Well you 're  you 're basically y Yeah. So you 're making all your training data more uniform.

Exactly. And if  if then  if this data becomes noisy , it b it becomes eff effectively becomes less noisy basically.

But of course you cannot add too much noise because then you 'll s then you 're clean recognition goes down , but I mean it 's yet to be seen how much , it 's a very simple technique.


Exactly. And if  if then  if this data becomes noisy , it b it becomes eff effectively becomes less noisy basically.

But of course you cannot add too much noise because then you 'll s then you 're clean recognition goes down , but I mean it 's yet to be seen how much , it 's a very simple technique.


But of course you cannot add too much noise because then you 'll s then you 're clean recognition goes down , but I mean it 's yet to be seen how much , it 's a very simple technique.

Yes indeed it 's a very simple technique , you just take your spectrum and  and use whatever is coming from FFT ,  add constant ,


Yes indeed it 's a very simple technique , you just take your spectrum and  and use whatever is coming from FFT ,  add constant ,

you know ? on  onto power spectrum. That  that  Or the other thing is of course if you have a spectrum , what you can s start doing , you can leave  start leaving out the p the parts which are uh uh low in energy and then perhaps uh one could try to find a  a all - pole model to such a spectrum. Because a all - pole model will still try to  to  to put the  the continuation basically of the  of the model into these parts where the issue set to zero. That 's what we want to try. I have a visitor from Brno. He 's a  kind of like young faculty. pretty hard - working so he  so he 's  so he 's looking into that.


you know ? on  onto power spectrum. That  that  Or the other thing is of course if you have a spectrum , what you can s start doing , you can leave  start leaving out the p the parts which are uh uh low in energy and then perhaps uh one could try to find a  a all - pole model to such a spectrum. Because a all - pole model will still try to  to  to put the  the continuation basically of the  of the model into these parts where the issue set to zero. That 's what we want to try. I have a visitor from Brno. He 's a  kind of like young faculty. pretty hard - working so he  so he 's  so he 's looking into that.

And then most of the effort is uh now also aimed at this e e TRAP recognition. This uh  this is this recognition from temporal patterns.


And then most of the effort is uh now also aimed at this e e TRAP recognition. This uh  this is this recognition from temporal patterns.


The TRAPS sound familiar , I  but I don't


The TRAPS sound familiar , I  but I don't

Yeah I mean tha This is familiar like sort of because we gave you the name , but , what it is , is that normally what you do is that you recognize uh speech based on a shortened spectrum.


The TRAPS sound familiar , I  but I don't

Yeah I mean tha This is familiar like sort of because we gave you the name , but , what it is , is that normally what you do is that you recognize uh speech based on a shortened spectrum.

Essentially L P - LPC , mel cepstrum , uh , everything starts with a spectral slice. Uh so if you s So , given the spectrogram you essentially are sliding  sliding the spectrogram along the uh f frequency axis


Yeah I mean tha This is familiar like sort of because we gave you the name , but , what it is , is that normally what you do is that you recognize uh speech based on a shortened spectrum.

Essentially L P - LPC , mel cepstrum , uh , everything starts with a spectral slice. Uh so if you s So , given the spectrogram you essentially are sliding  sliding the spectrogram along the uh f frequency axis


Essentially L P - LPC , mel cepstrum , uh , everything starts with a spectral slice. Uh so if you s So , given the spectrogram you essentially are sliding  sliding the spectrogram along the uh f frequency axis

and you keep shifting this thing , and you have a spectrogram.


and you keep shifting this thing , and you have a spectrogram.

So you can say " well you can also take the time trajectory of the energy at a given frequency " , and what you get is then , that you get a p  vector.


So you can say " well you can also take the time trajectory of the energy at a given frequency " , and what you get is then , that you get a p  vector.

And this vector can be a  a  s assigned to s some phoneme. Namely you can say i it  I will  I will say that this vector will eh  will  will describe the phoneme which is in the center of the vector. And you can try to classify based on that.


And this vector can be a  a  s assigned to s some phoneme. Namely you can say i it  I will  I will say that this vector will eh  will  will describe the phoneme which is in the center of the vector. And you can try to classify based on that.

And you  so you classi so it 's a very different vector , very different properties , we don't know much about it , but the truth is


And this vector can be a  a  s assigned to s some phoneme. Namely you can say i it  I will  I will say that this vector will eh  will  will describe the phoneme which is in the center of the vector. And you can try to classify based on that.

And you  so you classi so it 's a very different vector , very different properties , we don't know much about it , but the truth is

Hmm. But you have many of those vectors per phoneme ,


And you  so you classi so it 's a very different vector , very different properties , we don't know much about it , but the truth is

Hmm. But you have many of those vectors per phoneme ,


Hmm. But you have many of those vectors per phoneme ,

And then you can start dec thinking about how to combine these decisions. Exactly , that 's what  yeah , that 's what it is.


And then you can start dec thinking about how to combine these decisions. Exactly , that 's what  yeah , that 's what it is.


And then you can start dec thinking about how to combine these decisions. Exactly , that 's what  yeah , that 's what it is.

Because if you run this uh recognition , you get  you still get about twenty percent error  uh twenty percent correct. You know ,


Because if you run this uh recognition , you get  you still get about twenty percent error  uh twenty percent correct. You know ,

on  on like for the frame by frame basis , so  uh  uh so it 's much better than chance.


on  on like for the frame by frame basis , so  uh  uh so it 's much better than chance.

That 's another thing. Well c currently we start  I mean we start always with critical band spectrum. For various reasons. But uh the latest uh observation uh is that you  you  you are  you can get quite a big advantage of using two critical bands at the same time.


That 's another thing. Well c currently we start  I mean we start always with critical band spectrum. For various reasons. But uh the latest uh observation uh is that you  you  you are  you can get quite a big advantage of using two critical bands at the same time.


And the reasons  there are some reasons for that. Because there are some reasons I can  I could talk about , will have to tell you about things like masking experiments which uh uh uh uh yield critical bands , and also experiments with release of masking , which actually tell you that something is happening across critical bands , across bands. And


And the reasons  there are some reasons for that. Because there are some reasons I can  I could talk about , will have to tell you about things like masking experiments which uh uh uh uh yield critical bands , and also experiments with release of masking , which actually tell you that something is happening across critical bands , across bands. And

Well how do you  how do you uh convert this uh energy over time in a particular frequency band into a vector of numbers ?


And the reasons  there are some reasons for that. Because there are some reasons I can  I could talk about , will have to tell you about things like masking experiments which uh uh uh uh yield critical bands , and also experiments with release of masking , which actually tell you that something is happening across critical bands , across bands. And

Well how do you  how do you uh convert this uh energy over time in a particular frequency band into a vector of numbers ?

It 's uh uh uh I mean time T - zero is one number ,  time t


And the reasons  there are some reasons for that. Because there are some reasons I can  I could talk about , will have to tell you about things like masking experiments which uh uh uh uh yield critical bands , and also experiments with release of masking , which actually tell you that something is happening across critical bands , across bands. And

Well how do you  how do you uh convert this uh energy over time in a particular frequency band into a vector of numbers ?

It 's uh uh uh I mean time T - zero is one number ,  time t

Yeah but what 's the number ? Is it just the


Well how do you  how do you uh convert this uh energy over time in a particular frequency band into a vector of numbers ?

It 's uh uh uh I mean time T - zero is one number ,  time t

Yeah but what 's the number ? Is it just the

It 's a spectral energy , logarithmic spectral energy ,


It 's uh uh uh I mean time T - zero is one number ,  time t

Yeah but what 's the number ? Is it just the

It 's a spectral energy , logarithmic spectral energy ,

it 's just the amount of energy in that band from f in that time interval.


Yeah but what 's the number ? Is it just the

It 's a spectral energy , logarithmic spectral energy ,

it 's just the amount of energy in that band from f in that time interval.


It 's a spectral energy , logarithmic spectral energy ,

it 's just the amount of energy in that band from f in that time interval.


it 's just the amount of energy in that band from f in that time interval.

And that 's what  that 's what I 'm saying then , so this is a  this is a starting vector. It 's just like shortened f  spectrum , or something. But now we are trying to understand what this vector actually represents ,


And that 's what  that 's what I 'm saying then , so this is a  this is a starting vector. It 's just like shortened f  spectrum , or something. But now we are trying to understand what this vector actually represents ,


And that 's what  that 's what I 'm saying then , so this is a  this is a starting vector. It 's just like shortened f  spectrum , or something. But now we are trying to understand what this vector actually represents ,

for instance a question is like " how correlated are the elements of this vector ? " Turns out they are quite correlated , because I mean , especially the neighboring ones , right ? They  they represent the same  almost the same configuration of the vocal tract.


for instance a question is like " how correlated are the elements of this vector ? " Turns out they are quite correlated , because I mean , especially the neighboring ones , right ? They  they represent the same  almost the same configuration of the vocal tract.

So there 's a very high correlation. So the classifiers which use the diagonal covariance matrix don't like it. So we 're thinking about de - correlating them.


So there 's a very high correlation. So the classifiers which use the diagonal covariance matrix don't like it. So we 're thinking about de - correlating them.

Then the question is uh " can you describe elements of this vector by Gaussian distributions " , or to what extent ? Because uh  And  and  and so on and so on. So we are learning quite a lot about that. And then another issue is how many vectors we should be using ,


Then the question is uh " can you describe elements of this vector by Gaussian distributions " , or to what extent ? Because uh  And  and  and so on and so on. So we are learning quite a lot about that. And then another issue is how many vectors we should be using ,


But I mean is the  is the critical band the right uh uh dimension ? So we somehow made arbitrary decision , " yes ". Then  but then now we are thinking a lot how to  uh how to use at least the neighboring band because that seems to be happening  This I somehow start to believe that 's what 's happening in recognition. Cuz a lot of experiments point to the fact that people can split the signal into critical bands , but then oh uh uh so you can  you are quite capable of processing a signal in uh uh independently in individual critical bands. That 's what masking experiments tell you. But at the same time you most likely pay attention to at least neighboring bands when you are making any decisions , you compare what 's happening in  in this band to what 's happening to the band  to  to  to the  to the neighboring bands. And that 's how you make uh decisions. That 's why the articulatory events , which uh F F Fletcher talks about , they are about two critical bands. You need at least two , basically. You need some relative , relative relation.


But I mean is the  is the critical band the right uh uh dimension ? So we somehow made arbitrary decision , " yes ". Then  but then now we are thinking a lot how to  uh how to use at least the neighboring band because that seems to be happening  This I somehow start to believe that 's what 's happening in recognition. Cuz a lot of experiments point to the fact that people can split the signal into critical bands , but then oh uh uh so you can  you are quite capable of processing a signal in uh uh independently in individual critical bands. That 's what masking experiments tell you. But at the same time you most likely pay attention to at least neighboring bands when you are making any decisions , you compare what 's happening in  in this band to what 's happening to the band  to  to  to the  to the neighboring bands. And that 's how you make uh decisions. That 's why the articulatory events , which uh F F Fletcher talks about , they are about two critical bands. You need at least two , basically. You need some relative , relative relation.

Absolute number doesn't tell you the right thing.


Absolute number doesn't tell you the right thing.

You need to  you need to compare it to something else , what 's happening but it 's what 's happening in the  in the close neighborhood. So if you are making decision what 's happening at one kilohertz , you want to know what 's happening at nine hundred hertz and it  and maybe at eleven hundred hertz , but you don't much care what 's happening at three kilohertz.


Absolute number doesn't tell you the right thing.

You need to  you need to compare it to something else , what 's happening but it 's what 's happening in the  in the close neighborhood. So if you are making decision what 's happening at one kilohertz , you want to know what 's happening at nine hundred hertz and it  and maybe at eleven hundred hertz , but you don't much care what 's happening at three kilohertz.

So it 's really w It 's sort of like saying that what 's happening at one kilohertz depends on what 's happening around it. It 's sort of relative to it.


You need to  you need to compare it to something else , what 's happening but it 's what 's happening in the  in the close neighborhood. So if you are making decision what 's happening at one kilohertz , you want to know what 's happening at nine hundred hertz and it  and maybe at eleven hundred hertz , but you don't much care what 's happening at three kilohertz.

So it 's really w It 's sort of like saying that what 's happening at one kilohertz depends on what 's happening around it. It 's sort of relative to it.

To some extent , it  that is also true. Yeah. But it 's  but for  but for instance ,  th uh  uh what  what uh humans are very much capable of doing is that if th if they are exactly the same thing happening in two neighboring critical bands , recognition can discard it.


So it 's really w It 's sort of like saying that what 's happening at one kilohertz depends on what 's happening around it. It 's sort of relative to it.

To some extent , it  that is also true. Yeah. But it 's  but for  but for instance ,  th uh  uh what  what uh humans are very much capable of doing is that if th if they are exactly the same thing happening in two neighboring critical bands , recognition can discard it.


To some extent , it  that is also true. Yeah. But it 's  but for  but for instance ,  th uh  uh what  what uh humans are very much capable of doing is that if th if they are exactly the same thing happening in two neighboring critical bands , recognition can discard it.


Hey ! OK , we need us another  another voice here.


And so so  so for instance if you d if you a if you add the noise that normally masks  masks the uh  the  the signal right ?


And so so  so for instance if you d if you a if you add the noise that normally masks  masks the uh  the  the signal right ?

and you can show that in  that if the  if you add the noise outside the critical band , that doesn't affect the  the decisions you 're making about a signal within a critical band.


and you can show that in  that if the  if you add the noise outside the critical band , that doesn't affect the  the decisions you 're making about a signal within a critical band.

Unless this noise is modulated. If the noise is modulated , with the same modulation frequency as the noise in a critical band , the amount of masking is less. The moment you  moment you provide the noise in n neighboring critical bands.


Unless this noise is modulated. If the noise is modulated , with the same modulation frequency as the noise in a critical band , the amount of masking is less. The moment you  moment you provide the noise in n neighboring critical bands.

So the s m masking curve , normally it looks like sort of  I start from  from here , so you   you have uh no noise then you  you  you are expanding the critical band , so the amount of maching is increasing. And when you e hit a certain point , which is a critical band , then the amount of masking is the same.


So the s m masking curve , normally it looks like sort of  I start from  from here , so you   you have uh no noise then you  you  you are expanding the critical band , so the amount of maching is increasing. And when you e hit a certain point , which is a critical band , then the amount of masking is the same.

So that 's the famous experiment of Fletcher , a long time ago. Like that 's where people started thinking " wow this is interesting ! " So.


So that 's the famous experiment of Fletcher , a long time ago. Like that 's where people started thinking " wow this is interesting ! " So.

But , if you  if you  if you modulate the noise , the masking goes up and the moment you start hitting the  another critical band , the masking goes down. So essentially  essentially that 's a very clear indication that  that  that  cognition can take uh uh into consideration what 's happening in the neighboring bands. But if you go too far in a  in a  if you  if the noise is very broad , you are not increasing much more , so  so if you  if you are far away from the signal  uh from the signal f uh the frequency at which the signal is , then the m even the  when the noise is co - modulated it  it 's not helping you much.


But , if you  if you  if you modulate the noise , the masking goes up and the moment you start hitting the  another critical band , the masking goes down. So essentially  essentially that 's a very clear indication that  that  that  cognition can take uh uh into consideration what 's happening in the neighboring bands. But if you go too far in a  in a  if you  if the noise is very broad , you are not increasing much more , so  so if you  if you are far away from the signal  uh from the signal f uh the frequency at which the signal is , then the m even the  when the noise is co - modulated it  it 's not helping you much.


But , if you  if you  if you modulate the noise , the masking goes up and the moment you start hitting the  another critical band , the masking goes down. So essentially  essentially that 's a very clear indication that  that  that  cognition can take uh uh into consideration what 's happening in the neighboring bands. But if you go too far in a  in a  if you  if the noise is very broad , you are not increasing much more , so  so if you  if you are far away from the signal  uh from the signal f uh the frequency at which the signal is , then the m even the  when the noise is co - modulated it  it 's not helping you much.

So. So things like this we are kind of playing with  with  with the hope that perhaps we could eventually u use this in a  in a real recognizer.


So. So things like this we are kind of playing with  with  with the hope that perhaps we could eventually u use this in a  in a real recognizer.


So. So things like this we are kind of playing with  with  with the hope that perhaps we could eventually u use this in a  in a real recognizer.

Like uh partially of course we promised to do this under the  the  the Aurora uh program.


So. So things like this we are kind of playing with  with  with the hope that perhaps we could eventually u use this in a  in a real recognizer.

Like uh partially of course we promised to do this under the  the  the Aurora uh program.

But you probably won't have anything before the next time we have to evaluate ,


Like uh partially of course we promised to do this under the  the  the Aurora uh program.

But you probably won't have anything before the next time we have to evaluate ,


But you probably won't have anything before the next time we have to evaluate ,

Well , maybe , most likely we will not have anything which c would comply with the rules.


Well , maybe , most likely we will not have anything which c would comply with the rules.


latency currently chops the require uh significant uh latency amount of processing ,


latency currently chops the require uh significant uh latency amount of processing ,

because uh we don't know any better , yet , than to use the neural net classifiers , uh and uh  and uh TRAPS.


because uh we don't know any better , yet , than to use the neural net classifiers , uh and uh  and uh TRAPS.


because uh we don't know any better , yet , than to use the neural net classifiers , uh and uh  and uh TRAPS.

Though the  the work which uh everybody is looking at now aims at s trying to find out what to do with these vectors , so that a g simple Gaussian classifier would be happier with it.


Though the  the work which uh everybody is looking at now aims at s trying to find out what to do with these vectors , so that a g simple Gaussian classifier would be happier with it.


Though the  the work which uh everybody is looking at now aims at s trying to find out what to do with these vectors , so that a g simple Gaussian classifier would be happier with it.

or to what extent a Gaussian classifier should be unhappy uh that , and how to Gaussian - ize the vectors , and


or to what extent a Gaussian classifier should be unhappy uh that , and how to Gaussian - ize the vectors , and


or to what extent a Gaussian classifier should be unhappy uh that , and how to Gaussian - ize the vectors , and

So this is uh what 's happening. Then Sunil is uh uh uh asked me f for one month 's vacation and since he did not take any vacation for two years , I had no  I didn't have heart to tell him no. So he 's in India.


So this is uh what 's happening. Then Sunil is uh uh uh asked me f for one month 's vacation and since he did not take any vacation for two years , I had no  I didn't have heart to tell him no. So he 's in India.


Uh well , he may be looking for a girl , for  for I don't  I don't  I don't ask. I know that Naran - when last time Narayanan did that he came back engaged.


Uh well , he may be looking for a girl , for  for I don't  I don't  I don't ask. I know that Naran - when last time Narayanan did that he came back engaged.

Right. Well , I mean , I 've known other friends who  they  they go to Ind - they go back home to India for a month , they come back married ,


Right. Well , I mean , I 've known other friends who  they  they go to Ind - they go back home to India for a month , they come back married ,

and then of course then what happened with Narayanan was that he start pushing me that he needs to get a PHD because they wouldn't give him his wife. And she 's very pretty and he loves her and so  so we had to really


and then of course then what happened with Narayanan was that he start pushing me that he needs to get a PHD because they wouldn't give him his wife. And she 's very pretty and he loves her and so  so we had to really

So he finally had some incentive to finish ,


and then of course then what happened with Narayanan was that he start pushing me that he needs to get a PHD because they wouldn't give him his wife. And she 's very pretty and he loves her and so  so we had to really

So he finally had some incentive to finish ,

Oh yeah. We had  well I had a incentive because he  he always had this plan except he never told me.


So he finally had some incentive to finish ,

Oh yeah. We had  well I had a incentive because he  he always had this plan except he never told me.

Sort of figured that  That was a uh that he uh he told me the day when we did very well at our NIST evaluations of speaker recognition , the technology , and he was involved there.


Oh yeah. We had  well I had a incentive because he  he always had this plan except he never told me.

Sort of figured that  That was a uh that he uh he told me the day when we did very well at our NIST evaluations of speaker recognition , the technology , and he was involved there.


Sort of figured that  That was a uh that he uh he told me the day when we did very well at our NIST evaluations of speaker recognition , the technology , and he was involved there.

We were  after presentation we were driving home and he told me.


We were  after presentation we were driving home and he told me.

Yeah. So I  I said " well , yeah , OK " so he took another  another three quarter of the year but uh he was out.


Yeah. So I  I said " well , yeah , OK " so he took another  another three quarter of the year but uh he was out.

So I  wouldn't surprise me if he has a plan like that , though  though uh Pratibha still needs to get out first.


So I  wouldn't surprise me if he has a plan like that , though  though uh Pratibha still needs to get out first.

Cuz Pratibha is there a  a year earlier.


Cuz Pratibha is there a  a year earlier.

And S and Satya needs to get out very first because he 's  he already has uh four years served , though one year he was getting masters. So. So.


And S and Satya needs to get out very first because he 's  he already has uh four years served , though one year he was getting masters. So. So.

So have the um  when is the next uh evaluation ? June or something ?


So have the um  when is the next uh evaluation ? June or something ?


So have the um  when is the next uh evaluation ? June or something ?

Uh there , we don't know about evaluation , next meeting is in June.


Uh there , we don't know about evaluation , next meeting is in June.


Uh there , we don't know about evaluation , next meeting is in June.

And uh uh but like getting  get together.


Uh there , we don't know about evaluation , next meeting is in June.

And uh uh but like getting  get together.

Oh , OK. Are people supposed to rerun their systems ,


And uh uh but like getting  get together.

Oh , OK. Are people supposed to rerun their systems ,


Oh , OK. Are people supposed to rerun their systems ,

I assume so. Uh yes , uh , but nobody even set up yet the  date for uh delivering uh endpointed data.


I assume so. Uh yes , uh , but nobody even set up yet the  date for uh delivering uh endpointed data.


I assume so. Uh yes , uh , but nobody even set up yet the  date for uh delivering uh endpointed data.

And this uh  that  that sort of stuff. But I uh , yeah , what I think would be of course extremely useful , if we can come to our next meeting and say " well you know we did get fifty percent improvement. If  if you are interested we eventually can tell you how " , but uh we can get fifty percent improvement.


And this uh  that  that sort of stuff. But I uh , yeah , what I think would be of course extremely useful , if we can come to our next meeting and say " well you know we did get fifty percent improvement. If  if you are interested we eventually can tell you how " , but uh we can get fifty percent improvement.

Because people will s will be saying it 's impossible.


And this uh  that  that sort of stuff. But I uh , yeah , what I think would be of course extremely useful , if we can come to our next meeting and say " well you know we did get fifty percent improvement. If  if you are interested we eventually can tell you how " , but uh we can get fifty percent improvement.

Because people will s will be saying it 's impossible.

Hmm. Do you know what the new baseline is ? Oh , I guess if you don't have


Because people will s will be saying it 's impossible.

Hmm. Do you know what the new baseline is ? Oh , I guess if you don't have

Twenty - two  t twenty  twenty - two percent better than the old baseline.


Hmm. Do you know what the new baseline is ? Oh , I guess if you don't have

Twenty - two  t twenty  twenty - two percent better than the old baseline.

u Yes. Yes. But I assume that it will be similar , I don't  I  I don't see the reason why it shouldn't be.


Twenty - two  t twenty  twenty - two percent better than the old baseline.

u Yes. Yes. But I assume that it will be similar , I don't  I  I don't see the reason why it shouldn't be.


u Yes. Yes. But I assume that it will be similar , I don't  I  I don't see the reason why it shouldn't be.

I d I don't see reason why it should be worse.


I d I don't see reason why it should be worse.

Cuz if it is worse , then we will raise the objection ,


Cuz if it is worse , then we will raise the objection ,

we say " well you know how come ? " Because eh if we just use our voice activity detector , which we don't claim even that it 's wonderful , it 's just like one of them.


we say " well you know how come ? " Because eh if we just use our voice activity detector , which we don't claim even that it 's wonderful , it 's just like one of them.


we say " well you know how come ? " Because eh if we just use our voice activity detector , which we don't claim even that it 's wonderful , it 's just like one of them.

We get this sort of improvement , how come that we don't see it on  on  on  on your endpointed data ?


We get this sort of improvement , how come that we don't see it on  on  on  on your endpointed data ?


We get this sort of improvement , how come that we don't see it on  on  on  on your endpointed data ?

because the voice activity detector that I choosed is something that cheating , it 's using the alignment of the speech recognition system ,


because the voice activity detector that I choosed is something that cheating , it 's using the alignment of the speech recognition system ,


because the voice activity detector that I choosed is something that cheating , it 's using the alignment of the speech recognition system ,

and only the alignment on the clean channel , and then mapped this alignment to the noisy channel.


and only the alignment on the clean channel , and then mapped this alignment to the noisy channel.


and only the alignment on the clean channel , and then mapped this alignment to the noisy channel.

Well David told me  David told me yesterday or Harry actually he told Harry from QualComm and Harry uh brought up the suggestion we should still go for fifty percent he says are you aware that your system does only thirty percent uh comparing to  to endpointed baselines ? So they must have run already something.


Well David told me  David told me yesterday or Harry actually he told Harry from QualComm and Harry uh brought up the suggestion we should still go for fifty percent he says are you aware that your system does only thirty percent uh comparing to  to endpointed baselines ? So they must have run already something.


Well David told me  David told me yesterday or Harry actually he told Harry from QualComm and Harry uh brought up the suggestion we should still go for fifty percent he says are you aware that your system does only thirty percent uh comparing to  to endpointed baselines ? So they must have run already something.

So. And Harry said " Yeah. But I mean we think that we  we didn't say the last word yet , that we have other  other things which we can try. "


So. And Harry said " Yeah. But I mean we think that we  we didn't say the last word yet , that we have other  other things which we can try. "


So. And Harry said " Yeah. But I mean we think that we  we didn't say the last word yet , that we have other  other things which we can try. "

So. So there 's a lot of discussion now about this uh new criterion. Because Nokia was objecting , with uh QualComm 's  we basically supported that , we said " yes ".


So. So there 's a lot of discussion now about this uh new criterion. Because Nokia was objecting , with uh QualComm 's  we basically supported that , we said " yes ".

Now everybody else is saying " well you guys might  must be out of your mind. " uh The  Guenter Hirsch who d doesn't speak for Ericsson anymore because he is not with Ericsson and Ericsson may not  may withdraw from the whole Aurora activity because they have so many troubles now.


Now everybody else is saying " well you guys might  must be out of your mind. " uh The  Guenter Hirsch who d doesn't speak for Ericsson anymore because he is not with Ericsson and Ericsson may not  may withdraw from the whole Aurora activity because they have so many troubles now.

Ericsson 's laying off twenty percent of people.


Ericsson 's laying off twenty percent of people.


Ericsson 's laying off twenty percent of people.

Well Guenter is already  he got the job uh already was working on it for past two years or three years


Well Guenter is already  he got the job uh already was working on it for past two years or three years


Well Guenter is already  he got the job uh already was working on it for past two years or three years

he got a job uh at some  some Fachschule , the technical college not too far from Aachen.


he got a job uh at some  some Fachschule , the technical college not too far from Aachen.

So it 's like professor  u university professor


So it 's like professor  u university professor

you know , not quite a university , not quite a sort of  it 's not Aachen University , but it 's a good school and he  he 's happy.


you know , not quite a university , not quite a sort of  it 's not Aachen University , but it 's a good school and he  he 's happy.

And he  well , he was hoping to work uh with Ericsson like on t uh like consulting basis , but right now he says  says it doesn't look like that anybody is even thinking about speech recognition.


And he  well , he was hoping to work uh with Ericsson like on t uh like consulting basis , but right now he says  says it doesn't look like that anybody is even thinking about speech recognition.


So. So. But this is being now discussed right now , and it 's possible that uh  that  that it may get through , that we will still stick to fifty percent.


So. So. But this is being now discussed right now , and it 's possible that uh  that  that it may get through , that we will still stick to fifty percent.

But that means that nobody will probably get this im this improvement. yet , wi with the current system. Which event es essentially I think that we should be happy with because that  that would mean that at least people may be forced to look into alternative solutions


But that means that nobody will probably get this im this improvement. yet , wi with the current system. Which event es essentially I think that we should be happy with because that  that would mean that at least people may be forced to look into alternative solutions


But that means that nobody will probably get this im this improvement. yet , wi with the current system. Which event es essentially I think that we should be happy with because that  that would mean that at least people may be forced to look into alternative solutions

Mm - hmm. But maybe  I  I mean we are not too far from  from fifty percent , from the new baseline.


Mm - hmm. But maybe  I  I mean we are not too far from  from fifty percent , from the new baseline.


Mm - hmm. But maybe  I  I mean we are not too far from  from fifty percent , from the new baseline.

Which would mean like sixty percent over the current baseline , which is


Mm - hmm. But maybe  I  I mean we are not too far from  from fifty percent , from the new baseline.

Which would mean like sixty percent over the current baseline , which is

Yeah. Yes. Yes. We  we getting  we getting there , right.


Which would mean like sixty percent over the current baseline , which is

Yeah. Yes. Yes. We  we getting  we getting there , right.

Well. We are around fifty , fifty - five.


Yeah. Yes. Yes. We  we getting  we getting there , right.

Well. We are around fifty , fifty - five.


Well. We are around fifty , fifty - five.


Is it like sort of  is  How did you come up with this number ? If you improve twenty  by twenty percent the c the f the all baselines , it 's just a quick c comp co computation ?


Is it like sort of  is  How did you come up with this number ? If you improve twenty  by twenty percent the c the f the all baselines , it 's just a quick c comp co computation ?

Yeah , because it de it depends on the weightings


Yeah , because it de it depends on the weightings


Yeah , because it de it depends on the weightings

Hmm. How 's your documentation or whatever it w what was it you guys were working on last week ?


Hmm. How 's your documentation or whatever it w what was it you guys were working on last week ?

Yeah , finally we  we 've not finished with this. We stopped.


Yeah , finally we  we 've not finished with this. We stopped.

Ma - nec to need a little more time to improve the English , and maybe s to fill in something  some small detail , something like that ,


Ma - nec to need a little more time to improve the English , and maybe s to fill in something  some small detail , something like that ,


Yeah. Well , we have a document that explain a big part of the experiments ,


Yeah. Well , we have a document that explain a big part of the experiments ,

Necessary to  to include the bi the bibliography.


Necessary to  to include the bi the bibliography.

it 's not , yeah , finished yet. Mm - hmm.


it 's not , yeah , finished yet. Mm - hmm.

So have you been running some new experiments ? I  I thought I saw some jobs of yours running on some of the machine


it 's not , yeah , finished yet. Mm - hmm.

So have you been running some new experiments ? I  I thought I saw some jobs of yours running on some of the machine

Yeah. Right. We 've fff  done some strange things like removing C - zero or C - one from the    the vector of parameters , and we noticed that C - one is almost not useful at all. You can remove it from the vector , it doesn't hurt.


So have you been running some new experiments ? I  I thought I saw some jobs of yours running on some of the machine

Yeah. Right. We 've fff  done some strange things like removing C - zero or C - one from the    the vector of parameters , and we noticed that C - one is almost not useful at all. You can remove it from the vector , it doesn't hurt.


Yeah. Right. We 've fff  done some strange things like removing C - zero or C - one from the    the vector of parameters , and we noticed that C - one is almost not useful at all. You can remove it from the vector , it doesn't hurt.


So we were just discussing , since you mentioned that , in  it w


So we were just discussing , since you mentioned that , in  it w

driving in the car with Morgan this morning , we were discussing a good experiment for b for beginning graduate student who wants to run a lot of  who wants to get a lot of numbers on something


driving in the car with Morgan this morning , we were discussing a good experiment for b for beginning graduate student who wants to run a lot of  who wants to get a lot of numbers on something

which is , like , " imagine that you will  you will start putting every co any coefficient , which you are using in your vector , in some general power.


which is , like , " imagine that you will  you will start putting every co any coefficient , which you are using in your vector , in some general power.

General pow power. Like sort of you take a s power of two , or take a square root , or something.


General pow power. Like sort of you take a s power of two , or take a square root , or something.


General pow power. Like sort of you take a s power of two , or take a square root , or something.

So suppose that you are working with a s C - zer C - one.


So suppose that you are working with a s C - zer C - one.


So suppose that you are working with a s C - zer C - one.

So if you put it in a s square root , that effectively makes your model half as efficient. Because uh your uh Gaussian mixture model , right ? computes the mean.


So if you put it in a s square root , that effectively makes your model half as efficient. Because uh your uh Gaussian mixture model , right ? computes the mean.

And  and uh i i i but it 's  the mean is an exponent of the whatever , the  the  this Gaussian function.


And  and uh i i i but it 's  the mean is an exponent of the whatever , the  the  this Gaussian function.

So you 're compressing the range of this coefficient , so it 's becoming less efficient.


So you 're compressing the range of this coefficient , so it 's becoming less efficient.


So. So. Morgan was @ @ and he was  he was saying well this might be the alternative way how to play with a  with a fudge factor , you know , uh in the


So. So. Morgan was @ @ and he was  he was saying well this might be the alternative way how to play with a  with a fudge factor , you know , uh in the

you know , just compress the whole vector.


you know , just compress the whole vector.

And I said " well in that case why don't we just start compressing individual elements , like when  when  because in old days we were doing  when  when people still were doing template matching and Euclidean distances , we were doing this liftering of parameters , right ?


And I said " well in that case why don't we just start compressing individual elements , like when  when  because in old days we were doing  when  when people still were doing template matching and Euclidean distances , we were doing this liftering of parameters , right ?

because we observed that uh higher parameters were more important than lower for recognition. And basically the  the C - ze C - one contributes mainly slope ,


because we observed that uh higher parameters were more important than lower for recognition. And basically the  the C - ze C - one contributes mainly slope ,

and it 's highly affected by uh frequency response of the  of the recording equipment and that sort of thing ,


and it 's highly affected by uh frequency response of the  of the recording equipment and that sort of thing ,


and it 's highly affected by uh frequency response of the  of the recording equipment and that sort of thing ,

so  so we were coming with all these f various lifters.


so  so we were coming with all these f various lifters.


so  so we were coming with all these f various lifters.

uh Bell Labs had he  this uh uh r raised cosine lifter which still I think is built into H  HTK for reasons n unknown to anybody , but  but uh we had exponential lifter , or triangle lifter , basic number of lifters.


uh Bell Labs had he  this uh uh r raised cosine lifter which still I think is built into H  HTK for reasons n unknown to anybody , but  but uh we had exponential lifter , or triangle lifter , basic number of lifters.

And. But so they may be a way to  to fiddle with the f with the f


And. But so they may be a way to  to fiddle with the f with the f

Insertions , deletions , or the  the  giving a relative  uh basically modifying relative importance of the various parameters.


Insertions , deletions , or the  the  giving a relative  uh basically modifying relative importance of the various parameters.

The only of course problem is that there 's an infinite number of combinations and if the  if you s if y


Insertions , deletions , or the  the  giving a relative  uh basically modifying relative importance of the various parameters.

The only of course problem is that there 's an infinite number of combinations and if the  if you s if y

Oh. Uh - huh. You need like a  some kind of a


The only of course problem is that there 's an infinite number of combinations and if the  if you s if y

Oh. Uh - huh. You need like a  some kind of a

Yeah , you need a lot of graduate students , and a lot of computing power.


The only of course problem is that there 's an infinite number of combinations and if the  if you s if y

Oh. Uh - huh. You need like a  some kind of a

Yeah , you need a lot of graduate students , and a lot of computing power.

You need to have a genetic algorithm , that basically tries random permutations of these things.


Oh. Uh - huh. You need like a  some kind of a

Yeah , you need a lot of graduate students , and a lot of computing power.

You need to have a genetic algorithm , that basically tries random permutations of these things.

I know. Exactly. Oh. If you were at Bell Labs or  I d d I shouldn't be saying this in  on  on a mike , right ? Or I  uh  IBM , that 's what  maybe that 's what somebody would be doing.


Yeah , you need a lot of graduate students , and a lot of computing power.

You need to have a genetic algorithm , that basically tries random permutations of these things.

I know. Exactly. Oh. If you were at Bell Labs or  I d d I shouldn't be saying this in  on  on a mike , right ? Or I  uh  IBM , that 's what  maybe that 's what somebody would be doing.


You need to have a genetic algorithm , that basically tries random permutations of these things.

I know. Exactly. Oh. If you were at Bell Labs or  I d d I shouldn't be saying this in  on  on a mike , right ? Or I  uh  IBM , that 's what  maybe that 's what somebody would be doing.


I know. Exactly. Oh. If you were at Bell Labs or  I d d I shouldn't be saying this in  on  on a mike , right ? Or I  uh  IBM , that 's what  maybe that 's what somebody would be doing.

Oh , I mean , I mean the places which have a lot of computing power , so because it is really it 's a p it 's a  it 's  it will be reasonable search


Oh , I mean , I mean the places which have a lot of computing power , so because it is really it 's a p it 's a  it 's  it will be reasonable search


Oh , I mean , I mean the places which have a lot of computing power , so because it is really it 's a p it 's a  it 's  it will be reasonable search

uh but I wonder if there isn't some way of doing this uh search like when we are searching say for best discriminants.


Oh , I mean , I mean the places which have a lot of computing power , so because it is really it 's a p it 's a  it 's  it will be reasonable search

uh but I wonder if there isn't some way of doing this uh search like when we are searching say for best discriminants.

You know actually , I don't know that this wouldn't be all that bad. I mean you  you compute the features once ,


uh but I wonder if there isn't some way of doing this uh search like when we are searching say for best discriminants.

You know actually , I don't know that this wouldn't be all that bad. I mean you  you compute the features once ,


uh but I wonder if there isn't some way of doing this uh search like when we are searching say for best discriminants.

You know actually , I don't know that this wouldn't be all that bad. I mean you  you compute the features once ,

right ? And then these exponents are just applied to that


You know actually , I don't know that this wouldn't be all that bad. I mean you  you compute the features once ,

right ? And then these exponents are just applied to that

Absolutely. And hev everything is fixed.


right ? And then these exponents are just applied to that

Absolutely. And hev everything is fixed.


Absolutely. And hev everything is fixed.

And is this something that you would adjust for training ? or only recognition ?


And is this something that you would adjust for training ? or only recognition ?


Because essentially you are saying " uh this feature is not important ".


Because essentially you are saying " uh this feature is not important ".

Or less important , so that 's  th that 's a  that 's a painful one , yeah.


Because essentially you are saying " uh this feature is not important ".

Or less important , so that 's  th that 's a  that 's a painful one , yeah.

So for each  uh set of exponents that you would try , it would require a training and a recognition ?


Or less important , so that 's  th that 's a  that 's a painful one , yeah.

So for each  uh set of exponents that you would try , it would require a training and a recognition ?

Yeah. But  but wait a minute. You may not need to re uh uh retrain the m model. You just may n may need to c uh give uh less weight to  to uh a mod uh a component of the model which represents this particular feature. You don't have to retrain it.


Or less important , so that 's  th that 's a  that 's a painful one , yeah.

So for each  uh set of exponents that you would try , it would require a training and a recognition ?

Yeah. But  but wait a minute. You may not need to re uh uh retrain the m model. You just may n may need to c uh give uh less weight to  to uh a mod uh a component of the model which represents this particular feature. You don't have to retrain it.

Oh. So if you  Instead of altering the feature vectors themselves , you  you modify the  the  the Gaussians in the models.


So for each  uh set of exponents that you would try , it would require a training and a recognition ?

Yeah. But  but wait a minute. You may not need to re uh uh retrain the m model. You just may n may need to c uh give uh less weight to  to uh a mod uh a component of the model which represents this particular feature. You don't have to retrain it.

Oh. So if you  Instead of altering the feature vectors themselves , you  you modify the  the  the Gaussians in the models.

You just multiply. Yeah. Yep. You modify the Gaussian in the model , but in the  in the test data you would have to put it in the power , but in a training what you c in a training uh  in trained model , all you would have to do is to multiply a model by appropriate constant.


Yeah. But  but wait a minute. You may not need to re uh uh retrain the m model. You just may n may need to c uh give uh less weight to  to uh a mod uh a component of the model which represents this particular feature. You don't have to retrain it.

Oh. So if you  Instead of altering the feature vectors themselves , you  you modify the  the  the Gaussians in the models.

You just multiply. Yeah. Yep. You modify the Gaussian in the model , but in the  in the test data you would have to put it in the power , but in a training what you c in a training uh  in trained model , all you would have to do is to multiply a model by appropriate constant.

Uh - huh. But why  if you 're  if you 're multi if you 're altering the model , why w in the test data , why would you have to muck with the uh cepstral coefficients ?


Oh. So if you  Instead of altering the feature vectors themselves , you  you modify the  the  the Gaussians in the models.

You just multiply. Yeah. Yep. You modify the Gaussian in the model , but in the  in the test data you would have to put it in the power , but in a training what you c in a training uh  in trained model , all you would have to do is to multiply a model by appropriate constant.

Uh - huh. But why  if you 're  if you 're multi if you 're altering the model , why w in the test data , why would you have to muck with the uh cepstral coefficients ?

Because in uh test  in uh test data you ca don't have a model. You have uh only data. But in a  in a tr


You just multiply. Yeah. Yep. You modify the Gaussian in the model , but in the  in the test data you would have to put it in the power , but in a training what you c in a training uh  in trained model , all you would have to do is to multiply a model by appropriate constant.

Uh - huh. But why  if you 're  if you 're multi if you 're altering the model , why w in the test data , why would you have to muck with the uh cepstral coefficients ?

Because in uh test  in uh test data you ca don't have a model. You have uh only data. But in a  in a tr

No. But you 're running your data through that same model.


Uh - huh. But why  if you 're  if you 're multi if you 're altering the model , why w in the test data , why would you have to muck with the uh cepstral coefficients ?

Because in uh test  in uh test data you ca don't have a model. You have uh only data. But in a  in a tr

No. But you 're running your data through that same model.

That is true , but w I mean , so what you want to do  You want to say if uh obs you  if you observe something like Stephane observes , that C - one is not important , you can do two things.


Because in uh test  in uh test data you ca don't have a model. You have uh only data. But in a  in a tr

No. But you 're running your data through that same model.

That is true , but w I mean , so what you want to do  You want to say if uh obs you  if you observe something like Stephane observes , that C - one is not important , you can do two things.


No. But you 're running your data through that same model.

That is true , but w I mean , so what you want to do  You want to say if uh obs you  if you observe something like Stephane observes , that C - one is not important , you can do two things.

If you have a trained  trained recognizer , in the model , you know the  the  the  the component which  I  I mean di dimension  wh


That is true , but w I mean , so what you want to do  You want to say if uh obs you  if you observe something like Stephane observes , that C - one is not important , you can do two things.

If you have a trained  trained recognizer , in the model , you know the  the  the  the component which  I  I mean di dimension  wh

Mm - hmm. All of the  all of the mean and variances that correspond to C - one , you put them to zero.


If you have a trained  trained recognizer , in the model , you know the  the  the  the component which  I  I mean di dimension  wh

Mm - hmm. All of the  all of the mean and variances that correspond to C - one , you put them to zero.

To the s you  you know it. But what I 'm proposing now , if it is important but not as important , you multiply it by point one in a model.


Mm - hmm. All of the  all of the mean and variances that correspond to C - one , you put them to zero.

To the s you  you know it. But what I 'm proposing now , if it is important but not as important , you multiply it by point one in a model.


To the s you  you know it. But what I 'm proposing now , if it is important but not as important , you multiply it by point one in a model.

But what are you multiplying ? Cuz those are means , right ?


But what are you multiplying ? Cuz those are means , right ?

You 're multiplying the standard deviation ?


You 're multiplying the standard deviation ?

I think that you multiply the  I would  I would have to look in the  in the math , I mean how  how does the model uh


I think that you multiply the  I would  I would have to look in the  in the math , I mean how  how does the model uh


I think that you multiply the  I would  I would have to look in the  in the math , I mean how  how does the model uh

Yeah , I think you 'd have to modify the standard deviation or something , so that you make it  wider or narrower.


Yeah , I think you 'd have to modify the standard deviation or something , so that you make it  wider or narrower.


Effectively , that 's  that  that 's  I  Exactly. That 's what you do. That 's what you do , you  you  you modify the standard deviation as it was trained.


Effectively , that 's  that  that 's  I  Exactly. That 's what you do. That 's what you do , you  you  you modify the standard deviation as it was trained.

Effectively you , you know y in f in front of the  of the model , you put a constant. S yeah effectively what you 're doing is you  is you are modifying the  the  the deviation. Right ?


Effectively you , you know y in f in front of the  of the model , you put a constant. S yeah effectively what you 're doing is you  is you are modifying the  the  the deviation. Right ?


So by making th the standard deviation narrower ,  uh your scores get worse for


I mean there 's  you 're  you 're allowing for less variance.


I mean there 's  you 're  you 're allowing for less variance.

Yes , so you making this particular dimension less important. Because see what you are fitting is the multidimensional Gaussian , right ?


Yes , so you making this particular dimension less important. Because see what you are fitting is the multidimensional Gaussian , right ?

It 's a  it has  it has uh thirty - nine dimensions , or thirteen dimensions if you g ignore deltas and double - deltas.


It 's a  it has  it has uh thirty - nine dimensions , or thirteen dimensions if you g ignore deltas and double - deltas.

So in order  if you  in order to make dimension which  which Stephane sees uh less important , uh uh I mean not  not useful , less important , what you do is that this particular component in the model you can multiply by w you can  you can basically de - weight it in the model. But you can't do it in a  in a test data because you don't have a model for th I mean uh when the test comes , but what you can do is that you put this particular component in  and  and you compress it. That becomes uh th gets less variance , subsequently becomes less important.


It 's a  it has  it has uh thirty - nine dimensions , or thirteen dimensions if you g ignore deltas and double - deltas.

So in order  if you  in order to make dimension which  which Stephane sees uh less important , uh uh I mean not  not useful , less important , what you do is that this particular component in the model you can multiply by w you can  you can basically de - weight it in the model. But you can't do it in a  in a test data because you don't have a model for th I mean uh when the test comes , but what you can do is that you put this particular component in  and  and you compress it. That becomes uh th gets less variance , subsequently becomes less important.

Couldn't you just do that to the test data and not do anything with your training data ?


So in order  if you  in order to make dimension which  which Stephane sees uh less important , uh uh I mean not  not useful , less important , what you do is that this particular component in the model you can multiply by w you can  you can basically de - weight it in the model. But you can't do it in a  in a test data because you don't have a model for th I mean uh when the test comes , but what you can do is that you put this particular component in  and  and you compress it. That becomes uh th gets less variance , subsequently becomes less important.

Couldn't you just do that to the test data and not do anything with your training data ?

That would be very bad , because uh your t your model was trained uh expecting uh , that wouldn't work. Because your model was trained expecting a certain var variance on C - one.


Couldn't you just do that to the test data and not do anything with your training data ?

That would be very bad , because uh your t your model was trained uh expecting uh , that wouldn't work. Because your model was trained expecting a certain var variance on C - one.

And because the model thinks C - one is important. After you train the model , you sort of  y you could do  you could do still what I was proposing initially , that during the training you  you compress C - one that becomes  then it becomes less important in a training.


That would be very bad , because uh your t your model was trained uh expecting uh , that wouldn't work. Because your model was trained expecting a certain var variance on C - one.

And because the model thinks C - one is important. After you train the model , you sort of  y you could do  you could do still what I was proposing initially , that during the training you  you compress C - one that becomes  then it becomes less important in a training.


And because the model thinks C - one is important. After you train the model , you sort of  y you could do  you could do still what I was proposing initially , that during the training you  you compress C - one that becomes  then it becomes less important in a training.

But if you have  if you want to run e ex extensive experiment without retraining the model , you don't have to retrain the model. You train it on the original vector. But after , you  wh when you are doing this parametric study of importance of C - one you will de - weight the C - one component in the model , and you will put in the  you will compress the  this component in a  in the test data. s by the same amount.


And because the model thinks C - one is important. After you train the model , you sort of  y you could do  you could do still what I was proposing initially , that during the training you  you compress C - one that becomes  then it becomes less important in a training.

But if you have  if you want to run e ex extensive experiment without retraining the model , you don't have to retrain the model. You train it on the original vector. But after , you  wh when you are doing this parametric study of importance of C - one you will de - weight the C - one component in the model , and you will put in the  you will compress the  this component in a  in the test data. s by the same amount.

Could you also if you wanted to  if you wanted to try an experiment uh by  leaving out say , C - one , couldn't you , in your test data , uh modify the  all of the C - one values to be um way outside of the normal range of the Gaussian for C - one that was trained in the model ? So that effectively , the C - one never really contributes to the score ?


But if you have  if you want to run e ex extensive experiment without retraining the model , you don't have to retrain the model. You train it on the original vector. But after , you  wh when you are doing this parametric study of importance of C - one you will de - weight the C - one component in the model , and you will put in the  you will compress the  this component in a  in the test data. s by the same amount.

Could you also if you wanted to  if you wanted to try an experiment uh by  leaving out say , C - one , couldn't you , in your test data , uh modify the  all of the C - one values to be um way outside of the normal range of the Gaussian for C - one that was trained in the model ? So that effectively , the C - one never really contributes to the score ?


Could you also if you wanted to  if you wanted to try an experiment uh by  leaving out say , C - one , couldn't you , in your test data , uh modify the  all of the C - one values to be um way outside of the normal range of the Gaussian for C - one that was trained in the model ? So that effectively , the C - one never really contributes to the score ?


right ? what you are proposing ? N no you don't want that.


right ? what you are proposing ? N no you don't want that.

Because that would  then your model would be unlikely. Your likelihood would be low , right ? Because you would be providing severe mismatch.


right ? what you are proposing ? N no you don't want that.

Because that would  then your model would be unlikely. Your likelihood would be low , right ? Because you would be providing severe mismatch.

Mm - hmm. But what if you set if to the mean of the model , then ? And it was a cons you set all C - ones coming in through your test data , you  you change whatever value that was there to the mean that your model had.


Because that would  then your model would be unlikely. Your likelihood would be low , right ? Because you would be providing severe mismatch.

Mm - hmm. But what if you set if to the mean of the model , then ? And it was a cons you set all C - ones coming in through your test data , you  you change whatever value that was there to the mean that your model had.

No that would be very good match , right ?


Mm - hmm. But what if you set if to the mean of the model , then ? And it was a cons you set all C - ones coming in through your test data , you  you change whatever value that was there to the mean that your model had.

No that would be very good match , right ?


No that would be very good match , right ?

Which  Well , yeah , but we have several means. So.


Which  Well , yeah , but we have several means. So.


but uh ,  no , no I don't think that it would be the same.  I mean , no , the  If you set it to a mean , that would  No , you can't do that. Y you ca you ca Ch - Chuck , you can't do that.


but uh ,  no , no I don't think that it would be the same.  I mean , no , the  If you set it to a mean , that would  No , you can't do that. Y you ca you ca Ch - Chuck , you can't do that.

Oh , that 's true , right , yeah , because you  you have


but uh ,  no , no I don't think that it would be the same.  I mean , no , the  If you set it to a mean , that would  No , you can't do that. Y you ca you ca Ch - Chuck , you can't do that.

Oh , that 's true , right , yeah , because you  you have

Because that would be a really f fiddling with the data ,


Oh , that 's true , right , yeah , because you  you have

Because that would be a really f fiddling with the data ,


Because that would be a really f fiddling with the data ,


But what you can do , I 'm confident you ca


But what you can do , I 'm confident you ca

well , I 'm reasonably confident and I putting it on the record , right ? I mean y people will listen to it for  for centuries now , is  what you can do , is you train the model uh with the  with the original data.


But what you can do , I 'm confident you ca

well , I 'm reasonably confident and I putting it on the record , right ? I mean y people will listen to it for  for centuries now , is  what you can do , is you train the model uh with the  with the original data.

Then you decide that you want to see how important C  C - one is. So what you will do is that a component in the model for C - one , you will divide it by  by two. And you will compress your test data by square root.


well , I 'm reasonably confident and I putting it on the record , right ? I mean y people will listen to it for  for centuries now , is  what you can do , is you train the model uh with the  with the original data.

Then you decide that you want to see how important C  C - one is. So what you will do is that a component in the model for C - one , you will divide it by  by two. And you will compress your test data by square root.


Then you decide that you want to see how important C  C - one is. So what you will do is that a component in the model for C - one , you will divide it by  by two. And you will compress your test data by square root.

Then you will still have a perfect m match. Except that this component of C - one will be half as important in a  in a overall score.


Then you will still have a perfect m match. Except that this component of C - one will be half as important in a  in a overall score.

Then you divide it by four and you take a square , f fourth root. Then if you think that some component is more  is more important then th th th it then  then uh uh i it is , based on training , then you uh multiply this particular component in the model by  by  by


Then you will still have a perfect m match. Except that this component of C - one will be half as important in a  in a overall score.

Then you divide it by four and you take a square , f fourth root. Then if you think that some component is more  is more important then th th th it then  then uh uh i it is , based on training , then you uh multiply this particular component in the model by  by  by

You 're talking about the standard deviation ?


Then you divide it by four and you take a square , f fourth root. Then if you think that some component is more  is more important then th th th it then  then uh uh i it is , based on training , then you uh multiply this particular component in the model by  by  by

You 're talking about the standard deviation ?


You 're talking about the standard deviation ?

Yeah , multiply this component uh i it by number b larger than one ,


Yeah , multiply this component uh i it by number b larger than one ,


Yeah , multiply this component uh i it by number b larger than one ,

and you put your data in power higher than one. Then it becomes more important. In the overall score , I believe.


and you put your data in power higher than one. Then it becomes more important. In the overall score , I believe.

But  don't you have to do something to the mean , also ?


But  don't you have to do something to the mean , also ?


But I think it 's  uh the  The variance is on  on the denominator in the  in the Gaussian equation. So. I think it 's maybe it 's the contrary. If you want to decrease the importance of a c parameter , you have to increase it 's variance.


But I think it 's  uh the  The variance is on  on the denominator in the  in the Gaussian equation. So. I think it 's maybe it 's the contrary. If you want to decrease the importance of a c parameter , you have to increase it 's variance.

Exactly. Yeah. So you  so you may want to do it other way around ,


Exactly. Yeah. So you  so you may want to do it other way around ,


But if your  If your um original data for C - one had a mean of two.


But if your  If your um original data for C - one had a mean of two.

And now you 're  you 're  you 're changing that by squaring it. Now your mean of your C - one original data has   is four. But your model still has a mean of two. So even though you 've expended the range , your mean doesn't match anymore.


And now you 're  you 're  you 're changing that by squaring it. Now your mean of your C - one original data has   is four. But your model still has a mean of two. So even though you 've expended the range , your mean doesn't match anymore.


I think  What I see  What could be done is you don't change your features , which are computed once for all ,


I think  What I see  What could be done is you don't change your features , which are computed once for all ,

but you just tune the model. So. You have your features. You train your  your model on these features.


but you just tune the model. So. You have your features. You train your  your model on these features.

And then if you want to decrease the importance of C - one you just take the variance of the C - one component in the  in the model and increase it if you want to decrease the importance of C - one or decrease it


And then if you want to decrease the importance of C - one you just take the variance of the C - one component in the  in the model and increase it if you want to decrease the importance of C - one or decrease it


Yeah. You would have to modify the mean in the model. I  you  I agree with you. Yeah. Yeah , but I mean , but it 's  it 's i it 's do - able ,


Yeah. You would have to modify the mean in the model. I  you  I agree with you. Yeah. Yeah , but I mean , but it 's  it 's i it 's do - able ,

right ? I mean , it 's predictable. Uh. Yeah.


right ? I mean , it 's predictable. Uh. Yeah.


Yeah. But as a simple thing , you could just  just muck with the variance.


Yeah. But as a simple thing , you could just  just muck with the variance.

to get uh this  uh this  the effect I think that you 're talking about ,


to get uh this  uh this  the effect I think that you 're talking about ,


Could increase the variance to decrease the importance.


Could increase the variance to decrease the importance.

Yeah , because if you had a huge variance , you 're dividing by a large number ,  you get a very small contribution.


Yeah , because if you had a huge variance , you 're dividing by a large number ,  you get a very small contribution.


Yeah , the sharper the variance , the more  more important to get that one right.


Yeah , the sharper the variance , the more  more important to get that one right.

Yeah , you know actually , this reminds me of something that happened uh when I was at BBN. We were playing with putting um pitch into the Mandarin recognizer.


Yeah , you know actually , this reminds me of something that happened uh when I was at BBN. We were playing with putting um pitch into the Mandarin recognizer.


Yeah , you know actually , this reminds me of something that happened uh when I was at BBN. We were playing with putting um pitch into the Mandarin recognizer.

And this particular pitch algorithm um when it didn't think there was any voicing , was spitting out zeros. So we were getting  uh when we did clustering , we were getting groups uh of features


Yeah , you know actually , this reminds me of something that happened uh when I was at BBN. We were playing with putting um pitch into the Mandarin recognizer.

And this particular pitch algorithm um when it didn't think there was any voicing , was spitting out zeros. So we were getting  uh when we did clustering , we were getting groups uh of features

p Pretty new outliers , interesting outliers , right ?


And this particular pitch algorithm um when it didn't think there was any voicing , was spitting out zeros. So we were getting  uh when we did clustering , we were getting groups uh of features

p Pretty new outliers , interesting outliers , right ?

yeah , with  with a mean of zero and basically zero variance.


p Pretty new outliers , interesting outliers , right ?

yeah , with  with a mean of zero and basically zero variance.

So , when ener  when anytime any one of those vectors came in that had a zero in it , we got a great score. I mean it was just ,  you know , incredibly  high score , and so that was throwing everything off.


yeah , with  with a mean of zero and basically zero variance.

So , when ener  when anytime any one of those vectors came in that had a zero in it , we got a great score. I mean it was just ,  you know , incredibly  high score , and so that was throwing everything off.


So , when ener  when anytime any one of those vectors came in that had a zero in it , we got a great score. I mean it was just ,  you know , incredibly  high score , and so that was throwing everything off.

So  if you have very small variance you get really good scores when you get something that matches.


So  if you have very small variance you get really good scores when you get something that matches.


So  if you have very small variance you get really good scores when you get something that matches.

So.  So that 's a way , yeah , yeah  That 's a way to increase the  yeah , n That 's interesting. So in fact , that would be  That doesn't require any retraining.


So.  So that 's a way , yeah , yeah  That 's a way to increase the  yeah , n That 's interesting. So in fact , that would be  That doesn't require any retraining.


just tuning the models and testing , actually.


You  you have a step where you you modify the models , make a d copy of your models with whatever variance modifications you make , and rerun recognition.


That could be set up fairly easily I think , and you have a whole bunch of you know


That could be set up fairly easily I think , and you have a whole bunch of you know

That 's an interesting idea , actually. For testing the  Yeah. Huh !


That could be set up fairly easily I think , and you have a whole bunch of you know

That 's an interesting idea , actually. For testing the  Yeah. Huh !

Didn't you say you got these uh HTK 's set up on the new Linux boxes ?


That 's an interesting idea , actually. For testing the  Yeah. Huh !

Didn't you say you got these uh HTK 's set up on the new Linux boxes ?


Didn't you say you got these uh HTK 's set up on the new Linux boxes ?


In fact , and  and they 're just t right now they 're installing uh  increasing the memory on that uh  the Linux box.


In fact , and  and they 're just t right now they 're installing uh  increasing the memory on that uh  the Linux box.

And Chuck is sort of really fishing for how to keep his computer busy ,


And Chuck is sort of really fishing for how to keep his computer busy ,


Absinthe. We 've got five processors on that.


because then y you just write the " do " - loops and then you pretend that you are working while you are sort of  you c you can go fishing.


Yeah. Then you are sort of in this mode like all of those ARPA people are , right ?


Yeah. Then you are sort of in this mode like all of those ARPA people are , right ?

Uh , since it is on the record , I can't say uh which company it was , but it was reported to me that uh somebody visited a company and during a  d during a discussion , there was this guy who was always hitting the carriage returns uh on a computer.


Uh , since it is on the record , I can't say uh which company it was , but it was reported to me that uh somebody visited a company and during a  d during a discussion , there was this guy who was always hitting the carriage returns uh on a computer.

So after two hours uh the visitor said " wh why are you hitting this carriage return ? " And he said " well you know , we are being paid by a computer ty I mean we are  we have a government contract. And they pay us by  by amount of computer time we use. " It was in old days when there were uh  of PDP - eights and that sort of thing.


Uh , since it is on the record , I can't say uh which company it was , but it was reported to me that uh somebody visited a company and during a  d during a discussion , there was this guy who was always hitting the carriage returns uh on a computer.

So after two hours uh the visitor said " wh why are you hitting this carriage return ? " And he said " well you know , we are being paid by a computer ty I mean we are  we have a government contract. And they pay us by  by amount of computer time we use. " It was in old days when there were uh  of PDP - eights and that sort of thing.

Oh , my gosh ! So he had to make it look like


So after two hours uh the visitor said " wh why are you hitting this carriage return ? " And he said " well you know , we are being paid by a computer ty I mean we are  we have a government contract. And they pay us by  by amount of computer time we use. " It was in old days when there were uh  of PDP - eights and that sort of thing.

Oh , my gosh ! So he had to make it look like

Because so they had a  they literally had to c monitor at the time  at the time on a computer how much time is being spent I  i i or on  on this particular project.


Oh , my gosh ! So he had to make it look like

Because so they had a  they literally had to c monitor at the time  at the time on a computer how much time is being spent I  i i or on  on this particular project.


Because so they had a  they literally had to c monitor at the time  at the time on a computer how much time is being spent I  i i or on  on this particular project.


Nobody was looking even at what was coming out.


Nobody was looking even at what was coming out.

Have you ever seen those little um  It 's  it 's this thing that 's the shape of a bird and it has a red ball and its beak dips into the water ?


Nobody was looking even at what was coming out.

Have you ever seen those little um  It 's  it 's this thing that 's the shape of a bird and it has a red ball and its beak dips into the water ?

So  if you could hook that up so it hit the keyboard


Have you ever seen those little um  It 's  it 's this thing that 's the shape of a bird and it has a red ball and its beak dips into the water ?

So  if you could hook that up so it hit the keyboard


So  if you could hook that up so it hit the keyboard


So  if you could hook that up so it hit the keyboard

It would be similar  similar to  I knew some people who were uh that was in old Communist uh Czechoslovakia , right ? so we were watching for American airplanes , coming to spy on  on uh  on us at the time ,


It would be similar  similar to  I knew some people who were uh that was in old Communist uh Czechoslovakia , right ? so we were watching for American airplanes , coming to spy on  on uh  on us at the time ,


It would be similar  similar to  I knew some people who were uh that was in old Communist uh Czechoslovakia , right ? so we were watching for American airplanes , coming to spy on  on uh  on us at the time ,

so there were three guys uh uh stationed in the middle of the woods on one l lonely uh watching tower , pretty much spending a year and a half there because there was this service right ? And so they  very quickly they made friends with local girls and local people in the village


so there were three guys uh uh stationed in the middle of the woods on one l lonely uh watching tower , pretty much spending a year and a half there because there was this service right ? And so they  very quickly they made friends with local girls and local people in the village


and so but they  there was one plane flying over s always uh uh above , and so that was the only work which they had. They  like four in the afternoon they had to report there was a plane from Prague to Brno Basically f flying there ,


and so but they  there was one plane flying over s always uh uh above , and so that was the only work which they had. They  like four in the afternoon they had to report there was a plane from Prague to Brno Basically f flying there ,

so they f very q f first thing was that they would always run back and  and at four o ' clock and  and quickly make a call , " this plane is uh uh passing " then a second thing was that they  they took the line from this u u post to uh uh a local pub. And they were calling from the pub. And they  but third thing which they made , and when they screwed up , they  finally they had to p the  the p the pub owner to make these phone calls because they didn't even bother to be there anymore. And one day there was  there was no plane. At least they were sort of smart enough that they looked if the plane is flying there , right ? And the pub owner says " oh my  four o ' clock , OK , quickly p pick up the phone , call that there 's a plane flying. "


so they f very q f first thing was that they would always run back and  and at four o ' clock and  and quickly make a call , " this plane is uh uh passing " then a second thing was that they  they took the line from this u u post to uh uh a local pub. And they were calling from the pub. And they  but third thing which they made , and when they screwed up , they  finally they had to p the  the p the pub owner to make these phone calls because they didn't even bother to be there anymore. And one day there was  there was no plane. At least they were sort of smart enough that they looked if the plane is flying there , right ? And the pub owner says " oh my  four o ' clock , OK , quickly p pick up the phone , call that there 's a plane flying. "


it was downed , or   and  so they got in trouble. But.  But uh.


Well , at least go test the s test the uh assumption about C - C - one I mean to begin with. But then of course one can then think about some predictable result to change all of them.


Well , at least go test the s test the uh assumption about C - C - one I mean to begin with. But then of course one can then think about some predictable result to change all of them.

It 's just like we used to do these uh  these uh  um the  the uh distance measures. It might be that uh


Well , at least go test the s test the uh assumption about C - C - one I mean to begin with. But then of course one can then think about some predictable result to change all of them.

It 's just like we used to do these uh  these uh  um the  the uh distance measures. It might be that uh

Yeah , so the first set of uh variance weighting vectors would be just you know one  modifying one and leaving the others the same.


It 's just like we used to do these uh  these uh  um the  the uh distance measures. It might be that uh

Yeah , so the first set of uh variance weighting vectors would be just you know one  modifying one and leaving the others the same.


Yeah , so the first set of uh variance weighting vectors would be just you know one  modifying one and leaving the others the same.


Because you see , I mean , what is happening here in a  in a  in a  in such a model is that it 's  tells you yeah what has a low variance uh is uh  is uh  is more reliable ,


Because you see , I mean , what is happening here in a  in a  in a  in such a model is that it 's  tells you yeah what has a low variance uh is uh  is uh  is more reliable ,

Wh - yeah , when the data matches that , then you get really


Wh - yeah , when the data matches that , then you get really


Wh - yeah , when the data matches that , then you get really

How do we know , especially when it comes to noise ?


How do we know , especially when it comes to noise ?

But there could just naturally be low variance.


How do we know , especially when it comes to noise ?

But there could just naturally be low variance.

Because I  Like , I 've noticed in the higher cepstral coefficients , the numbers seem to get smaller , right ? So d


But there could just naturally be low variance.

Because I  Like , I 've noticed in the higher cepstral coefficients , the numbers seem to get smaller , right ? So d


Because I  Like , I 've noticed in the higher cepstral coefficients , the numbers seem to get smaller , right ? So d


Yeah. Exactly. And so it seems like they 're already sort of compressed.


Yeah. Exactly. And so it seems like they 're already sort of compressed.

Yeah that 's why uh people used these lifters were inverse variance weighting lifters basically that makes uh uh Euclidean distance more like uh Mahalanobis distance with a diagonal covariance when you knew what all the variances were over the old data.


Yeah that 's why uh people used these lifters were inverse variance weighting lifters basically that makes uh uh Euclidean distance more like uh Mahalanobis distance with a diagonal covariance when you knew what all the variances were over the old data.


Yeah that 's why uh people used these lifters were inverse variance weighting lifters basically that makes uh uh Euclidean distance more like uh Mahalanobis distance with a diagonal covariance when you knew what all the variances were over the old data.

What they would do is that they would weight each coefficient by inverse of the variance. Turns out that uh the variance decreases at least at fast , I believe , as the index of the cepstral coefficients. I think you can show that uh uh analytically.


What they would do is that they would weight each coefficient by inverse of the variance. Turns out that uh the variance decreases at least at fast , I believe , as the index of the cepstral coefficients. I think you can show that uh uh analytically.

So typically what happens is that you  you need to weight the  uh weight the higher coefficients more than uh the lower coefficients.


So typically what happens is that you  you need to weight the  uh weight the higher coefficients more than uh the lower coefficients.


When  Yeah. When we talked about Aurora still I wanted to m make a plea  uh encourage for uh more communication between  between uh  uh different uh parts of the distributed uh  uh center. Uh even when there is absolutely nothing to  to s to say but the weather is good in Ore - in  in Berkeley. I 'm sure that it 's being appreciated in Oregon and maybe it will generate similar responses down here , like , uh


What  you know , nowadays , yeah. It 's actually do - able , almost.


What  you know , nowadays , yeah. It 's actually do - able , almost.

Is the um  if we mail to " Aurora - inhouse " , does that go up to you guys also ?


Is the um  if we mail to " Aurora - inhouse " , does that go up to you guys also ?


Yeah we sh Do we have a mailing list that includes uh the OGI people ?


Oh ! Maybe we should set that up. That would make it much easier.


Oh ! Maybe we should set that up. That would make it much easier.

Yeah. Yeah. Yeah , that would make it easier.


Oh ! Maybe we should set that up. That would make it much easier.

Yeah. Yeah. Yeah , that would make it easier.

So maybe just call it " Aurora " or something that would


Oh ! Maybe we should set that up. That would make it much easier.

Yeah. Yeah. Yeah , that would make it easier.

So maybe just call it " Aurora " or something that would

Yeah. Yeah. And then we also can send the  the dis to the same address right , and it goes to everybody


Yeah. Yeah. Yeah , that would make it easier.

So maybe just call it " Aurora " or something that would

Yeah. Yeah. And then we also can send the  the dis to the same address right , and it goes to everybody


So maybe just call it " Aurora " or something that would

Yeah. Yeah. And then we also can send the  the dis to the same address right , and it goes to everybody


Yeah. Yeah. And then we also can send the  the dis to the same address right , and it goes to everybody


Because what 's happening naturally in research , I know , is that people essentially start working on something and they don't want to be much bothered , right ? but what the  the  then the danger is in a group like this , is that two people are working on the same thing and i c of course both of them come with the s very good solution , but it could have been done somehow in half of the effort or something.


Because what 's happening naturally in research , I know , is that people essentially start working on something and they don't want to be much bothered , right ? but what the  the  then the danger is in a group like this , is that two people are working on the same thing and i c of course both of them come with the s very good solution , but it could have been done somehow in half of the effort or something.

Oh , there 's another thing which I wanted to uh uh report. Lucash , I think , uh wrote the software for this Aurora - two system. reasonably uh good one , because he 's doing it for Intel , but I trust that we have uh rights to uh use it uh or distribute it and everything. Cuz Intel 's intentions originally was to distribute it free of charge anyways.


Oh , there 's another thing which I wanted to uh uh report. Lucash , I think , uh wrote the software for this Aurora - two system. reasonably uh good one , because he 's doing it for Intel , but I trust that we have uh rights to uh use it uh or distribute it and everything. Cuz Intel 's intentions originally was to distribute it free of charge anyways.

u s And so  so uh we  we will make sure that at least you can see the software and if  if  if  if it is of any use. Just uh


u s And so  so uh we  we will make sure that at least you can see the software and if  if  if  if it is of any use. Just uh

It might be a reasonable point for p perhaps uh start converging.


It might be a reasonable point for p perhaps uh start converging.

Because Morgan 's point is that  He is an experienced guy. He says " well you know it 's very difficult to collaborate if you are working with supposedly the same thing , in quotes , except which is not s is not the same.


Because Morgan 's point is that  He is an experienced guy. He says " well you know it 's very difficult to collaborate if you are working with supposedly the same thing , in quotes , except which is not s is not the same.

Which  which uh uh one is using that set of hurdles , another one set  is using another set of hurdles. So. And  And then it 's difficult to c compare.


Because Morgan 's point is that  He is an experienced guy. He says " well you know it 's very difficult to collaborate if you are working with supposedly the same thing , in quotes , except which is not s is not the same.

Which  which uh uh one is using that set of hurdles , another one set  is using another set of hurdles. So. And  And then it 's difficult to c compare.

What about Harry ? Uh. We received a mail last week and you are starting to  to do some experiments.


Which  which uh uh one is using that set of hurdles , another one set  is using another set of hurdles. So. And  And then it 's difficult to c compare.

What about Harry ? Uh. We received a mail last week and you are starting to  to do some experiments.

He got the  he got the software. Yeah. They sent the release.


What about Harry ? Uh. We received a mail last week and you are starting to  to do some experiments.

He got the  he got the software. Yeah. They sent the release.


He got the  he got the software. Yeah. They sent the release.


Yeah because Intel paid us uh should I say on a microphone ? uh some amount of money , not much. Not much I can say on a microphone. Much less then we should have gotten  for this amount of work. And they wanted uh to  to have software so that they can also play with it , which means that it has to be in a certain environment


Yeah because Intel paid us uh should I say on a microphone ? uh some amount of money , not much. Not much I can say on a microphone. Much less then we should have gotten  for this amount of work. And they wanted uh to  to have software so that they can also play with it , which means that it has to be in a certain environment

they use actu actually some Intel libraries , but in the process , Lucash just rewrote the whole thing because he figured rather than trying to f make sense uh of uh  including ICSI software uh not for training on the nets


they use actu actually some Intel libraries , but in the process , Lucash just rewrote the whole thing because he figured rather than trying to f make sense uh of uh  including ICSI software uh not for training on the nets


they use actu actually some Intel libraries , but in the process , Lucash just rewrote the whole thing because he figured rather than trying to f make sense uh of uh  including ICSI software uh not for training on the nets

but I think he rewrote the  the  the  or so maybe somehow reused over the parts of the thing so that  so that  the whole thing , including MLP , trained MLP is one piece of uh software.


but I think he rewrote the  the  the  or so maybe somehow reused over the parts of the thing so that  so that  the whole thing , including MLP , trained MLP is one piece of uh software.


I mean , I remember when we were trying to put together all the ICSI software for the submission.


I mean , I remember when we were trying to put together all the ICSI software for the submission.

Or  That 's what he was saying , right. He said that it was like  it was like just so many libraries and nobody knew what was used when , and  and so that 's where he started and that 's where he realized that it needs to be  needs to be uh uh at least cleaned up ,


Or  That 's what he was saying , right. He said that it was like  it was like just so many libraries and nobody knew what was used when , and  and so that 's where he started and that 's where he realized that it needs to be  needs to be uh uh at least cleaned up ,


Yeah. Well , the  the only thing I would check is if he  does he use Intel math libraries ,


Yeah. Well , the  the only thing I would check is if he  does he use Intel math libraries ,

because if it 's the case , it 's maybe not so easy to use it on another architecture.


Yeah. Well , the  the only thing I would check is if he  does he use Intel math libraries ,

because if it 's the case , it 's maybe not so easy to use it on another architecture.

n not maybe  Maybe not in a first  maybe not in a first ap approximation because I think he started first just with a plain C  C or C - plus - plus or something before


because if it 's the case , it 's maybe not so easy to use it on another architecture.

n not maybe  Maybe not in a first  maybe not in a first ap approximation because I think he started first just with a plain C  C or C - plus - plus or something before


n not maybe  Maybe not in a first  maybe not in a first ap approximation because I think he started first just with a plain C  C or C - plus - plus or something before


And uh in  otherwise the Intel libraries , I think they are available free of f freely. But they may be running only on  on uh  on uh Windows.


Yeah , on Intel architecture , may not run in SUN.


That is p that is  that is possible. That 's why Intel of course is distributing it ,


That is p that is  that is possible. That 's why Intel of course is distributing it ,

Yeah. Well there are  at least there are optimized version for their architecture.


Yeah. Well there are  at least there are optimized version for their architecture.


Yeah. Well there are  at least there are optimized version for their architecture.

I don't know. I never checked carefully these sorts of


Yeah. Well there are  at least there are optimized version for their architecture.

I don't know. I never checked carefully these sorts of

I know there was some issues that initially of course we d do all the development on Linux but we use  we don't have  we have only three uh uh uh uh s SUNs and we have them only because they have a SPERT board in. Otherwise  otherwise we t almost exclusively are working with uh PC 's now , with Intel. In that way Intel succeeded with us , because they gave us too many good machines for very little money or nothing.


I don't know. I never checked carefully these sorts of

I know there was some issues that initially of course we d do all the development on Linux but we use  we don't have  we have only three uh uh uh uh s SUNs and we have them only because they have a SPERT board in. Otherwise  otherwise we t almost exclusively are working with uh PC 's now , with Intel. In that way Intel succeeded with us , because they gave us too many good machines for very little money or nothing.


I know there was some issues that initially of course we d do all the development on Linux but we use  we don't have  we have only three uh uh uh uh s SUNs and we have them only because they have a SPERT board in. Otherwise  otherwise we t almost exclusively are working with uh PC 's now , with Intel. In that way Intel succeeded with us , because they gave us too many good machines for very little money or nothing.


Hmm. Does anybody have anything else ? to  Shall we read some digits ?


Hmm. Does anybody have anything else ? to  Shall we read some digits ?

So. Hynek , I don't know if you 've ever done this.


So. Hynek , I don't know if you 've ever done this.


So. Hynek , I don't know if you 've ever done this.

The way that it works is each person goes around in turn ,  and uh you say the transcript number and then you read the digits , the  the strings of numbers as individual digits.