Jens Damman, but we're in a group, and I I will start it. Wait. Um I've used a marketing report on uh the site. Uh I think you've uh read it too. Uh and uh f and furthermore I uh surfed the o the other site.

I I didn't read i read it, so it's not for me,

You didn't read it?

I didn't get it uh anyway.

No, I didn don't thing we got it.

It's only for you.

Oh okay, I I was the only one who get it.

Yeah.

Yes.

Okay it was uh uh uh um um a report about uh an experiment with uh a lot of users. And uh they had a lot of findings in their report uh with statistical uh uh uh thing uh with statistical uh proof. So I um I had three pages with findings and sev a lot of uh a lot of findings. So we can use this uh to uh create our own remote control. Uh seventy five percent of the users find uh most remote controls ugly. Yeah, I think uh uh that's a lot, so we have to make a beautiful remote control. Uh eighty percent of users would spend when uh a remote control will l uh look fancy. I think this fits uh at the uh what what uh Michael said about uh older people. Older people will uh spend more money uh for uh something uh uh what's good. Because younger people are more critical uh about uh uh where they spend their money money at. Uh seventy five percent uh seventy five percent of the users say they zap a lot. Well okay, that's uh normal. I think uh we we have to make uh good zap buttons. But that's one of our requirements.

The last point is quite an interesting.

Yes, fifty percent of users say they only use ten percent of the buttons.

So

Um Martijn alr already said it.

if we Yeah.

And uh maybe our uh fold open system is is a good one, but I don't think it's uh

Yeah, we should have the ten percent on the on the top,

reachable.

then you're you're.

Yeah, the ten percent on the top, yeah.

Yeah.

That that's a good one. Um uh page two. Remote controls are often lost somewhere in the room. That's exactly what we said about um maybe a home station for uh for it uh to uh recharge the batteries or something. Uh I thought mo maybe we could make a clap system, so when you clap your hands it will beep or something. Uh you must find it uh quickly.

Uh. Maybe just a button on the home station. So remote control beeps when you click that button on the home station.

Okay, yeah. Yeah, we can uh combine that. Uh it takes too much time to learn how to use a r new remote control. Uh I think we must t uh take a look at this. It's only uh th thirty four of the thirty four percent. But it's uh a tough one. Because if we make a ha whole new product, our own style, we we c uh this is so difficult, uh a difficulty I think. Uh next, remote controls are bad for R_S_I_. Yeah, but only if they zap a lot, and they watch over five hours T_V_ or something. I don't We we haven't Uh we mustn't look too much at uh the last point. Okay, last page.

Uh the last uh experiment uh was about gadgets, like uh speech recognition. We didn't uh think about that already. And uh an L_C_D_ on the remote control. We already thought about that. Um uh they finally had a conclusion that younger people um uh under an age of f forty five are uh more interested in new features. And but they're more critical. And older people uh want to spend uh more money. But uh they uh they don't want to uh have a lot of uh new features, because they're in their old uh thinking way. And they want to keep the old uh things the old things..

So we have uh a new uh age of forty plus.

But y But you but you already said that the uh company was about uh forty plus uh clients?

People. So yeah, so we just can skip the L_C_D_ r on the remote control,

I I think we can speak, uh we can skip speech recognition uh directly,

Yeah.

because Yeah.

because it's not reachable for twenty five Euros.

It's too Okay.

Yeah.

Um then I have my personal uh preference. Okay, that's not very good, because I thought about television, D_V_D_ player, stereo and V_C_R_. I had a question about. But it's already out of the question, this. Um my point is, well, I If we ma uh make a a remote uh control for only television, I think it's hard to uh sell it for twenty five Euros. But it's the exercise.

Because it's too expensive. Yeah, probably.

Yeah, only only for television uh On the market you can now buy for twenty Euros a remote control for everything. And we only make it for television, so we mi mm we m must made it make it uh very special.

Yeah, but good usability, so you can use it.

Okay, I told about the home station. Uh it must be simple, because uh our the the elderly people uh needs to use it. And I I found a motto. And we put the fashion in electronics. And that's uh the motto we are uh referenced to uh for our uh our style. We we have to make a a new product. We have to be um Yeah. One of a kind, I think.

So it has to look uh uh uh unique,

Unique.

The company is about our uh th th their own fashion, their own style.

but Yeah.

Yeah, I reckon.

But old people are not looking for that.

Uh I think mm.

Not really.

Uh.

Yeah. I think the main thing is the usability, that's where we can uh make it a special product.

Yeah.

Yeah,

Okay. Yeah.

to k to keep it simple when you.

But uh we also have to stand out,

Yeah.

Sorry I thought about it, yes.

'cause there are already, like you said, so many controls out that support lot of stuff. But we have to make sure that we're better usability, and stand out by just looks of it. So make it just a different colour or different shape,

Yeah. Okay.

so.

Okay, this was uh my presentation.

Thanks.

Okay.

I don't.

So

Yeah.

Paul, you can do the next one if you want.

You can ask some questions or something.

Okay. Yeah well Uh.

It's on the on the uh net net uh thing,

There it is.

isn't it?

Okay. Technical functions design. Okay, well, so we have s mm uh broad audience. Isn't that isn't true anymore. But um we have elderly people, so we need to keep it simple. Uh the way I want to keep it simple is to use the sen uh standard. So standardised uh methods like uh on all uh remote controls. Not too full, like uh Jens already said, only ten percent is being used.

So what do you think of only the numbers and the on-off button or.

Yeah, I have it on the next page.

Okay.

But yes uh basic functions like numbers and that sort of stuff. Um so the options that we put on there should be easy to use, and'cause you have more room then, and for elderly people big buttons. Uh an icon on it or text on it, so it's very clear what that buttons does. So it doesn't take much time to figure out uh how to use it. Um a way to find out uh what people use is maybe just to use uh questionnaires on the internet or just ask some random people, elderly people what they use, what they want on a remote control

Yeah.

to find out. But there is already in a one done. Of functions I could think of. Uh volume, channels, the the basic according to. Just one two three etcetera. Uh text service options. Um basic on-off. And I found an uh Could I think of favourites? I always look up the same um pages on text and always have to click the number.

Okay.

But if you could make a new option, that you just have to press one button and you get on your.

Yeah, but then you have to remember what favourite is what channel. So you might as well remember the number. Or not?

Maybe i maybe it's too complicated,

No y

but not sure.

Well

It's a good idea, but.

it's It was just a thought. So I'm, I u I would find it handy, I think, when you just press one button and you get on six six six.

Yeah, okay. But um how can you remember what uh channel uh connects to what favourite button?

Well uh

Uh.

what I was reading on the page. Uh a remote control just sends commands, basics commands to uh the television. So switch to channel six.

Yeah?

So uh button six says says six. And if you make favourites, it can say six six six in a row. Just numbers. That can be in the in the.

Yeah okay. But uh uh for a user to to remember, if I press that button it goes to that channel.

Well if you said a favourite.

Yeah, but isn't it hard to remember? Like favourite one and.

Uh mayb for me it's.

Well i

If I use my telephone, I never use those buttons to to to call sh Never.

Never? Oh.

No, neither do I.

So.

Oh are you?

And.

Okay now, m maybe not.

If I don't do it, maybe old people.

No, maybe elderly people uh. Right.

Yeah, I dunno.

They don't like new features. So maybe not.

Mm-hmm.

Um well play, pause. I dunno if that's usable

It's not, it's still not It's not anymore n uh.

when Not anymore for T_V_.

Right, on off. I dunno, miss Did I miss any other buttons, basic buttons? I couldn't think of any other,'specially not for T_V_.

Um.

Just on the front as well. No, that's the only th the only thing you need.

Is.

Uh uh the p uh next and previous. Previous I know, but next channel?

Just the channel um uh What I mean is uh.

I don't thi

Forward.

Li like a web browser,

Six seven eight or five.

so Oh, okay.

Just very simple.

But uh um there's also a button to uh go to the channel you've been before. Like a web browser back button.

Um I dunno.

Uh Yeah,

I don't have Uh I did.

I know what it is, but I think it's all too difficult for old people..

Yeah,

No. Uh.

I don't think you use that. Y only when you want to go to.

Yeah, okay.

Yeah, just use uh when you wanna switch between channels all the time.

And how do you want to uh do it, like if you have a channel above ten?

Yeah.

Normally you can press one, zero or.

Yeah, I think um f Especially for older peo elderly people use standard buttons. So you have that uh.

A ten plus or.

Yeah, the ten plus button. Just uh one one pressing, or quickly after each other.

Okay.

To just keep it simple and standard uh features.

Yeah, w wouldn't it be a problem to uh Because you h have to be fast enough. Maybe the elderly people.

Uh yeah mayb

It gets some seconds.

But I think that's in the T_V_ as well. That's how the uh the T_V_ T_V_ handles it. But you can have a button that says um two two st two stripes.

Oh okay. Yeah.

Yeah, it's it's no it's not a.

So you have uh like a five seconds period to press those two buttons.

Yeah, but that's th mm.

It's what Paul says. It's not a remote control uh uh that um uh makes the T_V_ um do one two. It's the T_V_ who depends that it must be two seconds or three seconds or one second that you must uh type one or two.

Okay.

So if you have a universal T_V_ controller, you needed one button that has two uh stripes.

Yeah.

So we have a a period of, I dunno, five seconds to press those buttons, and that And not for elderly people to look, one two uh press and aim and.

Yeah. Yeah.

But do we still need a two level remote control? Because if we only have that l only f

It's only for television now.

So I I don't think so.

No.

Uh I just thought of another one.

No.

Most things in modern T_V_s are also on the menu.

Yeah.

So you also need a menu button. And then uh navigation uh But we can integrate that with volume and previous and next,

Yeah.

so you have

I I think you ha really have to divide between functions you often use, like um uh m maybe uh switch channels and uh volume and k that kind of things, and uh the menu button.

four arrows.

Because you n almost never use menu button.

Yeah, okay,

So.

but um.

Maybe it's still still a good idea, I'm not sure. You'll also have to use a mute button to to.

Yeah, a mute button. Yes.

Maybe, not.

Don't think so.

I don't know where where you have to put it.

Yeah well,

that's that's I think that's the layers that produce. Now I have to figure out what's uh what to put on. What we're gonna use.

Okay. Yeah. Yeah. That's right.

And later we can d uh do the design.

Okay.

Okay, uh now my personal preferences. Uh using the standards, basic Um I think that we should stand out uh unique, being unique with the design. So we have to, I dunno, uh make a different shape than usual. So when you are in the shop and you see our T_V_ controller hanging, that it stands out. Not just in in the row when you see all the same uh remote controls.

Yeah. Does it have to be Uh it has to be uh with different colours

Um I dunno. Different colours um.

or.

Maybe we can give it out in different colours. You can choose blue or yellow

It's like a iMac or something.

or.

Just to make it.

Mm-hmm.

Yeah. Well why not? Or Yeah.

That's fa That's fancy. That's uh fashion.

But it looks cheap as well, because it's a small thing. It's only twenty five Euros. It looks very cheap if you make it.

Well, maybe you can look at uh mobile phones.

Oh, you just I dunno what happened.

Oh.

Who?

Yeah, we have to look at mobile phones, that's right. Just.

They they're uh designed very well.

Yeah.

And well basically are the same, just a bit smaller. You think you can't make a T_V_ controller too small,'cause then you will always lo always lose it.

Yeah.

But uh well, I think that's a good example.

Okay,.

Okay.

we hurry up a bit, because otherwise we won't make it.

Okay, sorry.

Is it fin Are you finished?

Well uh.

Okay.

Yeah, I I'm finished. I think we discussed everything.

Okay. Well, the working design. The method I used is uh search the web. Just the web page provided. Um Basically I'm I'm not very technical uh uh educated, but uh I could figure it out. Um basically what happens is you press a button, uh then a circuit gets closed by pressing the button, uh like a switch. And by closing that certain circuit, the chip knows uh what bu button is pressed. So like you press a one, that circuit is closed and uh Then the chip produces a pattern. Like a Morse code to uh And and sends that to the uh L_E_D_. That's the uh light emitting diode, I think. Um and the light emitting diode is uh producing infrared light. That's un uh invisible to the human eye. And uh transmit that uh to the T_V_. However it has also an uh a visible uh diode that's uh blinking red if you look in it. And that's uh another diode, I believe.

Mm-hmm.

Because infrared is not visible. So that's er uh do two different things

So we we also have to have a LED li LED light on it?

but Yeah. I I think so..

Uh j Is.

I I'm not sure if it's the same thing.

Yeah, it's I think it's usable.

Ain't it just to to indicate that it's transmitting?

Yeah, that's active.

Yeah.

Yeah.

Or just a green one,

So.

because it's If you use it, it's green or the red, it's r green.

Yeah. Maybe uh.

I I think it's in the case that it's active. It's not uh.

Yeah, that's right.

Yeah, when you press it.

But if you.

So.

I it's it's just uh the the.

Red's l shows up like something's wrong,

Two?

and green is like it's okay, you press the button.

Mm.

Yeah, I dunno.

Maybe depends on uh.

Ma on on most on most uh uh remote controls it's red.

Yeah, I know.

Yes.

So.

Yeah.

Okay, we make it red.

But maybe.

So

Well

I know.

we don't have to make it red. Maybe integrate it in the design as well.

You have to The whole the whole uh remote control becomes uh green if you press the buttons. Uh that's cool..

I I think uh the batteries will be uh a little.

Uh Oh,

Empty.

maybe it's it is would an e No. Just uh.

No, we have a recharger in it, so.

Oh.

Yeah, okay.

If we can still make that then.

Uh maybe.

Yeah, but it doesn't have to be red.

It's just to indicate something's on.

Yeah.

Yeah. That it's working. That it's not not the batteries are low.

But it's cool if it was green.

Yeah. It's it's not very important,

Green or red or whatever, it is cool..

so yeah.

Yeah, I know. Just to indicate it's working.

So mm.

And uh the receptor in the T_V_ senses the pattern. So we have to understand what patterns are used to, you know, to make it universal. So that it can be used with all the T_V_s. We have to really understand what patterns are used, so we can uh o On the Otherwise it won't work.

So we have to uh make buttons for that as well, to make it uh.

No. I I uh The chip um uh is producing the pattern. So we have to make a chip that produces patterns to, y you know, to.

Okay.

Yeah.

An automatically search function for each television, or something.

That are working. Yeah, or I dunno how it's uh exactly how it works. It wasn't explained there.

Well, I I use a universal uh remote control,

Uh I kno

and list of all the T_V_s you have, etcetera. And you have to put in a number, so it works on your T_V_.

Yeah? Okay.

Oh, I have a modern one.

And the modern one you you uh you type uh search, and the LED began to blink blink blink. And uh uh uh after uh a short time the television turns off. And then you know, oh it it's the right one. And you can stop it,

Ah okay.

and then it's okay. So you don't have to search for your television or your code.

Okay.

It uh search uh the pattern for itself.

Okay,

Okay, yeah.

so we use that.

So it uh We have to make a a s a bit more intelligent chip than the standard one. Because you have to.

Yes.

Yep.

Yeah.

I just say Can you s just say it again, because I was just looking There's just a short.

Uh okay. Well uh y you have this chip.

Yeah.

That's uh when the circuit is closed, it produces the pattern.

Mm-hmm.

But uh ma basically for uh brands of T_V_ these patterns are different. So like when you press a one on one T_V_ it go go to one. And on the other T_V_ it won't work, basically. So uh basically what you have to do is uh get these patterns right.

Yeah.

And uh by d uh that can be done by uh just uh cha yeah, changing this pattern all the time.

Changing the signal.

And um What he's doing when he's configuring this pattern, this chip, uh is trying to switch off the television. And when it's uh switched off, you can push a button as uh it's working now, so And then he saves that setting

Okay. Yeah.

and then um it's working.

Yeah, that's the right uh option.

Okay. Well uh the components.

Yeah, that's a bit technical, and I hadn't I w I was a little short on time. Um but I think I understand it. Um the energy source is uh the battery, basically.

Yeah.

Um that's connecting to all the components.

Because it has to be fed with energy. Okay. Uh the subcomponent is uh w I I think it is uh the button pressed. So basically when you press a button, a switch get closed. Um that's connecting to a chip. So the chip knows what button you pressed. And the chip sends out uh the pattern to the infrared bulb. I didn didn't put the description by this one. Th this is a normal bulb. So the normal flashing light.

Okay. So that's the LED, L_E_D_.

Yeah. Yeah, and this is a LED too.

But this one is producing infrared light

Yeah, okay.

that's invisible. And this one is producing normal light.

So we can make the normal one also a normal light. Not a LED light, but as a normal one. To flash up your.

Yeah.

Uh if you if you use the buttons, uh both of them works. But one you can't see and one indicates that you use a button.

Yeah.

Yeah, presu Yeah yeah yeah. Because uh when you button press a button and it doesn't work, it can Basically if the battery is low, it won't work.

Yeah. Yeah, and.

Maybe you have to uh put uh a small light around the button you push. So you see green if you push that button.

Huh, that's a good idea.

Mm I think it's unnecessary power uh you use then.

Yeah, but if you u if you do that, you know that you're uh sending a signal.

No, y Yeah.

Okay.

And it's you also know which button you p

But then have to be on the in the all uh remote control have to be LEDs.

Yeah, that's right. But it.

Everywhere in the r

But does it make any difference for the energy you use? You got still one LED.

No I don't think so, but Yeah, i it it will look different, and I think we need to find something else.

That looks different, yeah.

Yeah.

Hmm.

'Cause otherwise we will just make another standard, and our uh motto also is.

Is LEDs uh beneath the the buttons?

Yeah, w around the buttons, or in the buttons even.

Yeah, okay.

Yeah, but mm like when you push it

Yeah, then then won't Then you won't see it.

n n Yeah. You have your finger over the button. So you can see.

It must be around it then.

Yeah. Or or m maybe on top of the A green light is flashing

Yeah, then.

or.

Yeah. Not not not not here, but here.

Yeah. There. Yeah.

Maybe uh.

The same as a telephone, or a mobile phone, or what do you mean?

Yeah, we're thinking about it.

If you push.

Uh on a mobile phone, in the dark uh

It lights up. Everything lights up. That's a good idea.

everything lights up. Why ain't that on a remote control?

Yeah.

It it only takes a l a little energy and it's not that much.

Yeah, if uh a mobile phone a phone can do it, it's.

Okay.

It's f Yeah.

Yeah.

Maybe that's a good idea for old people as well.

Yeah, i if you're in the dark, you can't see the remote.

Mm-hmm.

It's only few LEDs. Only four or something. Four LED.

Yeah.

No, I dunno. But if we use a battery station, which I think we will use.

Yeah,

Yeah, that's a good idea, okay.

I uh.

Yeah. We'll have enough power

Everything agre

to.

Everyone agrees with that, or Okay.

Yeah.

Yeah.

Yep.

Maybe what Paul said, uh under the on the on the home station, uh a button to uh to call your uh remote control, that it beeps.

Yeah, and then you Okay, yeah. But you have to make a sound device in it then.

And also it's.

Yeah, there must be sound in it.

But.

Hmm.

Yeah.

Okay, it shouldn't take much.

I dunno.

Yeah. B maybe it's that that's a little too hard to make. Especially for that kind of money. Because it's i it has to be.

Mm uh, twenty five Euros, I think we can make it.

Uh I th

Yeah. Production cost is uh t uh twelve and a half.

Bec Twelve and a half, okay. But but we only have to make it for television, and um we must have something special.

Yeah, okay.

I think uh you also have uh remote controls with a lot of options.

So.

But we lose about ninety percent of those options. So I think you can uh.

Uh. We have to give our customers some extras.

Yeah. I think we will save money with that.

Yeah, okay.

Okay. Are you almost finished or just.

Yeah, the the personal preference, I didn't fill it out.

Okay. Okay.

Because I was short on time.

No worry.

But um Basically what we could do too is uh have a Bluetooth uh integration.

Yeah, I just want to talk some about some more. So maybe you have to Yeah?

Yeah?

Okay.

Okay.

So this Oh, sorry. Wh what's that? So uh Oh, sorry. We have some new uh project requirements. We have to have a look what they are. They're still in um Uh teletext has become outdated since the popularity of internet. So a teletext option, maybe we have to skip that one. I'm not sure. I don't think so,

No,

but.

and I I think a lot of people use uh teletext, still use.

No.

Yeah, and it's it's on your comp it's on your television. It's only one button. So I don't think it is.

Yeah.

Uh.

We definitely should use it. Um.

Hmm.

Yeah, that's uh what I told you. The remote control should only be used for a television. So that's maybe easier. And um the the forty plus people, I already told you. Oh no, sorry. Oh, this is a problem. Oh sorry about that. The new product should reach a new market with customers are the younger than forty. So it should be flashy or just more interesting.

Okay.

Oh. Mm.

Okay, that's It changes things.

I'm sorry about that. I just I just didn't read it well.

So does it make some decision about that?.

Changes.

Yeah.

Um.

That kinda changes the whole situation.

Uh.

Well yeah, then we have to make some nice features. I think uh the thing in the dark is a good way,

That still stays.

to make it more like a mobile phone. More modern.

Yeah.

Yeah.

That's important I think and.

L_C_D_ doesn't work for uh twelve and a half Euros I think.

And I d I don't don't think it's very usable in a remote control, especially when you only have T_V_ functions on it.

Oh.

Yeah.

So.

Mm ah I I don't y you'll use it often, because you can see on the television wh what channel you.

Oh, I'm watching uh the channel one. Okay. No, it's not.

Yeah, I tho I think that's not usable.

No.

So, but uh Yeah. What other features can we put in?

Yeah. I think you you have to make it a bit flashy and and popular. And uh then the usability is not that required, because the Like in the mobile phones, usability is not that good I think.

But uh some of you had uh something to read about um uh speech uh recognition. About you said one and the television turns on one. Is that reachable maybe?

I didn't read.

That's very That's fancy. That's cool.

It's very fashion.

I didn't read any

Twelve and you've got twelve.

b Yeah. Yeah okay.

Mm.

I I know.

Only the numbers, only numbers. Uh furthermore nothing. But only the numbers, one to twenty or something.

Mm.

That should be cool.

Yeah, maybe we have to integrate that as well. If it's possible. But.

If it's possible,

Yeah,

I don't think it's very expensive actually.

I don't know.

I dunno. I didn't read it.

Mm.

Why should it?

Yeah, I think it's.

Uh if i if i

You only have a microphone in it.

Yeah.

I didn't have information about that.

But it has to work. And and and does it have to work only in English, or in Dutch too or.

Nah, maybe.

Ah that's a problem, but.

And and uh w Yeah?

Uh only in English. Only in English I think.

What But uh I dunno how that works then. Uh does your uh Does it lie in the centre of the room and can you scream from wherever

Yeah, it's probably my job to figure that out, but.

uh one to have in channel one?

No, that Yeah, then th we have to think about that. But do do we do it?

Yeah,

It's more if we if we do it.

I dunno. So, is it very usable? That's what I'm looking at.

And and do you have to speak in in like in a thing

Yeah.

or.

Maybe maybe it's too hard to uh to realise that.

Is.

It We have short time to to put it on the market,

Yeah, I dunno.

so that We have to do a lot of testing before we can do that.

Mm Yeah well.

Okay.

Yeah, I think so..

We make uh make uh, we can make th th the new remote control very flashy..

Yeah, and uh and uh.

It's.

In uh indeed uh the languages are a difficult thing. Uh because we we have a lot of languages.

Yeah.

I d Yeah. I don't think it's uh useful enough to take the effort to design something with uh speech recognition.

And and.

Yeah. I think so.

Yeah, and also if if you have a good speech uh speech recognition, you can just throw the uh.

Yeah. A uh someone says that uh give me one Coke, and the T_V_ turns uh to one.

the remote away. Mm. Hmm. Mm. Yeah, okay.

Yeah,

Yeah. Well yeah,

it's not uh.

that's the right command. And then you have to say uh T_V_ channel one, or something. Not just one,

But Hmm yeah, T_V_ one.

but.

Okay, but th that becomes your f Uh uh because that's very hard for uh speech recognition.

Well I dunno. But I don't think, it just ain't useful enough.

Yeah.

So

Mm-hmm.

we have to make some decisions. So

Okay, no speech recognition.

you can see on the uh So we have to know what we're going to put on. Do we, do we make uh a light under the under the n under the numbers and everything? Do.

Yeah, I think so.

If you press something, it lights up for a few seconds, so you can see what other but buttons there are, okay?

Yeah, that's good.

Okay, we just take that one.

Yep.

And what else, we have.

Also the one in the dark. So uh It lights up when it's dark?

Yeah, but it's Oh th I thought would, that that would be the same.

I didn't.

If you push something, it it all lights up.

Okay.

Yeah. Yeah yeah,

Yeah, but um.

it it mustn't work all the time.

Yes, that's what I mean.

It uh uh it have to work only when you use it.

Or you can switch it on or something.

So if you.

Or maybe when you.

No, if you use one button, it must turn uh on for twenty seconds, and then it must turn off.

yeah. It lights up all.

Yeah, that's right. You have to Yeah, that's right. That's what I said. It's the same as the telephone. Yeah.

Okay yeah. D Yeah, alright. Yeah.

And do we use a a Uh what's it called? Like a iMac, if you can look through it. Or just a normal remote control.

Maybe just as an option, w like we discussed, like iPod.

Mm, maybe it's a good idea.

Different colours, uh maybe use even different fonts.

Yeah.

Like uh phones.

Oh yeah.

Different colours. Okay,

Hmm.

so y you just make it th through You look through it?

A see-through. Mm, that's cool.

Yeah. Uh as an option maybe.

Okay. And so the buttons we have, this is, yeah, this is normal. We put in the the simple buttons on the top,

It's the standard.

and probably the the the more complicated buttons down there.

Yeah, uh Yeah,

But you have You had a lot of different buttons.

we but we don't We don't really have any complicated buttons.

Hmm.

Yeah, maybe you have you have to Uh when you use teletec teletext, you can press a green or a red button to go to the next one, or to go to the previous one.

Yeah okay, but Yeah, well w I think the buttons are very easy. With just uh standard buttons we just have so little.

O but maybe you can put um the g the options of teletext on the second level of the remote control. Because you ne almost never use it.

No, I don't need don't need Uh teletext options are only four buttons or something.

Yeah, but younger people I think um more often use the internet and elderly people often use teletext.

Yeah, that's right.

I use teletext as well.

So uh.

No, I use te teletext every day, I think.

Hmm. Okay.

Yeah. For me too, it is. So we just keep it one level then? Okay.

Yeah yeah, one level. And I think, uh'cause um we don't have that many buttons,

Mm.

I think the design is most important. You can d uh integrate the buttons in the design very much.

Yeah.

Yeah.

'Cause simple buttons.

Okay.

If you put one above it, it's clear.

Okay. So that's it for toda for We're going to have a lunch lunch break. Um.

How long is lunch break?

So yeah, you know what you have to do. This is uh this is it. You get your meal and everything, so.

Trendwatching. Okay. That's fine.

Yeah.

Yo.

So, we're finished for t for this time. We're going to have some lunch.

Okay.

Bye.

Yeah.

Yeah..

Mm. Uh.

Question: What were the key conclusions after the meeting was over?

Response: The participants decided to keep the teletext option, despite it becoming outdated, as many people still use it. They also decided to include speech recognition as a feature, but only in English. Additionally, they opted for a see-through design as an option, similar to the iMac, and to integrate the buttons into the design as much as possible.

Question: And what about the colors?

Response: The participants considered offering the remote in different colors as an option, much like the iPod. They also discussed using different fonts for the buttons, again as an option.

Question: Why was the decision made to include speech recognition as a feature, but only in English?

Response: The participants decided to include speech recognition as a feature, but only in English, primarily due to the time constraints of the project. Implementing speech recognition in multiple languages would require extensive testing and development, which would delay the product launch.

Question: Besides the languages, were there other concerns about implementing speech recognition as a feature?

Response:
The participants had concerns regarding the accuracy and practicality of speech recognition as a feature:

* It could struggle to decipher commands in noisy environments.
* It would require the user to speak in a very specific manner in order for it to work properly.
* The user would have to be in close proximity to the device for it to recognize their voice.

Question: What benefits did the participants expect to gain by implementing speech recognition as a feature, despite the concerns they had?

Response:
The participants expected to gain the following benefits by implementing speech recognition as a feature:

* Increased user convenience and ease of use.
* Hands-free operation of the remote control.
* Enhanced accessibility for users with disabilities.
* The ability to control the TV from a distance or in low-light conditions.

Question: Did the participants decide to include an LCD screen on the remote control?

Response: No, the participants decided not to include an LCD screen on the remote control due to cost constraints. They wanted to keep the price of the remote control competitive, and adding an LCD screen would have increased the production cost significantly.

Question: Besides the cost, were there other concerns regarding the inclusion of the LCD screen?

Response: There was concern that an LCD screen would not be very useful on a remote control with only TV functions.

Question: What was the objective of the meeting?

Response: The main objective of the meeting was to discuss the design and features of a new TV remote control. The participants aimed to create a product that appealed to a younger audience, while still considering the needs of older users.

Question: Why was the decision reached to keep the teletext option?

Response: There were concerns that the teletext option had become outdated and less commonly used, especially by younger people. However, after discussing the matter, the participants decided to keep it as many people still use it, particularly older users.

Question: Did any of the participants express disagreement with Martijn regarding the usefulness of an LCD screen on the remote?

Response: There is no mention of Martijn expressing disagreement with the usefulness of an LCD screen on the remote.


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 some of the open questions that arose during the meeting that were not answered?

Response:
The participants discussed the following questions that were not answered:
* Whether the neural network trainings or the HTK runs were slowing down the research.
* What software to use for large vocabulary training slash testing for tandem systems.
* How much more parameters can be added to the neural network within the computational limits.

Question: What did they conclude based on their discussion on the VAD?

Response:
**Summary of discussion regarding VAD:**
* The participants agreed that VAD, or voice activity detection, is a useful technique for removing silent sections from speech data.
* However, there was some discussion about how to implement VAD in the context of the current evaluation. The participants were not allowed to modify the recognizer scripts, which limited their options.
* The participants also discussed the possibility of inserting some kind of marker to indicate silence regions, but they did not reach a consensus on how to do this.

Question: Besides the issue of implementing VAD, what other challenges did the participants discuss regarding the evaluation of tandem systems?

Response: The meeting notes did not discuss any challenges regarding the evaluation of tandem systems, apart from the VAD implementation issue.

Question: What did the participants think about the use of streaming video for the next meeting?

Response: The meeting transcript provided does not mention anything about streaming video, so I cannot answer your question.

Question: What was the main goal the participants aimed to achieve by arranging this meeting?

Response:
The main goal of the meeting was to discuss the ongoing research on tandem systems for speech recognition. The participants aimed to:
* Review the progress of the research and identify key challenges.
* Discuss potential solutions to these challenges and decide on the next steps for the research.
* Coordinate efforts between the two research sites involved in the project.

Question: Did the participants reach a consensus on how to implement VAD in the context of the current evaluation?

Response: The meeting transcript did not mention anything about streaming video or reaching a consensus on how to implement VAD.

Question: What were Professor B's thoughts on the use of LDAs in tandem systems?

Response: Professor B did not express any thoughts or opinions regarding the use of LDAs in tandem systems during the meeting.

Question: Apart from the computational limits, what other factors did the participants consider when deciding on the number of parameters to add to the neural network?

Response: The meeting transcript did not mention any factors considered by the participants when deciding on the number of parameters to add to the neural network, other than computational limits.

Question: What were the main observations of Professor B regarding the results of the tandem approach tests?

Response:
* Professor B observed that:
  * If the network is trained on the task data, tandem works pretty well.
  * When trained on a single language but a different task, the error rate increases by approximately 10 percent relative to the baseline.
  * Jumping to multilingual data worsens the results further by approximately 20 to 30 percent.
  * Surprisingly, excluding English from the multilingual data does not make much of a difference in the results.

Question: What was decided regarding which software to use for large vocabulary training slash testing for tandem systems?

Response: The meeting notes did not mention any decisions made regarding which software to use for large vocabulary training slash testing for tandem systems.