Is it  ? Um , is it like a  Gaussian mixture model ?

It 's the same system that they use  when they participate in the Hub - five evals. It 's a ,  um  sort of  came out of , uh  uh , looking a lot like HTK. I mean , they started off with  um , when they were building their system they were always comparing to HTK to make sure they were getting similar results. And so ,  it 's a Gaussian mixture system , uh


It 's the same system that they use  when they participate in the Hub - five evals. It 's a ,  um  sort of  came out of , uh  uh , looking a lot like HTK. I mean , they started off with  um , when they were building their system they were always comparing to HTK to make sure they were getting similar results. And so ,  it 's a Gaussian mixture system , uh

Do they have the same sort of mix - down sort of procedure , where they  start off with a small number of some things


It 's the same system that they use  when they participate in the Hub - five evals. It 's a ,  um  sort of  came out of , uh  uh , looking a lot like HTK. I mean , they started off with  um , when they were building their system they were always comparing to HTK to make sure they were getting similar results. And so ,  it 's a Gaussian mixture system , uh

Do they have the same sort of mix - down sort of procedure , where they  start off with a small number of some things

I don't know. Yeah. And then  divide the mixtures in half.


Do they have the same sort of mix - down sort of procedure , where they  start off with a small number of some things

I don't know. Yeah. And then  divide the mixtures in half.

I don't know if they do that. I 'm not really sure.


I don't know. Yeah. And then  divide the mixtures in half.

I don't know if they do that. I 'm not really sure.


I don't know if they do that. I 'm not really sure.


I don't know if they do that. I 'm not really sure.

D Do you know what kind of tying they use ? Are they  they sort of  some sort of  a bunch of Gaussians that they share across everything ? Or   or if it 's  ?


D Do you know what kind of tying they use ? Are they  they sort of  some sort of  a bunch of Gaussians that they share across everything ? Or   or if it 's  ?

Yeah , th I have  I  I  I don't have it up here but I have a   the whole system description , that describes exactly what their  system is


Yeah , th I have  I  I  I don't have it up here but I have a   the whole system description , that describes exactly what their  system is


It 's some kind of a mixture of Gaussians and ,  uh , clustering and , uh  They 're  they 're trying to put in sort of all of the standard features that people use nowadays.


It 's some kind of a mixture of Gaussians and ,  uh , clustering and , uh  They 're  they 're trying to put in sort of all of the standard features that people use nowadays.

So the other , uh , Aurora thing maybe is  I I dunno if any of this is gonna   come in in time to be relevant , but , uh , we had talked about , uh ,  Guenter  playing around , uh , uh , over in Germany


So the other , uh , Aurora thing maybe is  I I dunno if any of this is gonna   come in in time to be relevant , but , uh , we had talked about , uh ,  Guenter  playing around , uh , uh , over in Germany

and  and , @ @  uh ,  possibly coming up with something  that would , uh ,  uh , fit in later. Uh , I saw that other mail where he said that he   uh , it wasn't going to work for him to do CVS.


So the other , uh , Aurora thing maybe is  I I dunno if any of this is gonna   come in in time to be relevant , but , uh , we had talked about , uh ,  Guenter  playing around , uh , uh , over in Germany

and  and , @ @  uh ,  possibly coming up with something  that would , uh ,  uh , fit in later. Uh , I saw that other mail where he said that he   uh , it wasn't going to work for him to do CVS.

Yeah. Yeah. So now he has a version of the software.


and  and , @ @  uh ,  possibly coming up with something  that would , uh ,  uh , fit in later. Uh , I saw that other mail where he said that he   uh , it wasn't going to work for him to do CVS.

Yeah. Yeah. So now he has a version of the software.

So he just has it all sitting there. Yeah.


Yeah. Yeah. So now he has a version of the software.

So he just has it all sitting there. Yeah.

So if he 'll  he might work on improving the noise estimate or on  some histogram things , or


So he just has it all sitting there. Yeah.

So if he 'll  he might work on improving the noise estimate or on  some histogram things , or


So if he 'll  he might work on improving the noise estimate or on  some histogram things , or

Yeah. I just saw the Eurospeech  We  we didn't talk about it at our meeting but I just saw the  just read the paper. Someone , I forget the name ,  and  and Ney , uh , about histogram equalization ? Did you see that one ?


Yeah. I just saw the Eurospeech  We  we didn't talk about it at our meeting but I just saw the  just read the paper. Someone , I forget the name ,  and  and Ney , uh , about histogram equalization ? Did you see that one ?


Yeah. Um   It was something  similar to n  on - line normalization finally  I mean , in  the idea of  of normalizing


Yeah. Um   It was something  similar to n  on - line normalization finally  I mean , in  the idea of  of normalizing

Yeah. But it 's a little more  it  it 's a little finer , right ? So they had like ten quantiles


Yeah. But it 's a little more  it  it 's a little finer , right ? So they had like ten quantiles


So you  you have the distributions from the training set ,


So you  you have the distributions from the training set ,

and then , uh  So this is just a  a histogram of  of  the amplitudes , I guess. Right ? And then   Um , people do this in image processing some.


So you  you have the distributions from the training set ,

and then , uh  So this is just a  a histogram of  of  the amplitudes , I guess. Right ? And then   Um , people do this in image processing some.

You have this kind of   of histogram of  of levels of brightness or whatever. And  and  and then ,  when you get a new  new thing that you  you want to adjust to be  better in some way ,  you adjust it so that the histogram of the new data looks like the old data.


and then , uh  So this is just a  a histogram of  of  the amplitudes , I guess. Right ? And then   Um , people do this in image processing some.

You have this kind of   of histogram of  of levels of brightness or whatever. And  and  and then ,  when you get a new  new thing that you  you want to adjust to be  better in some way ,  you adjust it so that the histogram of the new data looks like the old data.


You have this kind of   of histogram of  of levels of brightness or whatever. And  and  and then ,  when you get a new  new thing that you  you want to adjust to be  better in some way ,  you adjust it so that the histogram of the new data looks like the old data.

You do this kind of  piece - wise linear or ,  uh , some kind of piece - wise approximation. They did a  uh one version that was piece - wise linear and another that had a power law thing between them   between the  points. And , uh , they said they s they sort of see it in a way as s for the speech case   as being kind of a generalization of spectral subtraction in a way , because , you know , in spectral subtraction you 're trying to  get rid of this excess energy. Uh , you know , it 's not supposed to be there. Uh   and , uh , this is sort of   adjusting it for  for a lot of different levels. And then they have s they have some kind of ,  uh ,  a floor or something ,


You do this kind of  piece - wise linear or ,  uh , some kind of piece - wise approximation. They did a  uh one version that was piece - wise linear and another that had a power law thing between them   between the  points. And , uh , they said they s they sort of see it in a way as s for the speech case   as being kind of a generalization of spectral subtraction in a way , because , you know , in spectral subtraction you 're trying to  get rid of this excess energy. Uh , you know , it 's not supposed to be there. Uh   and , uh , this is sort of   adjusting it for  for a lot of different levels. And then they have s they have some kind of ,  uh ,  a floor or something ,

so if it gets too low you don't  don't do it.


so if it gets too low you don't  don't do it.

And they  they claimed very nice results ,


And they  they claimed very nice results ,

So is this a histogram across different frequency bins ?


So is this a histogram across different frequency bins ?


So is this a histogram across different frequency bins ?

Um , I think this i You know , I don't remember that. Do you remember  ?


Um , I think this i You know , I don't remember that. Do you remember  ?

I think they have , yeah , different histograms. I uh  Something like one per  frequency band ,


I think they have , yeah , different histograms. I uh  Something like one per  frequency band ,


But I should read the paper. I just went  through the poster quickly ,


But I should read the paper. I just went  through the poster quickly ,

And I don't remember whether it was  filter bank things


And I don't remember whether it was  filter bank things


And  and that  that , um ,  histogram represents  the  different energy levels that have been seen at that  frequency ?


And  and that  that , um ,  histogram represents  the  different energy levels that have been seen at that  frequency ?

I don't remember that. And how often they  you 've seen them. Yeah.


I don't remember that. And how often they  you 've seen them. Yeah.

Yeah. And they do  they said that they could do it for the test  So you don't have to change the training. You just do a measurement over the training. And then , uh , for testing , uh , you can do it for one per utterance. Even relatively short utterances. And they claim it  it works pretty well.


Yeah. And they do  they said that they could do it for the test  So you don't have to change the training. You just do a measurement over the training. And then , uh , for testing , uh , you can do it for one per utterance. Even relatively short utterances. And they claim it  it works pretty well.

So they , uh  Is the idea that you  you run a test utterance through some histogram generation thing and then you compare the histograms and that tells you  what to do to the utterance to make it more like  ?


So they , uh  Is the idea that you  you run a test utterance through some histogram generation thing and then you compare the histograms and that tells you  what to do to the utterance to make it more like  ?

I didn't read carefully how they actually implemented it ,


I didn't read carefully how they actually implemented it ,


I didn't read carefully how they actually implemented it ,

whether it was some ,  uh , on - line thing , or whether it was a second pass , or what. But  but they   That  that was sort of the idea.


whether it was some ,  uh , on - line thing , or whether it was a second pass , or what. But  but they   That  that was sort of the idea.

So that  that seemed , you know , different. We 're sort of curious about , uh , what are some things that are , u u um ,  @ @   conceptually quite different from what we 've done.


So that  that seemed , you know , different. We 're sort of curious about , uh , what are some things that are , u u um ,  @ @   conceptually quite different from what we 've done.

Cuz we  you know , one thing that w that , uh , Stephane and Sunil seemed to find ,  uh , was , you know , they could actually make a unified piece of software that handled a range of different things that people were talking about , and it was really just sort of setting of different  constants. And it would turn , you know , one thing into another. It 'd turn Wiener filtering into spectral subtraction , or whatever. But there 's other things that we 're not doing. So , we 're not making any use of pitch , uh , uh , which again , might  might be important , uh , because the stuff between the harmonics is probably a schmutz. And  and the ,  uh , transcribers will have fun with that. Uh   And , um , the , uh , stuff at the harmonics isn't so much. And  and , uh  And we there 's this overall idea of really sort of matching the  the hi distributions somehow. Uh , not just , um ,  um  not just subtracting off your estimate of the noise. So. So I guess , uh ,  Guenter 's gonna play around with some of these things now over this next  period ,


Cuz we  you know , one thing that w that , uh , Stephane and Sunil seemed to find ,  uh , was , you know , they could actually make a unified piece of software that handled a range of different things that people were talking about , and it was really just sort of setting of different  constants. And it would turn , you know , one thing into another. It 'd turn Wiener filtering into spectral subtraction , or whatever. But there 's other things that we 're not doing. So , we 're not making any use of pitch , uh , uh , which again , might  might be important , uh , because the stuff between the harmonics is probably a schmutz. And  and the ,  uh , transcribers will have fun with that. Uh   And , um , the , uh , stuff at the harmonics isn't so much. And  and , uh  And we there 's this overall idea of really sort of matching the  the hi distributions somehow. Uh , not just , um ,  um  not just subtracting off your estimate of the noise. So. So I guess , uh ,  Guenter 's gonna play around with some of these things now over this next  period ,


So potentially if he came up with something that was useful , like a diff a better noise estimation module or something , he could ship it to you guys u up there


Yeah. Yeah. So , that 's good. So , why don't we just , uh , um  I think starting   starting a w couple weeks from now , especially if you 're not gonna be around for a while , we 'll  we 'll be shifting more over to some other   other territory. But , uh , uh ,  uh , n not  not so much in this meeting about Aurora , but  but , uh , uh , maybe just , uh , quickly today about  maybe you could just say a little bit about what you 've been talking about with Michael. And  and then Barry can say something about  what   what we 're talking about.


Yeah. Yeah. So , that 's good. So , why don't we just , uh , um  I think starting   starting a w couple weeks from now , especially if you 're not gonna be around for a while , we 'll  we 'll be shifting more over to some other   other territory. But , uh , uh ,  uh , n not  not so much in this meeting about Aurora , but  but , uh , uh , maybe just , uh , quickly today about  maybe you could just say a little bit about what you 've been talking about with Michael. And  and then Barry can say something about  what   what we 're talking about.

OK. So Michael Kleinschmidt , who 's a PHD student from Germany ,  showed up this week. He 'll be here for about six months. And he 's done some work using  an auditory model  of , um ,  human hearing , and  using that f uh , to generate speech recognition features. And  he did  work back in Germany  with , um , a toy recognition system  using , um , isolated  digit recognition  as the task. It was actually just a single - layer neural network  that classified words  classified digits ,  in fact. Um , and  he tried that on  I think on some Aurora data and got results that he thought  seemed respectable. And he w he 's coming here to u u use it on a  uh , a real speech recognition system. So I 'll be working with him on that. And , um , maybe I should say a little more about these features , although I don't understand them that well. The  I think it 's a two - stage idea. And , um ,  the first stage of these features correspond to what 's called the peripheral  auditory system. And  I guess that is like  a filter bank with a compressive nonlinearity. And  I 'm - I 'm not sure what we have @ @ in there that isn't already modeled in something like ,  um ,  PLP. I should learn more about that. And then  the second stage  is , um ,  the most different thing , I think , from what we usually do. It 's , um    it computes features which are ,  um ,  based on  sort of like based on diffe different w um , wavelet basis functions  used to analyze  the input.  So th he uses analysis functions called  Gabor functions , um ,  which have a certain  extent , um ,  in time and in frequency. And  the idea is these are used to sample ,  um , the signal in a represented as a time - frequency representation. So you 're  sampling some piece of this time - frequency plane. And , um ,  that ,  um , is  is interesting , cuz ,  @ @ for  for one thing , you could use it ,  um , in a  a multi - scale way. You could have these  instead of having everything  like we use a twenty - five millisecond or so analysis window ,  typically , um , and that 's our time scale for features , but you could   using this , um , basis function idea , you could have some basis functions which have a lot longer time scale and , um , some which have a lot shorter , and  so it would be like  a set of multi - scale features. So he 's interested in , um  Th - this is  because it 's , um  there are these different parameters for the shape of these  basis functions ,  um   there are a lot of different possible basis functions. And so he   he actually does  an optimization procedure to choose an   an optimal set of basis functions out of all the possible ones.


Yeah. Yeah. So , that 's good. So , why don't we just , uh , um  I think starting   starting a w couple weeks from now , especially if you 're not gonna be around for a while , we 'll  we 'll be shifting more over to some other   other territory. But , uh , uh ,  uh , n not  not so much in this meeting about Aurora , but  but , uh , uh , maybe just , uh , quickly today about  maybe you could just say a little bit about what you 've been talking about with Michael. And  and then Barry can say something about  what   what we 're talking about.

OK. So Michael Kleinschmidt , who 's a PHD student from Germany ,  showed up this week. He 'll be here for about six months. And he 's done some work using  an auditory model  of , um ,  human hearing , and  using that f uh , to generate speech recognition features. And  he did  work back in Germany  with , um , a toy recognition system  using , um , isolated  digit recognition  as the task. It was actually just a single - layer neural network  that classified words  classified digits ,  in fact. Um , and  he tried that on  I think on some Aurora data and got results that he thought  seemed respectable. And he w he 's coming here to u u use it on a  uh , a real speech recognition system. So I 'll be working with him on that. And , um , maybe I should say a little more about these features , although I don't understand them that well. The  I think it 's a two - stage idea. And , um ,  the first stage of these features correspond to what 's called the peripheral  auditory system. And  I guess that is like  a filter bank with a compressive nonlinearity. And  I 'm - I 'm not sure what we have @ @ in there that isn't already modeled in something like ,  um ,  PLP. I should learn more about that. And then  the second stage  is , um ,  the most different thing , I think , from what we usually do. It 's , um    it computes features which are ,  um ,  based on  sort of like based on diffe different w um , wavelet basis functions  used to analyze  the input.  So th he uses analysis functions called  Gabor functions , um ,  which have a certain  extent , um ,  in time and in frequency. And  the idea is these are used to sample ,  um , the signal in a represented as a time - frequency representation. So you 're  sampling some piece of this time - frequency plane. And , um ,  that ,  um , is  is interesting , cuz ,  @ @ for  for one thing , you could use it ,  um , in a  a multi - scale way. You could have these  instead of having everything  like we use a twenty - five millisecond or so analysis window ,  typically , um , and that 's our time scale for features , but you could   using this , um , basis function idea , you could have some basis functions which have a lot longer time scale and , um , some which have a lot shorter , and  so it would be like  a set of multi - scale features. So he 's interested in , um  Th - this is  because it 's , um  there are these different parameters for the shape of these  basis functions ,  um   there are a lot of different possible basis functions. And so he   he actually does  an optimization procedure to choose an   an optimal set of basis functions out of all the possible ones.

Hmm. H What does he do to choose those ?


Yeah. Yeah. So , that 's good. So , why don't we just , uh , um  I think starting   starting a w couple weeks from now , especially if you 're not gonna be around for a while , we 'll  we 'll be shifting more over to some other   other territory. But , uh , uh ,  uh , n not  not so much in this meeting about Aurora , but  but , uh , uh , maybe just , uh , quickly today about  maybe you could just say a little bit about what you 've been talking about with Michael. And  and then Barry can say something about  what   what we 're talking about.

OK. So Michael Kleinschmidt , who 's a PHD student from Germany ,  showed up this week. He 'll be here for about six months. And he 's done some work using  an auditory model  of , um ,  human hearing , and  using that f uh , to generate speech recognition features. And  he did  work back in Germany  with , um , a toy recognition system  using , um , isolated  digit recognition  as the task. It was actually just a single - layer neural network  that classified words  classified digits ,  in fact. Um , and  he tried that on  I think on some Aurora data and got results that he thought  seemed respectable. And he w he 's coming here to u u use it on a  uh , a real speech recognition system. So I 'll be working with him on that. And , um , maybe I should say a little more about these features , although I don't understand them that well. The  I think it 's a two - stage idea. And , um ,  the first stage of these features correspond to what 's called the peripheral  auditory system. And  I guess that is like  a filter bank with a compressive nonlinearity. And  I 'm - I 'm not sure what we have @ @ in there that isn't already modeled in something like ,  um ,  PLP. I should learn more about that. And then  the second stage  is , um ,  the most different thing , I think , from what we usually do. It 's , um    it computes features which are ,  um ,  based on  sort of like based on diffe different w um , wavelet basis functions  used to analyze  the input.  So th he uses analysis functions called  Gabor functions , um ,  which have a certain  extent , um ,  in time and in frequency. And  the idea is these are used to sample ,  um , the signal in a represented as a time - frequency representation. So you 're  sampling some piece of this time - frequency plane. And , um ,  that ,  um , is  is interesting , cuz ,  @ @ for  for one thing , you could use it ,  um , in a  a multi - scale way. You could have these  instead of having everything  like we use a twenty - five millisecond or so analysis window ,  typically , um , and that 's our time scale for features , but you could   using this , um , basis function idea , you could have some basis functions which have a lot longer time scale and , um , some which have a lot shorter , and  so it would be like  a set of multi - scale features. So he 's interested in , um  Th - this is  because it 's , um  there are these different parameters for the shape of these  basis functions ,  um   there are a lot of different possible basis functions. And so he   he actually does  an optimization procedure to choose an   an optimal set of basis functions out of all the possible ones.

Hmm. H What does he do to choose those ?

The method he uses is kind of funny  is ,   um ,  he starts with  he has a set of M of them. Um , he  and then  he uses that to classify  I mean , he t he tries , um ,  using  just M minus one of them. So there are M possible subsets of this  length - M vector. He tries classifying , using each of the M  possible sub - vectors.


OK. So Michael Kleinschmidt , who 's a PHD student from Germany ,  showed up this week. He 'll be here for about six months. And he 's done some work using  an auditory model  of , um ,  human hearing , and  using that f uh , to generate speech recognition features. And  he did  work back in Germany  with , um , a toy recognition system  using , um , isolated  digit recognition  as the task. It was actually just a single - layer neural network  that classified words  classified digits ,  in fact. Um , and  he tried that on  I think on some Aurora data and got results that he thought  seemed respectable. And he w he 's coming here to u u use it on a  uh , a real speech recognition system. So I 'll be working with him on that. And , um , maybe I should say a little more about these features , although I don't understand them that well. The  I think it 's a two - stage idea. And , um ,  the first stage of these features correspond to what 's called the peripheral  auditory system. And  I guess that is like  a filter bank with a compressive nonlinearity. And  I 'm - I 'm not sure what we have @ @ in there that isn't already modeled in something like ,  um ,  PLP. I should learn more about that. And then  the second stage  is , um ,  the most different thing , I think , from what we usually do. It 's , um    it computes features which are ,  um ,  based on  sort of like based on diffe different w um , wavelet basis functions  used to analyze  the input.  So th he uses analysis functions called  Gabor functions , um ,  which have a certain  extent , um ,  in time and in frequency. And  the idea is these are used to sample ,  um , the signal in a represented as a time - frequency representation. So you 're  sampling some piece of this time - frequency plane. And , um ,  that ,  um , is  is interesting , cuz ,  @ @ for  for one thing , you could use it ,  um , in a  a multi - scale way. You could have these  instead of having everything  like we use a twenty - five millisecond or so analysis window ,  typically , um , and that 's our time scale for features , but you could   using this , um , basis function idea , you could have some basis functions which have a lot longer time scale and , um , some which have a lot shorter , and  so it would be like  a set of multi - scale features. So he 's interested in , um  Th - this is  because it 's , um  there are these different parameters for the shape of these  basis functions ,  um   there are a lot of different possible basis functions. And so he   he actually does  an optimization procedure to choose an   an optimal set of basis functions out of all the possible ones.

Hmm. H What does he do to choose those ?

The method he uses is kind of funny  is ,   um ,  he starts with  he has a set of M of them. Um , he  and then  he uses that to classify  I mean , he t he tries , um ,  using  just M minus one of them. So there are M possible subsets of this  length - M vector. He tries classifying , using each of the M  possible sub - vectors.


Hmm. H What does he do to choose those ?

The method he uses is kind of funny  is ,   um ,  he starts with  he has a set of M of them. Um , he  and then  he uses that to classify  I mean , he t he tries , um ,  using  just M minus one of them. So there are M possible subsets of this  length - M vector. He tries classifying , using each of the M  possible sub - vectors.

Whichever sub - vector ,  um , works the  the best , I guess , he says   the  the fe feature that didn't use was the most useless feature ,


The method he uses is kind of funny  is ,   um ,  he starts with  he has a set of M of them. Um , he  and then  he uses that to classify  I mean , he t he tries , um ,  using  just M minus one of them. So there are M possible subsets of this  length - M vector. He tries classifying , using each of the M  possible sub - vectors.

Whichever sub - vector ,  um , works the  the best , I guess , he says   the  the fe feature that didn't use was the most useless feature ,


Whichever sub - vector ,  um , works the  the best , I guess , he says   the  the fe feature that didn't use was the most useless feature ,

so we 'll throw it out and we 're gonna randomly select another feature  from the set of possible basis functions.


so we 'll throw it out and we 're gonna randomly select another feature  from the set of possible basis functions.


it 's a little bit like a genetic algorithm or something in a way.


it 's a little bit like a genetic algorithm or something in a way.

But it 's  but it 's  uh , it 's  there 's a lot  number of things I like about it , let me just say.


But it 's  but it 's  uh , it 's  there 's a lot  number of things I like about it , let me just say.


But it 's  but it 's  uh , it 's  there 's a lot  number of things I like about it , let me just say.

So , first thing , well , you 're absolutely right. I mean ,  i i  in truth ,  both pieces of this are  have their analogies in stuff we already do. But it 's a different take  at how to approach it and potentially one that 's m maybe a bit more systematic than what we 've done , uh , and a b a bit more inspiration from  from auditory things. So it 's  so I think it 's a neat thing to try. The primary features ,  um , are in fact  Yeah , essentially , it 's  it 's , uh , you know , PLP or  or mel cepstrum , or something like that. You 've  you 've got some ,  uh , compression. We always have some compression. We always have some  you know , the  the  the kind of filter bank with a kind of   quasi - log scaling. Um ,  if you put in  if you also include the RASTA in it  i RASTA  the filtering being done in the log domain  has an AGC - like , uh , characteristic , which , you know , people typi typically put in these kind of ,  uh ,  um ,  uh , auditory front - ends. So it 's very , very similar , uh , but it 's not exactly the same. Um , I would agree that the second one is  is somewhat more different but ,  um , it 's mainly different in that the things that we have been doing like that have been   um , had a different kind of motivation and have ended up with different kinds of constraints. So , for instance , if you look at the LDA RASTA stuff ,  you know , basically what they do is they  they look at the different eigenvectors out of the LDA and they form filters out of it. Right ? And those  filters have different , uh , kinds of temporal extents and temporal characteristics. And so in fact they 're multi - scale. But , they 're not sort of systematically multi - scale , like " let 's start here and go to there , and go to there , and go to there " , and so forth. It 's more like ,  you run it on this , you do discriminant analysis , and you find out what 's helpful.


But it 's  but it 's  uh , it 's  there 's a lot  number of things I like about it , let me just say.

So , first thing , well , you 're absolutely right. I mean ,  i i  in truth ,  both pieces of this are  have their analogies in stuff we already do. But it 's a different take  at how to approach it and potentially one that 's m maybe a bit more systematic than what we 've done , uh , and a b a bit more inspiration from  from auditory things. So it 's  so I think it 's a neat thing to try. The primary features ,  um , are in fact  Yeah , essentially , it 's  it 's , uh , you know , PLP or  or mel cepstrum , or something like that. You 've  you 've got some ,  uh , compression. We always have some compression. We always have some  you know , the  the  the kind of filter bank with a kind of   quasi - log scaling. Um ,  if you put in  if you also include the RASTA in it  i RASTA  the filtering being done in the log domain  has an AGC - like , uh , characteristic , which , you know , people typi typically put in these kind of ,  uh ,  um ,  uh , auditory front - ends. So it 's very , very similar , uh , but it 's not exactly the same. Um , I would agree that the second one is  is somewhat more different but ,  um , it 's mainly different in that the things that we have been doing like that have been   um , had a different kind of motivation and have ended up with different kinds of constraints. So , for instance , if you look at the LDA RASTA stuff ,  you know , basically what they do is they  they look at the different eigenvectors out of the LDA and they form filters out of it. Right ? And those  filters have different , uh , kinds of temporal extents and temporal characteristics. And so in fact they 're multi - scale. But , they 're not sort of systematically multi - scale , like " let 's start here and go to there , and go to there , and go to there " , and so forth. It 's more like ,  you run it on this , you do discriminant analysis , and you find out what 's helpful.

I it 's multi - scale because you use several of these in parallel ,


So , first thing , well , you 're absolutely right. I mean ,  i i  in truth ,  both pieces of this are  have their analogies in stuff we already do. But it 's a different take  at how to approach it and potentially one that 's m maybe a bit more systematic than what we 've done , uh , and a b a bit more inspiration from  from auditory things. So it 's  so I think it 's a neat thing to try. The primary features ,  um , are in fact  Yeah , essentially , it 's  it 's , uh , you know , PLP or  or mel cepstrum , or something like that. You 've  you 've got some ,  uh , compression. We always have some compression. We always have some  you know , the  the  the kind of filter bank with a kind of   quasi - log scaling. Um ,  if you put in  if you also include the RASTA in it  i RASTA  the filtering being done in the log domain  has an AGC - like , uh , characteristic , which , you know , people typi typically put in these kind of ,  uh ,  um ,  uh , auditory front - ends. So it 's very , very similar , uh , but it 's not exactly the same. Um , I would agree that the second one is  is somewhat more different but ,  um , it 's mainly different in that the things that we have been doing like that have been   um , had a different kind of motivation and have ended up with different kinds of constraints. So , for instance , if you look at the LDA RASTA stuff ,  you know , basically what they do is they  they look at the different eigenvectors out of the LDA and they form filters out of it. Right ? And those  filters have different , uh , kinds of temporal extents and temporal characteristics. And so in fact they 're multi - scale. But , they 're not sort of systematically multi - scale , like " let 's start here and go to there , and go to there , and go to there " , and so forth. It 's more like ,  you run it on this , you do discriminant analysis , and you find out what 's helpful.

I it 's multi - scale because you use several of these in parallel ,


I it 's multi - scale because you use several of these in parallel ,


Uh , I mean , you don't have to but  but  but , uh , Hynek has. Um , but it 's also , uh   Hyn - when Hynek 's had people do this kind of LDA analysis , they 've done it on frequency direction and they 've done it on the time direction. I think he may have had people sometimes doing it on both simultaneously  some two - D  and that would be the closest to these Gabor function kind of things. Uh , but I don't think they 've done that much of that. And , uh , the other thing that 's interesting  the  the , uh  the feature selection thing , it 's a simple method , but I kinda like it. Um ,  there 's a   a old , old method for feature selection. I mean ,  eh , uh , I remember people referring to it as old when I was playing with it twenty years ago , so I know it 's pretty old , uh , called Stepwise Linear Discriminant Analysis in which you  which  I think it 's used in social sciences a lot. So , you  you  you  you pick the best feature. And then  you take  y you find the next feature that 's the best in combination with it. And then so on and so on. And what  what Michael 's describing seems to me much , much better , because the problem with the stepwise discriminant analysis is that you don't know that  you know , if you 've  picked the right set of features. Just because something 's a good feature doesn't mean that you should be adding it. So ,  um ,  uh , here at least you 're starting off with all of them , and you 're  throwing out useless features. I think that 's  that seems , uh   that seems like a lot better idea. Uh , you 're always looking at things in combination with other features. Um , so the only thing is , of course , there 's this  this artificial question of  of , uh ,  exactly how you  how you a how you assess it and if  if your order had been different in throwing them out. I mean , it still isn't necessarily really optimal , but it seems like a pretty good heuristic. So I th I think it 's  it 's  I think it 's kinda neat stuff.


Uh , I mean , you don't have to but  but  but , uh , Hynek has. Um , but it 's also , uh   Hyn - when Hynek 's had people do this kind of LDA analysis , they 've done it on frequency direction and they 've done it on the time direction. I think he may have had people sometimes doing it on both simultaneously  some two - D  and that would be the closest to these Gabor function kind of things. Uh , but I don't think they 've done that much of that. And , uh , the other thing that 's interesting  the  the , uh  the feature selection thing , it 's a simple method , but I kinda like it. Um ,  there 's a   a old , old method for feature selection. I mean ,  eh , uh , I remember people referring to it as old when I was playing with it twenty years ago , so I know it 's pretty old , uh , called Stepwise Linear Discriminant Analysis in which you  which  I think it 's used in social sciences a lot. So , you  you  you  you pick the best feature. And then  you take  y you find the next feature that 's the best in combination with it. And then so on and so on. And what  what Michael 's describing seems to me much , much better , because the problem with the stepwise discriminant analysis is that you don't know that  you know , if you 've  picked the right set of features. Just because something 's a good feature doesn't mean that you should be adding it. So ,  um ,  uh , here at least you 're starting off with all of them , and you 're  throwing out useless features. I think that 's  that seems , uh   that seems like a lot better idea. Uh , you 're always looking at things in combination with other features. Um , so the only thing is , of course , there 's this  this artificial question of  of , uh ,  exactly how you  how you a how you assess it and if  if your order had been different in throwing them out. I mean , it still isn't necessarily really optimal , but it seems like a pretty good heuristic. So I th I think it 's  it 's  I think it 's kinda neat stuff.

And  and  and , uh , the thing that I wanted to  to add to it also was to have us use this in a multi - stream way.


And  and  and , uh , the thing that I wanted to  to add to it also was to have us use this in a multi - stream way.

Um , so  so that , um ,  when you come up with these different things ,  and these different functions ,  you don't necessarily just put them all into one huge vector , but perhaps  you  have some of them in one stream and some of them in another stream , and so forth. And , um , um ,  um  And we 've also talked a little bit about , uh ,  uh , Shihab Shamma 's stuff , in which  you  the way you look at it is that there 's these different mappings and some of them emphasize , uh , upward moving ,  uh , energy and fre and frequency. And some are emphasizing downward and  fast things and slow things and  and  so forth. So. So there 's a bunch of stuff to look at. But , uh , I think we 're sorta gonna start off with what  he , uh , came here with and branch out   branch out from there. And his advisor is here , too ,  at the same time. So , he 'll be another  interesting source of  wisdom.


Um , so  so that , um ,  when you come up with these different things ,  and these different functions ,  you don't necessarily just put them all into one huge vector , but perhaps  you  have some of them in one stream and some of them in another stream , and so forth. And , um , um ,  um  And we 've also talked a little bit about , uh ,  uh , Shihab Shamma 's stuff , in which  you  the way you look at it is that there 's these different mappings and some of them emphasize , uh , upward moving ,  uh , energy and fre and frequency. And some are emphasizing downward and  fast things and slow things and  and  so forth. So. So there 's a bunch of stuff to look at. But , uh , I think we 're sorta gonna start off with what  he , uh , came here with and branch out   branch out from there. And his advisor is here , too ,  at the same time. So , he 'll be another  interesting source of  wisdom.


Um , so  so that , um ,  when you come up with these different things ,  and these different functions ,  you don't necessarily just put them all into one huge vector , but perhaps  you  have some of them in one stream and some of them in another stream , and so forth. And , um , um ,  um  And we 've also talked a little bit about , uh ,  uh , Shihab Shamma 's stuff , in which  you  the way you look at it is that there 's these different mappings and some of them emphasize , uh , upward moving ,  uh , energy and fre and frequency. And some are emphasizing downward and  fast things and slow things and  and  so forth. So. So there 's a bunch of stuff to look at. But , uh , I think we 're sorta gonna start off with what  he , uh , came here with and branch out   branch out from there. And his advisor is here , too ,  at the same time. So , he 'll be another  interesting source of  wisdom.

As  as we were talking about this I was thinking ,  um ,  whether there 's a relationship between   um ,   between Michael 's approach to , uh , some  some sort of optimal brain damage or optimal brain surgeon on the neural nets.


As  as we were talking about this I was thinking ,  um ,  whether there 's a relationship between   um ,   between Michael 's approach to , uh , some  some sort of optimal brain damage or optimal brain surgeon on the neural nets.


As  as we were talking about this I was thinking ,  um ,  whether there 's a relationship between   um ,   between Michael 's approach to , uh , some  some sort of optimal brain damage or optimal brain surgeon on the neural nets.

So , like , if we have , um  we have our  we have our RASTA features and  and presumably the neural nets are  are learning some sort of a nonlinear mapping ,  uh , from the  the  the features  to  to this  this probability posterior space.


So , like , if we have , um  we have our  we have our RASTA features and  and presumably the neural nets are  are learning some sort of a nonlinear mapping ,  uh , from the  the  the features  to  to this  this probability posterior space.


So , like , if we have , um  we have our  we have our RASTA features and  and presumably the neural nets are  are learning some sort of a nonlinear mapping ,  uh , from the  the  the features  to  to this  this probability posterior space.

Right ? And , um    and each of the hidden units is learning some sort of  some sort of  some sort of pattern. Right ? And it could be , like   like these , um  these auditory patterns that Michael  is looking at. And then when you 're looking at the   the , uh ,  um ,  the best features ,  you know , you can take out  you can do the  do this , uh , brain surgery by taking out ,  um , hidden units that don't really help at all.


Right ? And , um    and each of the hidden units is learning some sort of  some sort of  some sort of pattern. Right ? And it could be , like   like these , um  these auditory patterns that Michael  is looking at. And then when you 're looking at the   the , uh ,  um ,  the best features ,  you know , you can take out  you can do the  do this , uh , brain surgery by taking out ,  um , hidden units that don't really help at all.


I mean , y actually , you make me think a  a very important point here is that , um ,  if we a again try to look at how is this different from what we 're already doing ,  uh , there 's a  a , uh   a nasty argument that could be made th that it 's  it 's not different at  at all , because , uh  if you ignore the  the selection part because we are going into a  a very powerful ,  uh , nonlinearity that , uh , in fact is combining over time and frequency , and is coming up with its own  you know , better than Gabor functions its , you know , neural net functions ,


I mean , y actually , you make me think a  a very important point here is that , um ,  if we a again try to look at how is this different from what we 're already doing ,  uh , there 's a  a , uh   a nasty argument that could be made th that it 's  it 's not different at  at all , because , uh  if you ignore the  the selection part because we are going into a  a very powerful ,  uh , nonlinearity that , uh , in fact is combining over time and frequency , and is coming up with its own  you know , better than Gabor functions its , you know , neural net functions ,

Um , so you could argue that in fact it  But I  I don't actually believe that argument because I know that , um ,  you can , uh  computing features is useful , even though  in principle you haven't   added anything  in fact , you subtracted something , from the original waveform  You know , uh , if you 've  you 've processed it in some way you 've typically lost something  some information. And so ,  you 've lost information and yet it does better with   with features than it does with the waveform. So , uh , I  I know that i sometimes it 's useful to   to constrain things. So that 's  why it really seems like the constraint  in  in all this stuff it 's the constraints that are actually what matters. Because if it wasn't  the constraints that mattered , then we would 've completely solved this problem long ago , because long ago we already knew how to put waveforms into powerful statistical mechanisms. So.


Um , so you could argue that in fact it  But I  I don't actually believe that argument because I know that , um ,  you can , uh  computing features is useful , even though  in principle you haven't   added anything  in fact , you subtracted something , from the original waveform  You know , uh , if you 've  you 've processed it in some way you 've typically lost something  some information. And so ,  you 've lost information and yet it does better with   with features than it does with the waveform. So , uh , I  I know that i sometimes it 's useful to   to constrain things. So that 's  why it really seems like the constraint  in  in all this stuff it 's the constraints that are actually what matters. Because if it wasn't  the constraints that mattered , then we would 've completely solved this problem long ago , because long ago we already knew how to put waveforms into powerful statistical mechanisms. So.

Yeah. Well , if we had infinite processing power and  data ,  I guess , using the waveform could


Um , so you could argue that in fact it  But I  I don't actually believe that argument because I know that , um ,  you can , uh  computing features is useful , even though  in principle you haven't   added anything  in fact , you subtracted something , from the original waveform  You know , uh , if you 've  you 've processed it in some way you 've typically lost something  some information. And so ,  you 've lost information and yet it does better with   with features than it does with the waveform. So , uh , I  I know that i sometimes it 's useful to   to constrain things. So that 's  why it really seems like the constraint  in  in all this stuff it 's the constraints that are actually what matters. Because if it wasn't  the constraints that mattered , then we would 've completely solved this problem long ago , because long ago we already knew how to put waveforms into powerful statistical mechanisms. So.

Yeah. Well , if we had infinite processing power and  data ,  I guess , using the waveform could

Yeah Uh , then it would work. Yeah , I agree. Yeah. There 's the problem.


Yeah. Well , if we had infinite processing power and  data ,  I guess , using the waveform could

Yeah Uh , then it would work. Yeah , I agree. Yeah. There 's the problem.


Yeah Uh , then it would work. Yeah , I agree. Yeah. There 's the problem.

Yeah. Then it would work. But  but , I mean , i it 's   With finite  of those things  I mean , uh , we  we have done experiments where we literally have put waveforms in and  and  and , uh ,


Yeah. Then it would work. But  but , I mean , i it 's   With finite  of those things  I mean , uh , we  we have done experiments where we literally have put waveforms in and  and  and , uh ,

we kept the number of parameters the same and so forth , and it used a lot of training data. And it  and it  it , uh  not infinite but a lot , and then compared to the number parameters  and it  it , uh  it just doesn't do nearly as well. So , anyway the point is that you want to suppress


we kept the number of parameters the same and so forth , and it used a lot of training data. And it  and it  it , uh  not infinite but a lot , and then compared to the number parameters  and it  it , uh  it just doesn't do nearly as well. So , anyway the point is that you want to suppress

it 's not just having the maximum information , you want to suppress ,  uh , the aspects of the input signal that are not helpful for  for the discrimination you 're trying to make. So. So maybe just briefly , uh


we kept the number of parameters the same and so forth , and it used a lot of training data. And it  and it  it , uh  not infinite but a lot , and then compared to the number parameters  and it  it , uh  it just doesn't do nearly as well. So , anyway the point is that you want to suppress

it 's not just having the maximum information , you want to suppress ,  uh , the aspects of the input signal that are not helpful for  for the discrimination you 're trying to make. So. So maybe just briefly , uh

Well , that sort of segues into  what  what I 'm doing.


it 's not just having the maximum information , you want to suppress ,  uh , the aspects of the input signal that are not helpful for  for the discrimination you 're trying to make. So. So maybe just briefly , uh

Well , that sort of segues into  what  what I 'm doing.


it 's not just having the maximum information , you want to suppress ,  uh , the aspects of the input signal that are not helpful for  for the discrimination you 're trying to make. So. So maybe just briefly , uh

Well , that sort of segues into  what  what I 'm doing.

Um ,  so , uh , the big picture is k um ,  come up with a set of ,  uh , intermediate categories , then build intermediate category classifiers , then do recognition , and , um , improve speech recognition in that way. Um , so right now I 'm in  in the phase where  I 'm looking at  at , um , deciding on a initial set of intermediate categories. And  I 'm looking  for data data - driven  methods that can help me find ,  um , a set of intermediate categories  of speech that , uh , will help me to discriminate  later down the line. And one of the ideas ,  um , that was to take a  take a neural net  train  train an ordinary neural net  to   uh , to learn the posterior probabilities of phones. And so , um , at the end of the day you have this neural net and it has hidden   hidden units. And each of these hidden units is   um , is learning some sort of pattern. And so , um , what  what are these patterns ?


Well , that sort of segues into  what  what I 'm doing.

Um ,  so , uh , the big picture is k um ,  come up with a set of ,  uh , intermediate categories , then build intermediate category classifiers , then do recognition , and , um , improve speech recognition in that way. Um , so right now I 'm in  in the phase where  I 'm looking at  at , um , deciding on a initial set of intermediate categories. And  I 'm looking  for data data - driven  methods that can help me find ,  um , a set of intermediate categories  of speech that , uh , will help me to discriminate  later down the line. And one of the ideas ,  um , that was to take a  take a neural net  train  train an ordinary neural net  to   uh , to learn the posterior probabilities of phones. And so , um , at the end of the day you have this neural net and it has hidden   hidden units. And each of these hidden units is   um , is learning some sort of pattern. And so , um , what  what are these patterns ?


Um ,  so , uh , the big picture is k um ,  come up with a set of ,  uh , intermediate categories , then build intermediate category classifiers , then do recognition , and , um , improve speech recognition in that way. Um , so right now I 'm in  in the phase where  I 'm looking at  at , um , deciding on a initial set of intermediate categories. And  I 'm looking  for data data - driven  methods that can help me find ,  um , a set of intermediate categories  of speech that , uh , will help me to discriminate  later down the line. And one of the ideas ,  um , that was to take a  take a neural net  train  train an ordinary neural net  to   uh , to learn the posterior probabilities of phones. And so , um , at the end of the day you have this neural net and it has hidden   hidden units. And each of these hidden units is   um , is learning some sort of pattern. And so , um , what  what are these patterns ?

I don't know. Um , and I 'm gonna to try to   to look at those patterns  to  to see ,  um ,  from those patterns  uh , presumably those are important patterns for discriminating between phone classes. And maybe   maybe some , uh , intermediate categories can come from  just looking at the patterns of   um , that the neural net learns.


Um ,  so , uh , the big picture is k um ,  come up with a set of ,  uh , intermediate categories , then build intermediate category classifiers , then do recognition , and , um , improve speech recognition in that way. Um , so right now I 'm in  in the phase where  I 'm looking at  at , um , deciding on a initial set of intermediate categories. And  I 'm looking  for data data - driven  methods that can help me find ,  um , a set of intermediate categories  of speech that , uh , will help me to discriminate  later down the line. And one of the ideas ,  um , that was to take a  take a neural net  train  train an ordinary neural net  to   uh , to learn the posterior probabilities of phones. And so , um , at the end of the day you have this neural net and it has hidden   hidden units. And each of these hidden units is   um , is learning some sort of pattern. And so , um , what  what are these patterns ?

I don't know. Um , and I 'm gonna to try to   to look at those patterns  to  to see ,  um ,  from those patterns  uh , presumably those are important patterns for discriminating between phone classes. And maybe   maybe some , uh , intermediate categories can come from  just looking at the patterns of   um , that the neural net learns.

Be - before you get on the next part l let me just point out that s there 's  there 's a  a pretty nice   relationship between what you 're talking about doing and what you 're talking about doing there. Right ?


I don't know. Um , and I 'm gonna to try to   to look at those patterns  to  to see ,  um ,  from those patterns  uh , presumably those are important patterns for discriminating between phone classes. And maybe   maybe some , uh , intermediate categories can come from  just looking at the patterns of   um , that the neural net learns.

Be - before you get on the next part l let me just point out that s there 's  there 's a  a pretty nice   relationship between what you 're talking about doing and what you 're talking about doing there. Right ?


I don't know. Um , and I 'm gonna to try to   to look at those patterns  to  to see ,  um ,  from those patterns  uh , presumably those are important patterns for discriminating between phone classes. And maybe   maybe some , uh , intermediate categories can come from  just looking at the patterns of   um , that the neural net learns.

Be - before you get on the next part l let me just point out that s there 's  there 's a  a pretty nice   relationship between what you 're talking about doing and what you 're talking about doing there. Right ?

So ,  it seems to me that , you know , if you take away the  the   the difference of this  primary features ,  and , say , you use  as we had talked about maybe doing  you use P - RASTA - PLP or something for the  the primary features ,  um , then this feature discovery ,  uh , uh , thing  is just what he 's talking about doing , too , except that he 's talking about doing them in order to discover  intermediate categories that correspond  to these  uh , uh , what these sub - features are  are  are  are showing you. And , um ,  the other difference is that , um ,  he 's doing this in a  in a multi - band setting , which means that he 's constraining himself  to look across time in some f relatively limited , uh , uh , spectral extent. Right ? And whereas in  in this case you 're saying " let 's just do it unconstrained ". So they 're  they 're really pretty related and maybe they 'll be  at some point where we 'll see the  the connections a little better and  connect them.


Be - before you get on the next part l let me just point out that s there 's  there 's a  a pretty nice   relationship between what you 're talking about doing and what you 're talking about doing there. Right ?

So ,  it seems to me that , you know , if you take away the  the   the difference of this  primary features ,  and , say , you use  as we had talked about maybe doing  you use P - RASTA - PLP or something for the  the primary features ,  um , then this feature discovery ,  uh , uh , thing  is just what he 's talking about doing , too , except that he 's talking about doing them in order to discover  intermediate categories that correspond  to these  uh , uh , what these sub - features are  are  are  are showing you. And , um ,  the other difference is that , um ,  he 's doing this in a  in a multi - band setting , which means that he 's constraining himself  to look across time in some f relatively limited , uh , uh , spectral extent. Right ? And whereas in  in this case you 're saying " let 's just do it unconstrained ". So they 're  they 're really pretty related and maybe they 'll be  at some point where we 'll see the  the connections a little better and  connect them.


So ,  it seems to me that , you know , if you take away the  the   the difference of this  primary features ,  and , say , you use  as we had talked about maybe doing  you use P - RASTA - PLP or something for the  the primary features ,  um , then this feature discovery ,  uh , uh , thing  is just what he 's talking about doing , too , except that he 's talking about doing them in order to discover  intermediate categories that correspond  to these  uh , uh , what these sub - features are  are  are  are showing you. And , um ,  the other difference is that , um ,  he 's doing this in a  in a multi - band setting , which means that he 's constraining himself  to look across time in some f relatively limited , uh , uh , spectral extent. Right ? And whereas in  in this case you 're saying " let 's just do it unconstrained ". So they 're  they 're really pretty related and maybe they 'll be  at some point where we 'll see the  the connections a little better and  connect them.

Mm - hmm. Um. Yeah , so  so that 's the  that 's the first part  uh , one  one of the ideas to get at some   some patterns of intermediate categories. Um ,  the other one  was ,  um , to ,  uh , come up with a  a  a model   um , a graphical model ,  that treats  the intermediate categories  as hidden  hidden variables , latent variables , that we don't know anything about , but that through ,  um , s statistical training and the EM algorithm ,  um , at the end of the day ,  we have , um  we have learned something about these  these latent , um  latent variables which happen to correspond to  intermediate categories. Um.   Yeah , and so those are the  the two directions that I 'm  I 'm looking into right now. And , uh ,  um    Yeah. I guess that 's  that 's it.


So ,  it seems to me that , you know , if you take away the  the   the difference of this  primary features ,  and , say , you use  as we had talked about maybe doing  you use P - RASTA - PLP or something for the  the primary features ,  um , then this feature discovery ,  uh , uh , thing  is just what he 's talking about doing , too , except that he 's talking about doing them in order to discover  intermediate categories that correspond  to these  uh , uh , what these sub - features are  are  are  are showing you. And , um ,  the other difference is that , um ,  he 's doing this in a  in a multi - band setting , which means that he 's constraining himself  to look across time in some f relatively limited , uh , uh , spectral extent. Right ? And whereas in  in this case you 're saying " let 's just do it unconstrained ". So they 're  they 're really pretty related and maybe they 'll be  at some point where we 'll see the  the connections a little better and  connect them.

Mm - hmm. Um. Yeah , so  so that 's the  that 's the first part  uh , one  one of the ideas to get at some   some patterns of intermediate categories. Um ,  the other one  was ,  um , to ,  uh , come up with a  a  a model   um , a graphical model ,  that treats  the intermediate categories  as hidden  hidden variables , latent variables , that we don't know anything about , but that through ,  um , s statistical training and the EM algorithm ,  um , at the end of the day ,  we have , um  we have learned something about these  these latent , um  latent variables which happen to correspond to  intermediate categories. Um.   Yeah , and so those are the  the two directions that I 'm  I 'm looking into right now. And , uh ,  um    Yeah. I guess that 's  that 's it.

OK. Should we do our digits and get ou get our treats ?


Mm - hmm. Um. Yeah , so  so that 's the  that 's the first part  uh , one  one of the ideas to get at some   some patterns of intermediate categories. Um ,  the other one  was ,  um , to ,  uh , come up with a  a  a model   um , a graphical model ,  that treats  the intermediate categories  as hidden  hidden variables , latent variables , that we don't know anything about , but that through ,  um , s statistical training and the EM algorithm ,  um , at the end of the day ,  we have , um  we have learned something about these  these latent , um  latent variables which happen to correspond to  intermediate categories. Um.   Yeah , and so those are the  the two directions that I 'm  I 'm looking into right now. And , uh ,  um    Yeah. I guess that 's  that 's it.

OK. Should we do our digits and get ou get our treats ?


Mm - hmm. Um. Yeah , so  so that 's the  that 's the first part  uh , one  one of the ideas to get at some   some patterns of intermediate categories. Um ,  the other one  was ,  um , to ,  uh , come up with a  a  a model   um , a graphical model ,  that treats  the intermediate categories  as hidden  hidden variables , latent variables , that we don't know anything about , but that through ,  um , s statistical training and the EM algorithm ,  um , at the end of the day ,  we have , um  we have learned something about these  these latent , um  latent variables which happen to correspond to  intermediate categories. Um.   Yeah , and so those are the  the two directions that I 'm  I 'm looking into right now. And , uh ,  um    Yeah. I guess that 's  that 's it.

OK. Should we do our digits and get ou get our treats ?

Yeah. It 's kind of like , you know , the little rats with the little thing dropping down to them.


OK. Should we do our digits and get ou get our treats ?

Yeah. It 's kind of like , you know , the little rats with the little thing dropping down to them.


Yeah. It 's kind of like , you know , the little rats with the little thing dropping down to them.

We do the digits and then we get our treats.


We do the digits and then we get our treats.


Am I starting now ? Anytime ? Oh sorry. 'Kay , um. Alright , welcome back fro to the second meeting. And um I hope you had a productive last thirty minutes.


Am I starting now ? Anytime ? Oh sorry. 'Kay , um. Alright , welcome back fro to the second meeting. And um I hope you had a productive last thirty minutes.

Um , and um , I'll be taking minutes on this one , and um  Being hooked up to the PowerPoint for this meeting isn't very necessary for uh myself , because it'll be more about uh , what you guys are bringing to the meeting today. Um , so , the first presentation we'll be looking at is Poppy's presentation. And , um  So , sorry ? So , um , take it away Poppy.


Um , and um , I'll be taking minutes on this one , and um  Being hooked up to the PowerPoint for this meeting isn't very necessary for uh myself , because it'll be more about uh , what you guys are bringing to the meeting today. Um , so , the first presentation we'll be looking at is Poppy's presentation. And , um  So , sorry ? So , um , take it away Poppy.


F_ okay. Function F_ eight. Sorry about this guys.


F_ okay. Function F_ eight. Sorry about this guys.

'Kay.  is on. Right.  Okay. I will take this time just to apologise.


F_ okay. Function F_ eight. Sorry about this guys.

'Kay.  is on. Right.  Okay. I will take this time just to apologise.

I , I only , uh , received my emails later on. 'Cause I was too busy carried away doing my own thing ,


'Kay.  is on. Right.  Okay. I will take this time just to apologise.

I , I only , uh , received my emails later on. 'Cause I was too busy carried away doing my own thing ,

which is not obviously not a very good part of a team-working thing.


I , I only , uh , received my emails later on. 'Cause I was too busy carried away doing my own thing ,

which is not obviously not a very good part of a team-working thing.


which is not obviously not a very good part of a team-working thing.

Um , so I was looking at how we're going to go about the working design , and what we actually need to do , and what the remote control needs to do. And it needs to um allow a person to have a portable desi device , so they can control the television from wherever they are. They don't need to actually manually touch the television set. So , it gives them much more flexibility , and allows them to be where they want to be. Um , from  Uh , on a functional side of things , we found out that wh from our previous meeting , we decided that there're certain points that will make our product unique. Um , one is the visibility in the dark , which was um Genevieve's idea. So we need to think about how we could bring this in um technically. And we could use illuminated buttons , which we are all familiar with when we're using a mobile phone , or um something fam familiar. A automatically , um lights up at first touch. Or we could use fluorescent materials which would just um take in the light during the day , and then as soon as they go off they would glow in the dark. Um , also we could use um an alarm. So if we lost the um remote control , perhaps there could be a button on the television set itself , which you could press , and then an alarm from the handset would sound where it was , hopefully in the room. Maybe behind a cushion or somewhere.


Um , so I was looking at how we're going to go about the working design , and what we actually need to do , and what the remote control needs to do. And it needs to um allow a person to have a portable desi device , so they can control the television from wherever they are. They don't need to actually manually touch the television set. So , it gives them much more flexibility , and allows them to be where they want to be. Um , from  Uh , on a functional side of things , we found out that wh from our previous meeting , we decided that there're certain points that will make our product unique. Um , one is the visibility in the dark , which was um Genevieve's idea. So we need to think about how we could bring this in um technically. And we could use illuminated buttons , which we are all familiar with when we're using a mobile phone , or um something fam familiar. A automatically , um lights up at first touch. Or we could use fluorescent materials which would just um take in the light during the day , and then as soon as they go off they would glow in the dark. Um , also we could use um an alarm. So if we lost the um remote control , perhaps there could be a button on the television set itself , which you could press , and then an alarm from the handset would sound where it was , hopefully in the room. Maybe behind a cushion or somewhere.

Um , so that would work. Um , oop. Go back there. Um , another thing I think we d missed out on on the last meeting was the fact that we should consider the environmental impact of our design. Um , from previous researches I've carried out on other projects , um we've learnt about smart materials where um um specific alloys of metals have a shape memory.


Um , so I was looking at how we're going to go about the working design , and what we actually need to do , and what the remote control needs to do. And it needs to um allow a person to have a portable desi device , so they can control the television from wherever they are. They don't need to actually manually touch the television set. So , it gives them much more flexibility , and allows them to be where they want to be. Um , from  Uh , on a functional side of things , we found out that wh from our previous meeting , we decided that there're certain points that will make our product unique. Um , one is the visibility in the dark , which was um Genevieve's idea. So we need to think about how we could bring this in um technically. And we could use illuminated buttons , which we are all familiar with when we're using a mobile phone , or um something fam familiar. A automatically , um lights up at first touch. Or we could use fluorescent materials which would just um take in the light during the day , and then as soon as they go off they would glow in the dark. Um , also we could use um an alarm. So if we lost the um remote control , perhaps there could be a button on the television set itself , which you could press , and then an alarm from the handset would sound where it was , hopefully in the room. Maybe behind a cushion or somewhere.

Um , so that would work. Um , oop. Go back there. Um , another thing I think we d missed out on on the last meeting was the fact that we should consider the environmental impact of our design. Um , from previous researches I've carried out on other projects , um we've learnt about smart materials where um um specific alloys of metals have a shape memory.

So they can be heated and um and cooled , and they change the shape of um the metal. So , for example , a screw that's holding something together could expand and it would force all the components apart. So um , the benefits of this for our product would be that when it came to the end of its product life , if it was heated , um everything would spring apart. So , all the um individual components could be easily separated , and then some could be reused , some could be recycled , and I think that would be very important for products now. Especially 'cause there's much uh responsibility for all the um companies who are coming up with like new designs. 'Cause all , we all know that our resources are being limited , and we have to be very environmentally conscious.


Um , so I was looking at how we're going to go about the working design , and what we actually need to do , and what the remote control needs to do. And it needs to um allow a person to have a portable desi device , so they can control the television from wherever they are. They don't need to actually manually touch the television set. So , it gives them much more flexibility , and allows them to be where they want to be. Um , from  Uh , on a functional side of things , we found out that wh from our previous meeting , we decided that there're certain points that will make our product unique. Um , one is the visibility in the dark , which was um Genevieve's idea. So we need to think about how we could bring this in um technically. And we could use illuminated buttons , which we are all familiar with when we're using a mobile phone , or um something fam familiar. A automatically , um lights up at first touch. Or we could use fluorescent materials which would just um take in the light during the day , and then as soon as they go off they would glow in the dark. Um , also we could use um an alarm. So if we lost the um remote control , perhaps there could be a button on the television set itself , which you could press , and then an alarm from the handset would sound where it was , hopefully in the room. Maybe behind a cushion or somewhere.

Um , so that would work. Um , oop. Go back there. Um , another thing I think we d missed out on on the last meeting was the fact that we should consider the environmental impact of our design. Um , from previous researches I've carried out on other projects , um we've learnt about smart materials where um um specific alloys of metals have a shape memory.

So they can be heated and um and cooled , and they change the shape of um the metal. So , for example , a screw that's holding something together could expand and it would force all the components apart. So um , the benefits of this for our product would be that when it came to the end of its product life , if it was heated , um everything would spring apart. So , all the um individual components could be easily separated , and then some could be reused , some could be recycled , and I think that would be very important for products now. Especially 'cause there's much uh responsibility for all the um companies who are coming up with like new designs. 'Cause all , we all know that our resources are being limited , and we have to be very environmentally conscious.

Right , um , one question. This , um , self-destructible uh metal , it allows for recycling materials ?


Um , so that would work. Um , oop. Go back there. Um , another thing I think we d missed out on on the last meeting was the fact that we should consider the environmental impact of our design. Um , from previous researches I've carried out on other projects , um we've learnt about smart materials where um um specific alloys of metals have a shape memory.

So they can be heated and um and cooled , and they change the shape of um the metal. So , for example , a screw that's holding something together could expand and it would force all the components apart. So um , the benefits of this for our product would be that when it came to the end of its product life , if it was heated , um everything would spring apart. So , all the um individual components could be easily separated , and then some could be reused , some could be recycled , and I think that would be very important for products now. Especially 'cause there's much uh responsibility for all the um companies who are coming up with like new designs. 'Cause all , we all know that our resources are being limited , and we have to be very environmentally conscious.

Right , um , one question. This , um , self-destructible uh metal , it allows for recycling materials ?


So they can be heated and um and cooled , and they change the shape of um the metal. So , for example , a screw that's holding something together could expand and it would force all the components apart. So um , the benefits of this for our product would be that when it came to the end of its product life , if it was heated , um everything would spring apart. So , all the um individual components could be easily separated , and then some could be reused , some could be recycled , and I think that would be very important for products now. Especially 'cause there's much uh responsibility for all the um companies who are coming up with like new designs. 'Cause all , we all know that our resources are being limited , and we have to be very environmentally conscious.

Right , um , one question. This , um , self-destructible uh metal , it allows for recycling materials ?

So that , um , someone could have this product for as long as they felt that they wanted it , and then once they contribute it , then that company can break down the part , the parts better ?


Right , um , one question. This , um , self-destructible uh metal , it allows for recycling materials ?

So that , um , someone could have this product for as long as they felt that they wanted it , and then once they contribute it , then that company can break down the part , the parts better ?

Um  And then  Yeah. Yeah they would , um you would make the , the product as you normally would , apart from the , the bits that hold it all together would be made out of this shape-memory alloy. And that's the part that would um allow all the other parts to be separated at the end. I mean , the user would return the p product to the company , 'cause it's the product's responsibility to get rid of what they've made. Um , and then the company could then just use , make use of this shape-memory alloys to split up the components ,


So that , um , someone could have this product for as long as they felt that they wanted it , and then once they contribute it , then that company can break down the part , the parts better ?

Um  And then  Yeah. Yeah they would , um you would make the , the product as you normally would , apart from the , the bits that hold it all together would be made out of this shape-memory alloy. And that's the part that would um allow all the other parts to be separated at the end. I mean , the user would return the p product to the company , 'cause it's the product's responsibility to get rid of what they've made. Um , and then the company could then just use , make use of this shape-memory alloys to split up the components ,


So that , um , someone could have this product for as long as they felt that they wanted it , and then once they contribute it , then that company can break down the part , the parts better ?

Um  And then  Yeah. Yeah they would , um you would make the , the product as you normally would , apart from the , the bits that hold it all together would be made out of this shape-memory alloy. And that's the part that would um allow all the other parts to be separated at the end. I mean , the user would return the p product to the company , 'cause it's the product's responsibility to get rid of what they've made. Um , and then the company could then just use , make use of this shape-memory alloys to split up the components ,

and then either reuse some bits , and other bits which are obviously gonna wear out with time , or not usable , they might be like be able to put into scrap metal. Something like the case , if it's scratched or something , you would want to reuse it , but you might be able to melt it down and reuse it again somewhere else.


Um  And then  Yeah. Yeah they would , um you would make the , the product as you normally would , apart from the , the bits that hold it all together would be made out of this shape-memory alloy. And that's the part that would um allow all the other parts to be separated at the end. I mean , the user would return the p product to the company , 'cause it's the product's responsibility to get rid of what they've made. Um , and then the company could then just use , make use of this shape-memory alloys to split up the components ,

and then either reuse some bits , and other bits which are obviously gonna wear out with time , or not usable , they might be like be able to put into scrap metal. Something like the case , if it's scratched or something , you would want to reuse it , but you might be able to melt it down and reuse it again somewhere else.

Mm-hmm. Would we be the company that would break down these , or uh metals ? Or would we contribute to another group ?


and then either reuse some bits , and other bits which are obviously gonna wear out with time , or not usable , they might be like be able to put into scrap metal. Something like the case , if it's scratched or something , you would want to reuse it , but you might be able to melt it down and reuse it again somewhere else.

Mm-hmm. Would we be the company that would break down these , or uh metals ? Or would we contribute to another group ?

You could  we could probably empl em employ a , a side company or something to do that for us. But it would be our responsibility to get that done and to dispose of the products that we made. For a certain percentage at least.


Mm-hmm. Would we be the company that would break down these , or uh metals ? Or would we contribute to another group ?

You could  we could probably empl em employ a , a side company or something to do that for us. But it would be our responsibility to get that done and to dispose of the products that we made. For a certain percentage at least.

Not every , not a hundred percent of everything we produce ,


You could  we could probably empl em employ a , a side company or something to do that for us. But it would be our responsibility to get that done and to dispose of the products that we made. For a certain percentage at least.

Not every , not a hundred percent of everything we produce ,

Okay. This sounds like a really great idea.


Not every , not a hundred percent of everything we produce ,

Okay. This sounds like a really great idea.


Not every , not a hundred percent of everything we produce ,

Okay. This sounds like a really great idea.

One thing we have to consider is our uh one hundred percent um turnover goal that we have for our financial sector.


Okay. This sounds like a really great idea.

One thing we have to consider is our uh one hundred percent um turnover goal that we have for our financial sector.


One thing we have to consider is our uh one hundred percent um turnover goal that we have for our financial sector.

Um , so we'll have to investigate how much that will cost us , cost the company ,


Um , so we'll have to investigate how much that will cost us , cost the company ,

um 'cause it sounds very labour-intensive. You would have to hire a number of people , and it might be more expensive.


Um , so we'll have to investigate how much that will cost us , cost the company ,

um 'cause it sounds very labour-intensive. You would have to hire a number of people , and it might be more expensive.

Well I  the fact of the shape-memory alloys is that they , they don't need to be manually de um deconstructed. Like , you don't have to individually um unscrew all the screws. Because of this , their properties are smart material.


um 'cause it sounds very labour-intensive. You would have to hire a number of people , and it might be more expensive.

Well I  the fact of the shape-memory alloys is that they , they don't need to be manually de um deconstructed. Like , you don't have to individually um unscrew all the screws. Because of this , their properties are smart material.


um 'cause it sounds very labour-intensive. You would have to hire a number of people , and it might be more expensive.

Well I  the fact of the shape-memory alloys is that they , they don't need to be manually de um deconstructed. Like , you don't have to individually um unscrew all the screws. Because of this , their properties are smart material.

All you need is just the heat , so they self-destruct themselves.


Well I  the fact of the shape-memory alloys is that they , they don't need to be manually de um deconstructed. Like , you don't have to individually um unscrew all the screws. Because of this , their properties are smart material.

All you need is just the heat , so they self-destruct themselves.

Alright. We'll still have to investigate the financial implications.


All you need is just the heat , so they self-destruct themselves.

Alright. We'll still have to investigate the financial implications.

So I suppose it does need like high contact , yeah , you know high uh quality machinery , and very specific machinery , but


All you need is just the heat , so they self-destruct themselves.

Alright. We'll still have to investigate the financial implications.

So I suppose it does need like high contact , yeah , you know high uh quality machinery , and very specific machinery , but

Alright. I like the environmental approach. Um , we'll have to see if that can meet our financial goals as well.


Alright. We'll still have to investigate the financial implications.

So I suppose it does need like high contact , yeah , you know high uh quality machinery , and very specific machinery , but

Alright. I like the environmental approach. Um , we'll have to see if that can meet our financial goals as well.

Yeah. Okay. Um also there is um components. This'll be how it uh will actually work. But I haven't put this plan together yet.


So I suppose it does need like high contact , yeah , you know high uh quality machinery , and very specific machinery , but

Alright. I like the environmental approach. Um , we'll have to see if that can meet our financial goals as well.

Yeah. Okay. Um also there is um components. This'll be how it uh will actually work. But I haven't put this plan together yet.


Alright. I like the environmental approach. Um , we'll have to see if that can meet our financial goals as well.

Yeah. Okay. Um also there is um components. This'll be how it uh will actually work. But I haven't put this plan together yet.


Yeah. Okay. Um also there is um components. This'll be how it uh will actually work. But I haven't put this plan together yet.


. Sorry , should I go back. This would actually show the circuit diagram. Although I haven't come up with the final circuit yet.


. Sorry , should I go back. This would actually show the circuit diagram. Although I haven't come up with the final circuit yet.

So those are what , um , we'll c construct the remote. Those are all the


So those are what , um , we'll c construct the remote. Those are all the

Yeah. I it just shows what sort of energy source. It could be a battery , like rechargeable probably. Um , an' yeah , well how the infrared will actually be sent through the chip to be received by the chip on the television set itself.


So those are what , um , we'll c construct the remote. Those are all the

Yeah. I it just shows what sort of energy source. It could be a battery , like rechargeable probably. Um , an' yeah , well how the infrared will actually be sent through the chip to be received by the chip on the television set itself.

Okay ? So , now is it F_ eight again to escape ? Or escape ? There we go. Okay.


Yeah. I it just shows what sort of energy source. It could be a battery , like rechargeable probably. Um , an' yeah , well how the infrared will actually be sent through the chip to be received by the chip on the television set itself.

Okay ? So , now is it F_ eight again to escape ? Or escape ? There we go. Okay.

Alright. Thank you very much. And , um , the next presenter will be Tara.


Okay ? So , now is it F_ eight again to escape ? Or escape ? There we go. Okay.

Alright. Thank you very much. And , um , the next presenter will be Tara.


Alright. Thank you very much. And , um , the next presenter will be Tara.


Do you think  Is it uh , function eight yeah ?


Okay. I'm the User  um User Interface Designer. Uh , the technical functions design of the apparatus is the effect the apparatus should have. Um , in this case it's the function of the remote control , which is to send messages to the television , television set. By taking inspiration from other similar designs , we'll try and come up with an original trendy remote control , which is sellable international. There're two functional design options. A multifunctional remote control , which can be used for several entertainment devices. And a single function remote control , used specifically for the television.


Okay. I'm the User  um User Interface Designer. Uh , the technical functions design of the apparatus is the effect the apparatus should have. Um , in this case it's the function of the remote control , which is to send messages to the television , television set. By taking inspiration from other similar designs , we'll try and come up with an original trendy remote control , which is sellable international. There're two functional design options. A multifunctional remote control , which can be used for several entertainment devices. And a single function remote control , used specifically for the television.

I'm sorry , what was that last one. Multifunctional and


Okay. I'm the User  um User Interface Designer. Uh , the technical functions design of the apparatus is the effect the apparatus should have. Um , in this case it's the function of the remote control , which is to send messages to the television , television set. By taking inspiration from other similar designs , we'll try and come up with an original trendy remote control , which is sellable international. There're two functional design options. A multifunctional remote control , which can be used for several entertainment devices. And a single function remote control , used specifically for the television.

I'm sorry , what was that last one. Multifunctional and

Sorry. Um , a single function just for the television itself.


I'm sorry , what was that last one. Multifunctional and

Sorry. Um , a single function just for the television itself.

Yeah. Um , multifunctional controls can be difficult to use , as the multitude of buttons can be confusing. A single function remote control is simpler to use , but it means you have to have other remote controls for your other entertainment devices.


Sorry. Um , a single function just for the television itself.

Yeah. Um , multifunctional controls can be difficult to use , as the multitude of buttons can be confusing. A single function remote control is simpler to use , but it means you have to have other remote controls for your other entertainment devices.


Yeah. Um , multifunctional controls can be difficult to use , as the multitude of buttons can be confusing. A single function remote control is simpler to use , but it means you have to have other remote controls for your other entertainment devices.

Um , I think that a single function remote control would be preferable , because it's easier to use. It'd be more compatible with a range of television sets , making it more internationally sellable. Um , it will make an original design more obtainable , as we have less functional necessities to include in the design. And it would be more profitable as it would be more simplistic. And less functions would have to be included. So it would be cheaper to make. And probably more sellable just because it's more compatible with a r a wider range of devices. Does anyone have any questions ?


Yeah. Um , multifunctional controls can be difficult to use , as the multitude of buttons can be confusing. A single function remote control is simpler to use , but it means you have to have other remote controls for your other entertainment devices.

Um , I think that a single function remote control would be preferable , because it's easier to use. It'd be more compatible with a range of television sets , making it more internationally sellable. Um , it will make an original design more obtainable , as we have less functional necessities to include in the design. And it would be more profitable as it would be more simplistic. And less functions would have to be included. So it would be cheaper to make. And probably more sellable just because it's more compatible with a r a wider range of devices. Does anyone have any questions ?

So as far as we know , um , a single function television remote control is us usable internationally ?


Um , I think that a single function remote control would be preferable , because it's easier to use. It'd be more compatible with a range of television sets , making it more internationally sellable. Um , it will make an original design more obtainable , as we have less functional necessities to include in the design. And it would be more profitable as it would be more simplistic. And less functions would have to be included. So it would be cheaper to make. And probably more sellable just because it's more compatible with a r a wider range of devices. Does anyone have any questions ?

So as far as we know , um , a single function television remote control is us usable internationally ?

Well , it's just that , when we're creating it , we're , we have to make it um compatible with different brands of devices.


So as far as we know , um , a single function television remote control is us usable internationally ?

Well , it's just that , when we're creating it , we're , we have to make it um compatible with different brands of devices.

And it would be easier to make it compatible with just different brands of television devices rather than other ent ,


Well , it's just that , when we're creating it , we're , we have to make it um compatible with different brands of devices.

And it would be easier to make it compatible with just different brands of television devices rather than other ent ,


And it would be easier to make it compatible with just different brands of television devices rather than other ent ,


Does everyone agree with this ? Does anyone object and , and find the multifunctional might be a better way to go ?


Does everyone agree with this ? Does anyone object and , and find the multifunctional might be a better way to go ?

Um ,  I was just wondering about the , what , what Genevieve said before , about having like some hidden controls like having the outer casing. And that would probably , um , I d , well well what you said before about it being a more profitable simplistic design. I suppose having that would complicate it a lot more.


Um ,  I was just wondering about the , what , what Genevieve said before , about having like some hidden controls like having the outer casing. And that would probably , um , I d , well well what you said before about it being a more profitable simplistic design. I suppose having that would complicate it a lot more.

Yeah , I think I agree with the single design thing for now , because we're trying to do so much , that if we're trying to make a unique , user-friendly , dadada , and it's also multi also multifunctional , um , we're gonna go over budget for one thing.


Yeah , I think I agree with the single design thing for now , because we're trying to do so much , that if we're trying to make a unique , user-friendly , dadada , and it's also multi also multifunctional , um , we're gonna go over budget for one thing.


Yeah. And with this we'll have more room in the budget probably to make a more original design.


Yeah. And with this we'll have more room in the budget probably to make a more original design.

We'll have more money to go into the design side of it.


We'll have more money to go into the design side of it.


are you ready for your presentation Genevieve ?


are you ready for your presentation Genevieve ?

Fabulous. Except you're not hooked up to the


Fabulous. Except you're not hooked up to the

I'm not hooked up , but other than that , completely ready.


I'm not hooked up , but other than that , completely ready.

Okay. Okay. Oh. I just lost my microphone.


Okay. Okay. Oh. I just lost my microphone.


Okay. So I'll be discussing the functional requirements of this remote control. Um , and I'll give you a little briefing on what that means exactly. Um , if you all remember from the email we got before our very first uh kick-off meeting , with the coffee machine ? The functional requirements of that was to produce hot coffee quickly. Um , so what I'll be talking about now is the equivalent for a remote control. Um , so basically what needs and desires are to be fulfilled. Um , I've done some marketing research , a lot of interviews with remote control users , um , and some internet research. And I'll show you my findings. Oh , and firstly I wanted to remind you about our company motto and purpose. So we believe in providing international market with fashionable products. Um , hence our motto , we put the fashion in electronics. So I think that should be our priority here. Um , and we should also be looking to trends in clothing and interior design. Not just in electronic fashion. So that it's something that fits in the household.


Okay. So I'll be discussing the functional requirements of this remote control. Um , and I'll give you a little briefing on what that means exactly. Um , if you all remember from the email we got before our very first uh kick-off meeting , with the coffee machine ? The functional requirements of that was to produce hot coffee quickly. Um , so what I'll be talking about now is the equivalent for a remote control. Um , so basically what needs and desires are to be fulfilled. Um , I've done some marketing research , a lot of interviews with remote control users , um , and some internet research. And I'll show you my findings. Oh , and firstly I wanted to remind you about our company motto and purpose. So we believe in providing international market with fashionable products. Um , hence our motto , we put the fashion in electronics. So I think that should be our priority here. Um , and we should also be looking to trends in clothing and interior design. Not just in electronic fashion. So that it's something that fits in the household.

I'm sorry , what was that last thing that you just said ?


Okay. So I'll be discussing the functional requirements of this remote control. Um , and I'll give you a little briefing on what that means exactly. Um , if you all remember from the email we got before our very first uh kick-off meeting , with the coffee machine ? The functional requirements of that was to produce hot coffee quickly. Um , so what I'll be talking about now is the equivalent for a remote control. Um , so basically what needs and desires are to be fulfilled. Um , I've done some marketing research , a lot of interviews with remote control users , um , and some internet research. And I'll show you my findings. Oh , and firstly I wanted to remind you about our company motto and purpose. So we believe in providing international market with fashionable products. Um , hence our motto , we put the fashion in electronics. So I think that should be our priority here. Um , and we should also be looking to trends in clothing and interior design. Not just in electronic fashion. So that it's something that fits in the household.

I'm sorry , what was that last thing that you just said ?

Um , we should be looking towards trends in both clothing and interior design.


I'm sorry , what was that last thing that you just said ?

Um , we should be looking towards trends in both clothing and interior design.

Any trends that are going on in , in the public , even media ,