Uh , but  but , you know , that 'll  uh , it 's clear that we , uh  we are not  with the real case that we 're looking at , we can't just look at reverberation in isolation because the interaction between that and noise is  is considerable. And that 's I mean , in the past we 've looked at , uh , and this is hard enough , the interaction between channel effects and  and , uh  and additive noise , uh , so convolutional effects and  and additive effects. And that 's hard enough. I mean , I don't think we really  I mean , we 're trying to deal with that. In a sense that 's what we 're trying to deal with in this Aurora task. And we have , uh , the , uh , uh , LDA stuff that in principle is doing something about convolutional effects. And we have the noise suppression that 's doing something about noise. Uh , even that 's hard enough. And  and the on - line normalization as well , in that s category. i i There 's all these interactions between these two and that 's part of why these guys had to work so hard on  on juggling everything around. But now when you throw in the reverberation , it 's even worse , because not only do you have these effects , but you also have some long time effects. And , um , so Dave has something which , uh , is doing some nice things under some conditions with  with long time effects but when it 's  when there 's noise there too , it 's  it 's  it 's pretty hard. So we have to start  Since any  almost any real situation is gonna have  uh , where you have the microphone distant , is going to have both things , we  we actually have to think about both at the same time.


Uh , but  but , you know , that 'll  uh , it 's clear that we , uh  we are not  with the real case that we 're looking at , we can't just look at reverberation in isolation because the interaction between that and noise is  is considerable. And that 's I mean , in the past we 've looked at , uh , and this is hard enough , the interaction between channel effects and  and , uh  and additive noise , uh , so convolutional effects and  and additive effects. And that 's hard enough. I mean , I don't think we really  I mean , we 're trying to deal with that. In a sense that 's what we 're trying to deal with in this Aurora task. And we have , uh , the , uh , uh , LDA stuff that in principle is doing something about convolutional effects. And we have the noise suppression that 's doing something about noise. Uh , even that 's hard enough. And  and the on - line normalization as well , in that s category. i i There 's all these interactions between these two and that 's part of why these guys had to work so hard on  on juggling everything around. But now when you throw in the reverberation , it 's even worse , because not only do you have these effects , but you also have some long time effects. And , um , so Dave has something which , uh , is doing some nice things under some conditions with  with long time effects but when it 's  when there 's noise there too , it 's  it 's  it 's pretty hard. So we have to start  Since any  almost any real situation is gonna have  uh , where you have the microphone distant , is going to have both things , we  we actually have to think about both at the same time.

So , um  So there 's this noise suppression thing , which is sort of worked out and then , uh , maybe you should just continue telling what  what else is in the  the form we have.


Uh , but  but , you know , that 'll  uh , it 's clear that we , uh  we are not  with the real case that we 're looking at , we can't just look at reverberation in isolation because the interaction between that and noise is  is considerable. And that 's I mean , in the past we 've looked at , uh , and this is hard enough , the interaction between channel effects and  and , uh  and additive noise , uh , so convolutional effects and  and additive effects. And that 's hard enough. I mean , I don't think we really  I mean , we 're trying to deal with that. In a sense that 's what we 're trying to deal with in this Aurora task. And we have , uh , the , uh , uh , LDA stuff that in principle is doing something about convolutional effects. And we have the noise suppression that 's doing something about noise. Uh , even that 's hard enough. And  and the on - line normalization as well , in that s category. i i There 's all these interactions between these two and that 's part of why these guys had to work so hard on  on juggling everything around. But now when you throw in the reverberation , it 's even worse , because not only do you have these effects , but you also have some long time effects. And , um , so Dave has something which , uh , is doing some nice things under some conditions with  with long time effects but when it 's  when there 's noise there too , it 's  it 's  it 's pretty hard. So we have to start  Since any  almost any real situation is gonna have  uh , where you have the microphone distant , is going to have both things , we  we actually have to think about both at the same time.

So , um  So there 's this noise suppression thing , which is sort of worked out and then , uh , maybe you should just continue telling what  what else is in the  the form we have.

Yeah , well ,  the , um , the other parts of the system are the  the blocks that were already present before and that we did not modify a lot.


So , um  So there 's this noise suppression thing , which is sort of worked out and then , uh , maybe you should just continue telling what  what else is in the  the form we have.

Yeah , well ,  the , um , the other parts of the system are the  the blocks that were already present before and that we did not modify a lot.

So that 's  again , that  that 's the Wiener filtering , followed by , uh  uh , that 's done at the FFT level. Then


Yeah , well ,  the , um , the other parts of the system are the  the blocks that were already present before and that we did not modify a lot.

So that 's  again , that  that 's the Wiener filtering , followed by , uh  uh , that 's done at the FFT level. Then


So that 's  again , that  that 's the Wiener filtering , followed by , uh  uh , that 's done at the FFT level. Then


The  the  the filtering is done in the frequency domain ?


The  the  the filtering is done in the frequency domain ?

Yeah , OK. And then the mel and then the log , and then the


Yeah , OK. And then the mel and then the log , and then the


followed by  upsampling. Then finally , we compute delta and we put the neural network also.


followed by  upsampling. Then finally , we compute delta and we put the neural network also.

Right , and then in parallel with  an  a neural net. And then following neural net , some  probably some orthogonalization.


Right , and then in parallel with  an  a neural net. And then following neural net , some  probably some orthogonalization.

And finally frame dropping , which um ,  would be a neural network also , used for estimated silence probabilities. And the input of this neural network would be somewhere between log  mel bands or one of the earlier stages of the processing.


And finally frame dropping , which um ,  would be a neural network also , used for estimated silence probabilities. And the input of this neural network would be somewhere between log  mel bands or one of the earlier stages of the processing.

Mm - hmm. So that 's sort of  most of this stuff is  yeah , is operating parallel with this other stuff.


And finally frame dropping , which um ,  would be a neural network also , used for estimated silence probabilities. And the input of this neural network would be somewhere between log  mel bands or one of the earlier stages of the processing.

Mm - hmm. So that 's sort of  most of this stuff is  yeah , is operating parallel with this other stuff.

Yeah. So the things that we , um , uh , I guess we sort of  uh , There 's  there 's some , uh , neat ideas for  V A So , I mean , in  I think there 's sort of like  There 's a bunch of tuning things to improve stuff. There 's questions about  various places where there 's an exponent , if it 's the right exponent , or  ways that we 're estimating noise , that we can improve estimating noise. And there 's gonna be a host of those. But structurally it seemed like the things  the main things that  that we brought up that , uh , are  are gonna need to get worked on seriously are , uh , uh , a   a significantly better VAD , uh , putting the neural net on , um , which , you know , we haven't been doing anything with , the , uh , neural net at the end there , and , uh , the , uh ,  opening up the second front. Uh.


Mm - hmm. So that 's sort of  most of this stuff is  yeah , is operating parallel with this other stuff.

Yeah. So the things that we , um , uh , I guess we sort of  uh , There 's  there 's some , uh , neat ideas for  V A So , I mean , in  I think there 's sort of like  There 's a bunch of tuning things to improve stuff. There 's questions about  various places where there 's an exponent , if it 's the right exponent , or  ways that we 're estimating noise , that we can improve estimating noise. And there 's gonna be a host of those. But structurally it seemed like the things  the main things that  that we brought up that , uh , are  are gonna need to get worked on seriously are , uh , uh , a   a significantly better VAD , uh , putting the neural net on , um , which , you know , we haven't been doing anything with , the , uh , neural net at the end there , and , uh , the , uh ,  opening up the second front. Uh.


Yeah. So the things that we , um , uh , I guess we sort of  uh , There 's  there 's some , uh , neat ideas for  V A So , I mean , in  I think there 's sort of like  There 's a bunch of tuning things to improve stuff. There 's questions about  various places where there 's an exponent , if it 's the right exponent , or  ways that we 're estimating noise , that we can improve estimating noise. And there 's gonna be a host of those. But structurally it seemed like the things  the main things that  that we brought up that , uh , are  are gonna need to get worked on seriously are , uh , uh , a   a significantly better VAD , uh , putting the neural net on , um , which , you know , we haven't been doing anything with , the , uh , neural net at the end there , and , uh , the , uh ,  opening up the second front. Uh.

Yeah , yeah , I mean , cuz we  we have  we have , uh , uh , half the  the , uh , data rate that they allow.


Yeah , yeah , I mean , cuz we  we have  we have , uh , uh , half the  the , uh , data rate that they allow.

And , uh , so the initial thing which came from , uh , the meeting that we had down south was , uh , that , um , we 'll initially just put in a mel spectrum as the second one. It 's , you know ,  cheap , easy. Uh. There 's a question about exactly how we do it. We probably will go to something better later , but the initial thing is that cepstra and spectra behave differently ,


And , uh , so the initial thing which came from , uh , the meeting that we had down south was , uh , that , um , we 'll initially just put in a mel spectrum as the second one. It 's , you know ,  cheap , easy. Uh. There 's a question about exactly how we do it. We probably will go to something better later , but the initial thing is that cepstra and spectra behave differently ,

so. Um ,  I think Tony Robinson used to do  I was saying this before. I think he used to do mel , uh , spectra and mel cepstra. He used them as alternate features. Put them together.


so. Um ,  I think Tony Robinson used to do  I was saying this before. I think he used to do mel , uh , spectra and mel cepstra. He used them as alternate features. Put them together.


so. Um ,  I think Tony Robinson used to do  I was saying this before. I think he used to do mel , uh , spectra and mel cepstra. He used them as alternate features. Put them together.

So if you took the system the way it is now , the way it 's fro you 're gonna freeze it , and it ran it on the last evaluation , where it would it be ?


So if you took the system the way it is now , the way it 's fro you 're gonna freeze it , and it ran it on the last evaluation , where it would it be ?


Although you  you know , you haven't tested it actually on the German and Danish , have you ?


Although you  you know , you haven't tested it actually on the German and Danish , have you ?

So on the ones that you did test it on it would have been second ?


So on the ones that you did test it on it would have been second ?

Yeah. Would it  I mean  But  When you 're saying second , you 're comparing to the numbers that the , uh  that the best system before got on , uh  also without German and Danish ?


Yeah. Would it  I mean  But  When you 're saying second , you 're comparing to the numbers that the , uh  that the best system before got on , uh  also without German and Danish ?

And th the ranking actually didn't change after the German and Danish. So , yeah.


And th the ranking actually didn't change after the German and Danish. So , yeah.

Well ranking didn't before , but I 'm just asking where this is to where theirs was without the German and Danish ,


Well ranking didn't before , but I 'm just asking where this is to where theirs was without the German and Danish ,


Where  where  where were we actually on the last test ?


Where  where  where were we actually on the last test ?

Oh , we were also esp essentially second , although there were  there were  I mean , we had a couple systems and they had a couple systems. And so , I guess by that  we were third , but I mean , there were two systems that were pretty close , that came from the same place.


Where  where  where were we actually on the last test ?

Oh , we were also esp essentially second , although there were  there were  I mean , we had a couple systems and they had a couple systems. And so , I guess by that  we were third , but I mean , there were two systems that were pretty close , that came from the same place.

Uh , so institutionally we were   we were second , with , uh , the third  third system.


Oh , we were also esp essentially second , although there were  there were  I mean , we had a couple systems and they had a couple systems. And so , I guess by that  we were third , but I mean , there were two systems that were pretty close , that came from the same place.

Uh , so institutionally we were   we were second , with , uh , the third  third system.

We 're  so this second that you 're saying now is system - wide second ?


Uh , so institutionally we were   we were second , with , uh , the third  third system.

We 're  so this second that you 're saying now is system - wide second ?

See  Uh , no I think it 's also institutional , isn't it ?


We 're  so this second that you 're saying now is system - wide second ?

See  Uh , no I think it 's also institutional , isn't it ?

Right ? I mean , I think both of their systems probably


See  Uh , no I think it 's also institutional , isn't it ?

Right ? I mean , I think both of their systems probably

Uh , we are between their two systems. So


Right ? I mean , I think both of their systems probably

Uh , we are between their two systems. So


Uh , we are between their two systems. So


Their  their first system is fifty - four point something. And , uh , we are fifty - three point something.


Their  their first system is fifty - four point something. And , uh , we are fifty - three point something.

But everything is  within the range of one  one percent.


Their  their first system is fifty - four point something. And , uh , we are fifty - three point something.

But everything is  within the range of one  one percent.

And their second system is also fifty - three point something. Yeah , one percent.


Their  their first system is fifty - four point something. And , uh , we are fifty - three point something.

But everything is  within the range of one  one percent.

And their second system is also fifty - three point something. Yeah , one percent.

Yeah , so  so basically they 're all  they 're all pretty close.


But everything is  within the range of one  one percent.

And their second system is also fifty - three point something. Yeah , one percent.

Yeah , so  so basically they 're all  they 're all pretty close.


And their second system is also fifty - three point something. Yeah , one percent.

Yeah , so  so basically they 're all  they 're all pretty close.


Yeah , so  so basically they 're all  they 're all pretty close.


And  and ,  um , you know , in some sense we 're all doing fairly similar things. Uh , I mean , one could argue about the LDA and so forth but I  I think , you know , in a lot of ways we 're doing very similar things. But what  what


And  and ,  um , you know , in some sense we 're all doing fairly similar things. Uh , I mean , one could argue about the LDA and so forth but I  I think , you know , in a lot of ways we 're doing very similar things. But what  what

So how did they fill up this  all these  these bits ? I mean , if we 're u


And  and ,  um , you know , in some sense we 're all doing fairly similar things. Uh , I mean , one could argue about the LDA and so forth but I  I think , you know , in a lot of ways we 're doing very similar things. But what  what

So how did they fill up this  all these  these bits ? I mean , if we 're u

Um , why are we using half ? Well , so you could  you c


And  and ,  um , you know , in some sense we 're all doing fairly similar things. Uh , I mean , one could argue about the LDA and so forth but I  I think , you know , in a lot of ways we 're doing very similar things. But what  what

So how did they fill up this  all these  these bits ? I mean , if we 're u

Um , why are we using half ? Well , so you could  you c

Yeah. Or how are they using more than half , I guess maybe is what I


So how did they fill up this  all these  these bits ? I mean , if we 're u

Um , why are we using half ? Well , so you could  you c

Yeah. Or how are they using more than half , I guess maybe is what I

Yeah , so I  I think  uh , you guys are closer to it than me , so correct me if I 'm wrong , but I  I think that what 's going on is that in  in both cases , some kind of normalization is done to deal with convola convolutional effects. Uh , they have some cepstral  modification ,


Um , why are we using half ? Well , so you could  you c

Yeah. Or how are they using more than half , I guess maybe is what I

Yeah , so I  I think  uh , you guys are closer to it than me , so correct me if I 'm wrong , but I  I think that what 's going on is that in  in both cases , some kind of normalization is done to deal with convola convolutional effects. Uh , they have some cepstral  modification ,


Yeah. Or how are they using more than half , I guess maybe is what I

Yeah , so I  I think  uh , you guys are closer to it than me , so correct me if I 'm wrong , but I  I think that what 's going on is that in  in both cases , some kind of normalization is done to deal with convola convolutional effects. Uh , they have some cepstral  modification ,

right ? In our case we have a couple things. We have the on - line normalization and then we have the LDA RASTA. And  they seem to comple complement each other enough and be different enough that they both seem to help  help us. But in any event , they 're both doing the same sort of thing. But there 's one difference. The LDA RASTA , uh , throws away high modulation frequencies. And they 're not doing that.


Yeah , so I  I think  uh , you guys are closer to it than me , so correct me if I 'm wrong , but I  I think that what 's going on is that in  in both cases , some kind of normalization is done to deal with convola convolutional effects. Uh , they have some cepstral  modification ,

right ? In our case we have a couple things. We have the on - line normalization and then we have the LDA RASTA. And  they seem to comple complement each other enough and be different enough that they both seem to help  help us. But in any event , they 're both doing the same sort of thing. But there 's one difference. The LDA RASTA , uh , throws away high modulation frequencies. And they 're not doing that.


right ? In our case we have a couple things. We have the on - line normalization and then we have the LDA RASTA. And  they seem to comple complement each other enough and be different enough that they both seem to help  help us. But in any event , they 're both doing the same sort of thing. But there 's one difference. The LDA RASTA , uh , throws away high modulation frequencies. And they 're not doing that.

So that if you throw away high modulation frequencies , then you can downsample.


So that if you throw away high modulation frequencies , then you can downsample.


So what if you didn't  So do you explicitly downsample then ? Do we explicitly downsample ?


So what if you didn't  So do you explicitly downsample then ? Do we explicitly downsample ?

And what if we didn't do that ? Would we get worse performance ?


And what if we didn't do that ? Would we get worse performance ?


And what if we didn't do that ? Would we get worse performance ?

I think it doesn't affect it , does it ?


I think it doesn't affect it , does it ?

Yeah. So I think the thing is , since we 're not evidently throwing away useful information , let 's try to put in some useful information.


Yeah. So I think the thing is , since we 're not evidently throwing away useful information , let 's try to put in some useful information.

And , uh , so I  you know , we  we 've found in a lot of ways for quite a while that having a second stream uh , helps a lot. So that 's  that 's put in , and you know , it may even end up with mel spectrum even though I 'm saying I think we could do much better , just because it 's simple.


And , uh , so I  you know , we  we 've found in a lot of ways for quite a while that having a second stream uh , helps a lot. So that 's  that 's put in , and you know , it may even end up with mel spectrum even though I 'm saying I think we could do much better , just because it 's simple.

Um. And you know , in the long run having something everybody will look at and say , " oh , yeah , I understand " , is  is very helpful.


And , uh , so I  you know , we  we 've found in a lot of ways for quite a while that having a second stream uh , helps a lot. So that 's  that 's put in , and you know , it may even end up with mel spectrum even though I 'm saying I think we could do much better , just because it 's simple.

Um. And you know , in the long run having something everybody will look at and say , " oh , yeah , I understand " , is  is very helpful.

So you would  you 're  You 're thinking to put the , uh , mel spectrum in before any of the noise removal stuff ? or after ?


Um. And you know , in the long run having something everybody will look at and say , " oh , yeah , I understand " , is  is very helpful.

So you would  you 're  You 're thinking to put the , uh , mel spectrum in before any of the noise removal stuff ? or after ?

Well , that 's a question. I mean , we were talking about that. It looks like it 'd be straightforward to  to , uh , remove the noise , um , and , uh ,


Um. And you know , in the long run having something everybody will look at and say , " oh , yeah , I understand " , is  is very helpful.

So you would  you 're  You 're thinking to put the , uh , mel spectrum in before any of the noise removal stuff ? or after ?

Well , that 's a question. I mean , we were talking about that. It looks like it 'd be straightforward to  to , uh , remove the noise , um , and , uh ,

Cuz that happens before the mel conversion , right ?


So you would  you 're  You 're thinking to put the , uh , mel spectrum in before any of the noise removal stuff ? or after ?

Well , that 's a question. I mean , we were talking about that. It looks like it 'd be straightforward to  to , uh , remove the noise , um , and , uh ,

Cuz that happens before the mel conversion , right ?

Yeah. So , I mean , to do it after the mel conversion  uh , after the noise removal , after the mel conversion. There 's even a question in my mind anyhow of whether th you should take the log or not. Uh. I sort of think you should , but I don't know.


Well , that 's a question. I mean , we were talking about that. It looks like it 'd be straightforward to  to , uh , remove the noise , um , and , uh ,

Cuz that happens before the mel conversion , right ?

Yeah. So , I mean , to do it after the mel conversion  uh , after the noise removal , after the mel conversion. There 's even a question in my mind anyhow of whether th you should take the log or not. Uh. I sort of think you should , but I don't know.


Cuz that happens before the mel conversion , right ?

Yeah. So , I mean , to do it after the mel conversion  uh , after the noise removal , after the mel conversion. There 's even a question in my mind anyhow of whether th you should take the log or not. Uh. I sort of think you should , but I don't know.

Right. Uh. Well , but normalizing spectra instead of cepstra ?


Yeah. So , I mean , to do it after the mel conversion  uh , after the noise removal , after the mel conversion. There 's even a question in my mind anyhow of whether th you should take the log or not. Uh. I sort of think you should , but I don't know.

Right. Uh. Well , but normalizing spectra instead of cepstra ?


Right. Uh. Well , but normalizing spectra instead of cepstra ?

Yeah , probably. Some kind would be good. You know ? I would think.


Right. Uh. Well , but normalizing spectra instead of cepstra ?

Yeah , probably. Some kind would be good. You know ? I would think.

Well , it  it  it  it  so it actually makes it dependent on the overall energy of the  uh , the frame.


Yeah , probably. Some kind would be good. You know ? I would think.

Well , it  it  it  it  so it actually makes it dependent on the overall energy of the  uh , the frame.


Yeah , probably. Some kind would be good. You know ? I would think.

Well , it  it  it  it  so it actually makes it dependent on the overall energy of the  uh , the frame.

If yo if you don't normalize and  if  if you don't normalize.


Well , it  it  it  it  so it actually makes it dependent on the overall energy of the  uh , the frame.

If yo if you don't normalize and  if  if you don't normalize.

Right. Yes , so I mean , one would think that you would want to normalize. But I  I  w w My thought is , uh , particularly if you take the log , try it. And then if  if normalization helps , then y you have something to compare against , and say , " OK , this much effect "  I mean , you don't want to change six things and then see what happens. You want to change them one at a time.


If yo if you don't normalize and  if  if you don't normalize.

Right. Yes , so I mean , one would think that you would want to normalize. But I  I  w w My thought is , uh , particularly if you take the log , try it. And then if  if normalization helps , then y you have something to compare against , and say , " OK , this much effect "  I mean , you don't want to change six things and then see what happens. You want to change them one at a time.


If yo if you don't normalize and  if  if you don't normalize.

Right. Yes , so I mean , one would think that you would want to normalize. But I  I  w w My thought is , uh , particularly if you take the log , try it. And then if  if normalization helps , then y you have something to compare against , and say , " OK , this much effect "  I mean , you don't want to change six things and then see what happens. You want to change them one at a time.

So adding this other stream in , that 's simple in some way. And then  saying , oh  uh  particularly because we 've found in the past there 's all these  these  these different results you get with slight modifications of how you do normalization. Normalization 's a very tricky , sensitive thing and  you learn a lot. So , I would think you would wanna  have some baseline that says , " OK , we don't normalize , this is what we get " , when we do this normalization , when we do that normalization. But  but the other question is  So I think ultimately we 'll wind up doing some normalization. I agree.


Right. Yes , so I mean , one would think that you would want to normalize. But I  I  w w My thought is , uh , particularly if you take the log , try it. And then if  if normalization helps , then y you have something to compare against , and say , " OK , this much effect "  I mean , you don't want to change six things and then see what happens. You want to change them one at a time.

So adding this other stream in , that 's simple in some way. And then  saying , oh  uh  particularly because we 've found in the past there 's all these  these  these different results you get with slight modifications of how you do normalization. Normalization 's a very tricky , sensitive thing and  you learn a lot. So , I would think you would wanna  have some baseline that says , " OK , we don't normalize , this is what we get " , when we do this normalization , when we do that normalization. But  but the other question is  So I think ultimately we 'll wind up doing some normalization. I agree.

So this second stream , will it add latency to the system


So adding this other stream in , that 's simple in some way. And then  saying , oh  uh  particularly because we 've found in the past there 's all these  these  these different results you get with slight modifications of how you do normalization. Normalization 's a very tricky , sensitive thing and  you learn a lot. So , I would think you would wanna  have some baseline that says , " OK , we don't normalize , this is what we get " , when we do this normalization , when we do that normalization. But  but the other question is  So I think ultimately we 'll wind up doing some normalization. I agree.

So this second stream , will it add latency to the system


So this second stream , will it add latency to the system


We 're not talking about computation time here.


We 're not talking about computation time here.

We 're ta I think we 're pretty far out.


We 're not talking about computation time here.

We 're ta I think we 're pretty far out.

So it 's just in terms of what data it 's depending on. It 's depending on the same data as the other.


We 're ta I think we 're pretty far out.

So it 's just in terms of what data it 's depending on. It 's depending on the same data as the other.


So it 's just in terms of what data it 's depending on. It 's depending on the same data as the other.


So with this , uh , new stream would you train up a VAD on both  both features , somehow ?


So with this , uh , new stream would you train up a VAD on both  both features , somehow ?

No , I guess the VAD has its own set of features.


So with this , uh , new stream would you train up a VAD on both  both features , somehow ?

No , I guess the VAD has its own set of features.

I mean , which could be this  one of these streams , or it can be something derived from  these streams.


No , I guess the VAD has its own set of features.

I mean , which could be this  one of these streams , or it can be something derived from  these streams.


I mean , which could be this  one of these streams , or it can be something derived from  these streams.


I mean , which could be this  one of these streams , or it can be something derived from  these streams.

And there is also the idea of using TRAPS , maybe , for the VAD , which , um


And there is also the idea of using TRAPS , maybe , for the VAD , which , um


And there is also the idea of using TRAPS , maybe , for the VAD , which , um

Well , Pratibha apparently showed , when , she was at IBM , that it 's a good idea. So.


And there is also the idea of using TRAPS , maybe , for the VAD , which , um

Well , Pratibha apparently showed , when , she was at IBM , that it 's a good idea. So.

Would  would that fit on the handset , or  ? Oh !


Well , Pratibha apparently showed , when , she was at IBM , that it 's a good idea. So.

Would  would that fit on the handset , or  ? Oh !


Would  would that fit on the handset , or  ? Oh !


Yeah , if it has to fit the delays and all this stuff.


Yeah , if it has to fit the delays and all this stuff.

Well , there 's the delays and the storage ,


Yeah , if it has to fit the delays and all this stuff.

Well , there 's the delays and the storage ,

yeah. But I don't think the storage is so big for that.


Well , there 's the delays and the storage ,

yeah. But I don't think the storage is so big for that.


yeah. But I don't think the storage is so big for that.


yeah. But I don't think the storage is so big for that.

I think th the biggest we 've run into for storage is the neural net. Right ?


I think th the biggest we 've run into for storage is the neural net. Right ?


I think th the biggest we 've run into for storage is the neural net. Right ?

Yeah. Um. And so I guess the issue there is , are we  are we using neural - net - based TRAPS , and  and how big are they ? So that 'll  that 'll be , you know , an issue.


Yeah. Um. And so I guess the issue there is , are we  are we using neural - net - based TRAPS , and  and how big are they ? So that 'll  that 'll be , you know , an issue.


Cuz she also does the , uh  the correlation - based , uh , TRAPS , with without the neural net , just looking at the correlation between


Cuz she also does the , uh  the correlation - based , uh , TRAPS , with without the neural net , just looking at the correlation between

Right. And maybe for VAD they would be OK. Yeah. Yeah.


Right. And maybe for VAD they would be OK. Yeah. Yeah.


Or a simple neural net , right ? I mean , the thing is , if you 're doing correlation , you 're just doing a simple  uh , uh  uh , dot product , you know , with some weights which you happened to learn from this  learn from the data.


Or a simple neural net , right ? I mean , the thing is , if you 're doing correlation , you 're just doing a simple  uh , uh  uh , dot product , you know , with some weights which you happened to learn from this  learn from the data.

And so , uh , putting a nonlinearity on it is ,  you know , not that big a deal. It certainly doesn't take much space.


And so , uh , putting a nonlinearity on it is ,  you know , not that big a deal. It certainly doesn't take much space.

So , uh , the question is , how complex a function do you need ? Do you need to have an added layer or something ? In which case , uh , potentially , you know , it could be big. So.


So , uh , the question is , how complex a function do you need ? Do you need to have an added layer or something ? In which case , uh , potentially , you know , it could be big. So.

So , uh , uh  So what 's next ? Maybe s s remind us.


So , uh , the question is , how complex a function do you need ? Do you need to have an added layer or something ? In which case , uh , potentially , you know , it could be big. So.

So , uh , uh  So what 's next ? Maybe s s remind us.

So the meeting with Hynek that you guys just had was to decide exactly what you were gonna freeze in this system ? Is that  ? Or was there  ? Were you talking about what t new stuff , or  ?


So , uh , uh  So what 's next ? Maybe s s remind us.

So the meeting with Hynek that you guys just had was to decide exactly what you were gonna freeze in this system ? Is that  ? Or was there  ? Were you talking about what t new stuff , or  ?

What to freeze and then what to do after we froze.


So the meeting with Hynek that you guys just had was to decide exactly what you were gonna freeze in this system ? Is that  ? Or was there  ? Were you talking about what t new stuff , or  ?

What to freeze and then what to do after we froze.

Yeah. And like I was saying , I think the  you know , the basic directions are , uh , uh  I mean , there 's lots of little things , such as improve the noise estimator but the bigger things are adding on the neural net and , uh , the second stream. And then , uh , improving the VAD. Uh. So.


What to freeze and then what to do after we froze.

Yeah. And like I was saying , I think the  you know , the basic directions are , uh , uh  I mean , there 's lots of little things , such as improve the noise estimator but the bigger things are adding on the neural net and , uh , the second stream. And then , uh , improving the VAD. Uh. So.

So , I 'll , um  I 'll actually  after the meeting I 'll add the second stream to the VAD and maybe I 'll start with the feature net in that case. It 's like , you 're looking at the VAD , right ?


Yeah. And like I was saying , I think the  you know , the basic directions are , uh , uh  I mean , there 's lots of little things , such as improve the noise estimator but the bigger things are adding on the neural net and , uh , the second stream. And then , uh , improving the VAD. Uh. So.

So , I 'll , um  I 'll actually  after the meeting I 'll add the second stream to the VAD and maybe I 'll start with the feature net in that case. It 's like , you 're looking at the VAD , right ?

Uh , yeah. I I 've a new feature net ready also.


So , I 'll , um  I 'll actually  after the meeting I 'll add the second stream to the VAD and maybe I 'll start with the feature net in that case. It 's like , you 're looking at the VAD , right ?

Uh , yeah. I I 've a new feature net ready also.

No , uh. Well p two network , one VAD and one  feature net.


Uh , yeah. I I 've a new feature net ready also.

No , uh. Well p two network , one VAD and one  feature net.


No , uh. Well p two network , one VAD and one  feature net.


No , uh. Well p two network , one VAD and one  feature net.

OK , so just figure how to take the features from the final


OK , so just figure how to take the features from the final


OK , so just figure how to take the features from the final

Um. But , yeah , I think there are plenty of issues to work on for the feature net @ @.


Um. But , yeah , I think there are plenty of issues to work on for the feature net @ @.


Um. But , yeah , I think there are plenty of issues to work on for the feature net @ @.

What about the , um  uh , the new part of the evaluation , the , uh , Wall Street Journal part ?


Um. But , yeah , I think there are plenty of issues to work on for the feature net @ @.

What about the , um  uh , the new part of the evaluation , the , uh , Wall Street Journal part ?

Right. Right. Um. Have you ever  ? Very good question. Have you ever worked with the Mississippi State h uh , software ?


What about the , um  uh , the new part of the evaluation , the , uh , Wall Street Journal part ?

Right. Right. Um. Have you ever  ? Very good question. Have you ever worked with the Mississippi State h uh , software ?


Right. Right. Um. Have you ever  ? Very good question. Have you ever worked with the Mississippi State h uh , software ?

Oh. Well you  you may be called upon to help , uh , uh , on account of , uh , all the work in this stuff here has been , uh , with small vocabulary.


Oh. Well you  you may be called upon to help , uh , uh , on account of , uh , all the work in this stuff here has been , uh , with small vocabulary.

OK. Mm - hmm. So what  how is the , uh , interaction supposed to happen ? Uh , I remember the last time we talked about this , it was sort of up in the air whether they were going to be taking , uh , people 's features and then running them or they were gonna give the system out or


Oh. Well you  you may be called upon to help , uh , uh , on account of , uh , all the work in this stuff here has been , uh , with small vocabulary.

OK. Mm - hmm. So what  how is the , uh , interaction supposed to happen ? Uh , I remember the last time we talked about this , it was sort of up in the air whether they were going to be taking , uh , people 's features and then running them or they were gonna give the system out or

Oh , so they 're gonna just deliver a system basically.


OK. Mm - hmm. So what  how is the , uh , interaction supposed to happen ? Uh , I remember the last time we talked about this , it was sort of up in the air whether they were going to be taking , uh , people 's features and then running them or they were gonna give the system out or

Oh , so they 're gonna just deliver a system basically.


Oh , so they 're gonna just deliver a system basically.


Oh , so they 're gonna just deliver a system basically.

Yeah , th I  I guess it 's almost ready.


Yeah , th I  I guess it 's almost ready.


Yeah , th I  I guess it 's almost ready.

So  That 's what  So they have released their , uh , document , describing the system.


Yeah , th I  I guess it 's almost ready.

So  That 's what  So they have released their , uh , document , describing the system.

Maybe you could , uh , point it  at Chuck ,


So  That 's what  So they have released their , uh , document , describing the system.

Maybe you could , uh , point it  at Chuck ,


Maybe you could , uh , point it  at Chuck ,


So we 'll have to grab this over CVS or something ?


So we 'll have to grab this over CVS or something ?

It - no , it 's just downloadable from their  from their web site.


So we 'll have to grab this over CVS or something ?

It - no , it 's just downloadable from their  from their web site.

Cuz one of the things that might be helpful , if you 've  if you 've got time in all of this is , is if  if these guys are really focusing on improving , uh , all the digit stuff , uh , maybe  and you got the front - end from them , maybe you could do the runs for the


It - no , it 's just downloadable from their  from their web site.

Cuz one of the things that might be helpful , if you 've  if you 've got time in all of this is , is if  if these guys are really focusing on improving , uh , all the digit stuff , uh , maybe  and you got the front - end from them , maybe you could do the runs for the


Cuz one of the things that might be helpful , if you 've  if you 've got time in all of this is , is if  if these guys are really focusing on improving , uh , all the digit stuff , uh , maybe  and you got the front - end from them , maybe you could do the runs for the

and  and , you know , iron out hassles that  that you have to , uh , tweak Joe about or whatever ,


and  and , you know , iron out hassles that  that you have to , uh , tweak Joe about or whatever ,

because you 're more experienced with running the large vocabulary stuff.


because you 're more experienced with running the large vocabulary stuff.

So I 'll point you to the web site and the mails corresponding. So I


because you 're more experienced with running the large vocabulary stuff.

So I 'll point you to the web site and the mails corresponding. So I

And it  but it 's not ready yet , the system ?


So I 'll point you to the web site and the mails corresponding. So I

And it  but it 's not ready yet , the system ?

Uh , I  I think they are still , uh , tuning something on that. So they 're like , d they 're varying different parameters like the insertion penalty and other stuff , and then seeing what 's the performance.


So I 'll point you to the web site and the mails corresponding. So I

And it  but it 's not ready yet , the system ?

Uh , I  I think they are still , uh , tuning something on that. So they 're like , d they 're varying different parameters like the insertion penalty and other stuff , and then seeing what 's the performance.

Are those going to be parameters that are frozen , nobody can change ? Or  ?


And it  but it 's not ready yet , the system ?

Uh , I  I think they are still , uh , tuning something on that. So they 're like , d they 're varying different parameters like the insertion penalty and other stuff , and then seeing what 's the performance.

Are those going to be parameters that are frozen , nobody can change ? Or  ?

Uh , w I guess there is , uh , time during which people are gonna make suggestions.


Uh , I  I think they are still , uh , tuning something on that. So they 're like , d they 're varying different parameters like the insertion penalty and other stuff , and then seeing what 's the performance.

Are those going to be parameters that are frozen , nobody can change ? Or  ?

Uh , w I guess there is , uh , time during which people are gonna make suggestions.

Oh , but everybody 's gonna have to use the same values.


Are those going to be parameters that are frozen , nobody can change ? Or  ?

Uh , w I guess there is , uh , time during which people are gonna make suggestions.

Oh , but everybody 's gonna have to use the same values.


Uh , w I guess there is , uh , time during which people are gonna make suggestions.

Oh , but everybody 's gonna have to use the same values.


Oh , but everybody 's gonna have to use the same values.


So these sugges these  this , uh , period during which people are gonna make suggestions is to know whether it is actually biased towards any set of features or


So these sugges these  this , uh , period during which people are gonna make suggestions is to know whether it is actually biased towards any set of features or

Yeah , so I th th certainly the thing that I would want to know about is whether we get really hurt , uh , on in insertion penalty , language model , scaling , sorts of things.


Yeah , so I th th certainly the thing that I would want to know about is whether we get really hurt , uh , on in insertion penalty , language model , scaling , sorts of things.


Uh , in which case , um , H Hari or Hynek will need to , you know , push the case  more about  about this.


Uh , in which case , um , H Hari or Hynek will need to , you know , push the case  more about  about this.

And we may be able to revisit this idea about , you know , somehow modifying our features to work with


And we may be able to revisit this idea about , you know , somehow modifying our features to work with


And we may be able to revisit this idea about , you know , somehow modifying our features to work with

That 's right. Um , some of that may be , uh , a last minute rush thing because if the  if our features are changing  Uh.


That 's right. Um , some of that may be , uh , a last minute rush thing because if the  if our features are changing  Uh.


That 's right. Um , some of that may be , uh , a last minute rush thing because if the  if our features are changing  Uh.

Uh. But , um. Yeah , the other thing is that even though it 's months away , uh , it 's starting to seem to me now like November fifteenth is right around the corner. And , um , if they haven't decided things like this , like what the parameters are gonna be for this , uh , when " deciding " is not just somebody deciding. I mean , in fact there should be some understanding behind the , uh ,  deciding , which means some experiments and  and so forth. It  it  it seems pretty tight to me.


That 's right. Um , some of that may be , uh , a last minute rush thing because if the  if our features are changing  Uh.

Uh. But , um. Yeah , the other thing is that even though it 's months away , uh , it 's starting to seem to me now like November fifteenth is right around the corner. And , um , if they haven't decided things like this , like what the parameters are gonna be for this , uh , when " deciding " is not just somebody deciding. I mean , in fact there should be some understanding behind the , uh ,  deciding , which means some experiments and  and so forth. It  it  it seems pretty tight to me.

So wha what 's the significance of November fifteenth ?


Uh. But , um. Yeah , the other thing is that even though it 's months away , uh , it 's starting to seem to me now like November fifteenth is right around the corner. And , um , if they haven't decided things like this , like what the parameters are gonna be for this , uh , when " deciding " is not just somebody deciding. I mean , in fact there should be some understanding behind the , uh ,  deciding , which means some experiments and  and so forth. It  it  it seems pretty tight to me.

So wha what 's the significance of November fifteenth ?


So wha what 's the significance of November fifteenth ?

Yeah. So , yeah , so after  But , you know , they may even decide in the end to push it off. It wouldn't , you know , entirely surprise me. But , uh , due to other reasons , like some people are going away , I 'm  I 'm hoping it 's not pushed off for  a l a long while. That would be , uh  put us in an awkward position. But  Anyway.


Yeah. So , yeah , so after  But , you know , they may even decide in the end to push it off. It wouldn't , you know , entirely surprise me. But , uh , due to other reasons , like some people are going away , I 'm  I 'm hoping it 's not pushed off for  a l a long while. That would be , uh  put us in an awkward position. But  Anyway.


Yeah. So , yeah , so after  But , you know , they may even decide in the end to push it off. It wouldn't , you know , entirely surprise me. But , uh , due to other reasons , like some people are going away , I 'm  I 'm hoping it 's not pushed off for  a l a long while. That would be , uh  put us in an awkward position. But  Anyway.

Great. Yeah , I think that 'll be helpful. There 's  there 's not anybody OGI currently who 's  who 's , uh , working with this and  and


Yeah. So , yeah , so after  But , you know , they may even decide in the end to push it off. It wouldn't , you know , entirely surprise me. But , uh , due to other reasons , like some people are going away , I 'm  I 'm hoping it 's not pushed off for  a l a long while. That would be , uh  put us in an awkward position. But  Anyway.

Great. Yeah , I think that 'll be helpful. There 's  there 's not anybody OGI currently who 's  who 's , uh , working with this and  and

Is  is this part of the evaluation just a small part , or ho how important is this to the overall  ?


Great. Yeah , I think that 'll be helpful. There 's  there 's not anybody OGI currently who 's  who 's , uh , working with this and  and

Is  is this part of the evaluation just a small part , or ho how important is this to the overall  ?

I  I think it 's  it 's , um  it depends how badly  you do. I mean , I think that it  it is  Uh.


Great. Yeah , I think that 'll be helpful. There 's  there 's not anybody OGI currently who 's  who 's , uh , working with this and  and

Is  is this part of the evaluation just a small part , or ho how important is this to the overall  ?

I  I think it 's  it 's , um  it depends how badly  you do. I mean , I think that it  it is  Uh.

This is one of those things that will be debated afterwards ?


Is  is this part of the evaluation just a small part , or ho how important is this to the overall  ?

I  I think it 's  it 's , um  it depends how badly  you do. I mean , I think that it  it is  Uh.

This is one of those things that will be debated afterwards ?

Yeah. Well , I mean , it 's  it 's  Conceptually , it  my impression , again , you guys correct me if I 'm wrong , but  my impression is that , um , they want it as a double check. That you haven't come across  you haven't invented features which are actually gonna do badly for a  a significantly different task , particularly one with larger vocabulary. And , um , but it 's not the main emphasis.


I  I think it 's  it 's , um  it depends how badly  you do. I mean , I think that it  it is  Uh.

This is one of those things that will be debated afterwards ?

Yeah. Well , I mean , it 's  it 's  Conceptually , it  my impression , again , you guys correct me if I 'm wrong , but  my impression is that , um , they want it as a double check. That you haven't come across  you haven't invented features which are actually gonna do badly for a  a significantly different task , particularly one with larger vocabulary. And , um , but it 's not the main emphasis.


This is one of those things that will be debated afterwards ?

Yeah. Well , I mean , it 's  it 's  Conceptually , it  my impression , again , you guys correct me if I 'm wrong , but  my impression is that , um , they want it as a double check. That you haven't come across  you haven't invented features which are actually gonna do badly for a  a significantly different task , particularly one with larger vocabulary. And , um , but it 's not the main emphasis.

I mean , the truth is , most of the applications they 're looking at are pretty small vocabulary.


Yeah. Well , I mean , it 's  it 's  Conceptually , it  my impression , again , you guys correct me if I 'm wrong , but  my impression is that , um , they want it as a double check. That you haven't come across  you haven't invented features which are actually gonna do badly for a  a significantly different task , particularly one with larger vocabulary. And , um , but it 's not the main emphasis.

I mean , the truth is , most of the applications they 're looking at are pretty small vocabulary.


I mean , the truth is , most of the applications they 're looking at are pretty small vocabulary.

So it 's  it 's a double check. So they 'll probably assign it some sort of low weight.


I mean , the truth is , most of the applications they 're looking at are pretty small vocabulary.

So it 's  it 's a double check. So they 'll probably assign it some sort of low weight.

Seems to me that if it 's a double check , they should give you a one or a zero. Y you passed the threshold or you didn't pass the threshold , and they shouldn't even care about what the score is.


So it 's  it 's a double check. So they 'll probably assign it some sort of low weight.

Seems to me that if it 's a double check , they should give you a one or a zero. Y you passed the threshold or you didn't pass the threshold , and they shouldn't even care about what the score is.

Yeah. But , I mean , we 'll  we 'll  we 'll see what they come up with. Uh , but in  in the current thing , for instance , where you have this well - matched , moderately - matched , and  and mis highly - mismatched , uh , the emphasis is somewhat on the  on the well - matched , but it 's only a  a marginal ,


Seems to me that if it 's a double check , they should give you a one or a zero. Y you passed the threshold or you didn't pass the threshold , and they shouldn't even care about what the score is.

Yeah. But , I mean , we 'll  we 'll  we 'll see what they come up with. Uh , but in  in the current thing , for instance , where you have this well - matched , moderately - matched , and  and mis highly - mismatched , uh , the emphasis is somewhat on the  on the well - matched , but it 's only a  a marginal ,

right ? It 's like forty , thirty - five , twenty - five , or something like that. So you still  if you were way , way off on the highly - mismatched , it would have a big effect.


Yeah. But , I mean , we 'll  we 'll  we 'll see what they come up with. Uh , but in  in the current thing , for instance , where you have this well - matched , moderately - matched , and  and mis highly - mismatched , uh , the emphasis is somewhat on the  on the well - matched , but it 's only a  a marginal ,

right ? It 's like forty , thirty - five , twenty - five , or something like that. So you still  if you were way , way off on the highly - mismatched , it would have a big effect.


right ? It 's like forty , thirty - five , twenty - five , or something like that. So you still  if you were way , way off on the highly - mismatched , it would have a big effect.


right ? It 's like forty , thirty - five , twenty - five , or something like that. So you still  if you were way , way off on the highly - mismatched , it would have a big effect.

And , um , it wouldn't surprise me if they did something like that with this. So again , if you 're  if you get  If it doesn't help you much , uh , for noisy versions of this  of large vocabulary data , then , uh , you know , it may not hurt you that much.


And , um , it wouldn't surprise me if they did something like that with this. So again , if you 're  if you get  If it doesn't help you much , uh , for noisy versions of this  of large vocabulary data , then , uh , you know , it may not hurt you that much.


And , um , it wouldn't surprise me if they did something like that with this. So again , if you 're  if you get  If it doesn't help you much , uh , for noisy versions of this  of large vocabulary data , then , uh , you know , it may not hurt you that much.

But if it  if you don't  if it doesn't help you much at all , um , or to put it another way , if it helps some people a lot more than it helps other people , uh , if their strategies do , then


And , um , it wouldn't surprise me if they did something like that with this. So again , if you 're  if you get  If it doesn't help you much , uh , for noisy versions of this  of large vocabulary data , then , uh , you know , it may not hurt you that much.

But if it  if you don't  if it doesn't help you much at all , um , or to put it another way , if it helps some people a lot more than it helps other people , uh , if their strategies do , then

Mm - hmm. So is this , uh  ? Uh , Guenter was putting a bunch of Wall Street Journal data on our disks.


But if it  if you don't  if it doesn't help you much at all , um , or to put it another way , if it helps some people a lot more than it helps other people , uh , if their strategies do , then

Mm - hmm. So is this , uh  ? Uh , Guenter was putting a bunch of Wall Street Journal data on our disks.


But if it  if you don't  if it doesn't help you much at all , um , or to put it another way , if it helps some people a lot more than it helps other people , uh , if their strategies do , then

Mm - hmm. So is this , uh  ? Uh , Guenter was putting a bunch of Wall Street Journal data on our disks.

So that 's the data that we 'll be running on ?


Mm - hmm. So is this , uh  ? Uh , Guenter was putting a bunch of Wall Street Journal data on our disks.

So that 's the data that we 'll be running on ?


So that 's the data that we 'll be running on ?


So that 's the data that we 'll be running on ?

Yeah. So  we have the data , just not the recognizer. OK.


Yeah. So  we have the data , just not the recognizer. OK.

So this test may take quite a while to run , then. May - judging by the amount of data that he was putting on.


Yeah. So  we have the data , just not the recognizer. OK.

So this test may take quite a while to run , then. May - judging by the amount of data that he was putting on.

Uh , well there 's training and test , right ?


So this test may take quite a while to run , then. May - judging by the amount of data that he was putting on.

Uh , well there 's training and test , right ?

No , I mean , if it 's like the other things , there 's  there 's data for training the H M Ms and  and data for testing it.


Uh , well there 's training and test , right ?

No , I mean , if it 's like the other things , there 's  there 's data for training the H M Ms and  and data for testing it.


No , I mean , if it 's like the other things , there 's  there 's data for training the H M Ms and  and data for testing it.


So , training the recognizer , but , um Um. But I think it 's trained on clean and  Is it trained on clean and  and test on  ?


So , training the recognizer , but , um Um. But I think it 's trained on clean and  Is it trained on clean and  and test on  ?

Apparently , no. It 's training on a range between ten and twenty DB , I think , and testing between five and fifteen.


Apparently , no. It 's training on a range between ten and twenty DB , I think , and testing between five and fifteen.


It 's , uh  It 's like a medium  medium - mismatch condition , sort of.


It 's , uh  It 's like a medium  medium - mismatch condition , sort of.

and  So the noise is  There is a range of different noises also  um  which are selected randomly and added randomly , uh , to the files. And there are noises that are different from the noises used  on TI - digits.


and  So the noise is  There is a range of different noises also  um  which are selected randomly and added randomly , uh , to the files. And there are noises that are different from the noises used  on TI - digits.

Yeah. Yeah. I mean , I wouldn't imagine that the amount of testing data was that huge. They probably put training  uh , almost certain they put training data there too. Maybe not. So. That 's that. Anybody have anything else ?


and  So the noise is  There is a range of different noises also  um  which are selected randomly and added randomly , uh , to the files. And there are noises that are different from the noises used  on TI - digits.

Yeah. Yeah. I mean , I wouldn't imagine that the amount of testing data was that huge. They probably put training  uh , almost certain they put training data there too. Maybe not. So. That 's that. Anybody have anything else ?

Uh , one  one last question on that. When did they estimate that they would have that system available for download ?


and  So the noise is  There is a range of different noises also  um  which are selected randomly and added randomly , uh , to the files. And there are noises that are different from the noises used  on TI - digits.

Yeah. Yeah. I mean , I wouldn't imagine that the amount of testing data was that huge. They probably put training  uh , almost certain they put training data there too. Maybe not. So. That 's that. Anybody have anything else ?

Uh , one  one last question on that. When did they estimate that they would have that system available for download ?

Um , I guess  I guess one  some preliminary version is already there.


Yeah. Yeah. I mean , I wouldn't imagine that the amount of testing data was that huge. They probably put training  uh , almost certain they put training data there too. Maybe not. So. That 's that. Anybody have anything else ?

Uh , one  one last question on that. When did they estimate that they would have that system available for download ?

Um , I guess  I guess one  some preliminary version is already there.

Oh , so there 's w something you can download to just learn ?


Uh , one  one last question on that. When did they estimate that they would have that system available for download ?

Um , I guess  I guess one  some preliminary version is already there.

Oh , so there 's w something you can download to just learn ?


Um , I guess  I guess one  some preliminary version is already there.

Oh , so there 's w something you can download to just learn ?


Oh , so there 's w something you can download to just learn ?

But they 're actually parallel - y doing some modifications also , I think.


But they 're actually parallel - y doing some modifications also , I think.


But they 're actually parallel - y doing some modifications also , I think.

So I guess the f final system will be frozen by middle of , like , one more week maybe.


So I guess the f final system will be frozen by middle of , like , one more week maybe.


You know , their  their  They have a lot of options  in their recognizer and  and the SVM is one of the things they 've done with it , but it 's not their more standard thing.


You know , their  their  They have a lot of options  in their recognizer and  and the SVM is one of the things they 've done with it , but it 's not their more standard thing.

For the most part it 's  it 's Gaussian mixtures.


For the most part it 's  it 's Gaussian mixtures.


For the most part it 's  it 's Gaussian mixtures.

It 's just a HMM , Gaussian mixture model.


It 's just a HMM , Gaussian mixture model.


Yeah , the SVM thing was an HMM also. It was just a  it  it  it was like a hybrid , like


So , just so that I understand , they 're providing scripts and everything so that basically , uh , you  you push a button and it does training , and then it does test , and everything ? Is that  the idea ?


So , just so that I understand , they 're providing scripts and everything so that basically , uh , you  you push a button and it does training , and then it does test , and everything ? Is that  the idea ?

I  I  I think  yeah , I  I guess something like that. It 's like   as painless as possible ,


So , just so that I understand , they 're providing scripts and everything so that basically , uh , you  you push a button and it does training , and then it does test , and everything ? Is that  the idea ?

I  I  I think  yeah , I  I guess something like that. It 's like   as painless as possible ,

is what  Do they provide all the scripts , everything , and then  Just ,


I  I  I think  yeah , I  I guess something like that. It 's like   as painless as possible ,

is what  Do they provide all the scripts , everything , and then  Just ,

I see. Hmm. Somehow yo there 's hooks to put your features in and


is what  Do they provide all the scripts , everything , and then  Just ,

I see. Hmm. Somehow yo there 's hooks to put your features in and


I see. Hmm. Somehow yo there 's hooks to put your features in and

Hmm. Yeah , um. In fact , I mean , if you look into it a little bit , it might be reasonable  You know Joe , right ? Yeah.


Hmm. Yeah , um. In fact , I mean , if you look into it a little bit , it might be reasonable  You know Joe , right ? Yeah.


Hmm. Yeah , um. In fact , I mean , if you look into it a little bit , it might be reasonable  You know Joe , right ? Yeah.

Just to sort of ask him about the issue of , um , different features having different kinds of , uh , scaling characteristics and so on. So that , you know , w w possibly having entirely different optimal values for  for the usual twiddle factors and what 's  what 's the plan about that ?


Just to sort of ask him about the issue of , um , different features having different kinds of , uh , scaling characteristics and so on. So that , you know , w w possibly having entirely different optimal values for  for the usual twiddle factors and what 's  what 's the plan about that ?

So sh shall we , like , add Chuck also to the mailing lists ? It may be better , I mean , in that case if he 's going to


So sh shall we , like , add Chuck also to the mailing lists ? It may be better , I mean , in that case if he 's going to

Because there 's a mailing list for this.


Because there 's a mailing list for this.


Because there 's a mailing list for this.

Yeah , I guess maybe Hari or Hynek , one of them , has to  send a mail to Joe. Or maybe if you


Yeah , I guess maybe Hari or Hynek , one of them , has to  send a mail to Joe. Or maybe if you


I  I was just talking with him on email the other day actually.


I  I was just talking with him on email the other day actually.

Uh , yeah , and just , um , se maybe see.


Uh , yeah , and just , um , se maybe see.


Yeah , so maybe just CC Hari and say that you 've just been asked to handle the large vocabulary part here , and , uh , you know ,


Yeah , so maybe just CC Hari and say that you 've just been asked to handle the large vocabulary part here , and , uh , you know ,

OK. Would it be better if I asked Hari to ask Joe ?


Yeah , so maybe just CC Hari and say that you 've just been asked to handle the large vocabulary part here , and , uh , you know ,

OK. Would it be better if I asked Hari to ask Joe ?

Uh. Why don't you just ask Joe but CC Hari , and then in the note say , " Hari , hopefully this is OK with you ".


OK. Would it be better if I asked Hari to ask Joe ?

Uh. Why don't you just ask Joe but CC Hari , and then in the note say , " Hari , hopefully this is OK with you ".

And then if Joe feels like he needs a confirmation , Hari can answer it.


Uh. Why don't you just ask Joe but CC Hari , and then in the note say , " Hari , hopefully this is OK with you ".

And then if Joe feels like he needs a confirmation , Hari can answer it.


And then if Joe feels like he needs a confirmation , Hari can answer it.


And then if Joe feels like he needs a confirmation , Hari can answer it.

That way you can get started asking  Joe quickly while he 's  while he 's maybe still , you know , putting in nails and screws and  Yeah.


That way you can get started asking  Joe quickly while he 's  while he 's maybe still , you know , putting in nails and screws and  Yeah.

And there is an , uh , archive of all the mails that has been  gon that has gone , uh , between these people  among these people. So just you can see all this  mails in the ISIP web site


And there is an , uh , archive of all the mails that has been  gon that has gone , uh , between these people  among these people. So just you can see all this  mails in the ISIP web site


Have you thought about  how long  would be uh , most useful for you to go up to OGI ?


Have you thought about  how long  would be uh , most useful for you to go up to OGI ?

I don't know , uh. We can   For September , we can set up a work schedule and we can maybe work independently. And then at some point it maybe be better to work together again.


Have you thought about  how long  would be uh , most useful for you to go up to OGI ?

I don't know , uh. We can   For September , we can set up a work schedule and we can maybe work independently. And then at some point it maybe be better to work together again.

Oh , so you 're  you 're imagining more that you would come back here first for a while and then  and then go up there ?


I don't know , uh. We can   For September , we can set up a work schedule and we can maybe work independently. And then at some point it maybe be better to work together again.

Oh , so you 're  you 're imagining more that you would come back here first for a while and then  and then go up there ?


Oh , so you 're  you 're imagining more that you would come back here first for a while and then  and then go up there ?

I ju you guys are Well , y anyway , you don't have to decide this second but thi think about it  about what  what you would think would be the  the best way to work it. I 'll


I ju you guys are Well , y anyway , you don't have to decide this second but thi think about it  about what  what you would think would be the  the best way to work it. I 'll


Um. Well , I 've been reading some literature about clustering of data. Just , um , I guess , let me put it in context. OK , so we 're talking about discovering intermediate categories to , um  to classify. And , uh , I was looking at some of the work that , uh , Sangita was doing on these TRAPS things. So she has , um  she has temporal patterns for , um , a certain set of phonemes , from  from TIMIT , right ? the most common phonemes. And each one of them has  has a  a nice pattern over time , a one  one second window. And it has  has these patterns. Um , so she has , um a TRAP for each one of the phonemes , um , times fifteen , for each of the fifteen critical bands. And , um ,  she does this agglomerative hierarchical clustering which  which basically , um , is a clustering algorithm that , uh , starts with many , many , many different points  many different clusters  uh , corresponding to the number of data , uh , patterns that you have in the data. And then you have this distance mej metric which , uh , measures how  how closely related they are. And you start , um  by merging the patterns that are most closely related.


And then you can pick , uh , values anywhere along that tree to fix your set of clusters.


And then you can pick , uh , values anywhere along that tree to fix your set of clusters.

Right , usually it 's when , um  when the sol similarity measures , um , don't go down as much.


And then you can pick , uh , values anywhere along that tree to fix your set of clusters.

Right , usually it 's when , um  when the sol similarity measures , um , don't go down as much.

And so , uh  so you stop at that point. And what she found was , sh um , was there were five broad , um  broad categories , uh , corresponding to , uh , things like , uh , fricatives and , uh , vocalic , um , and , uh , stops.


Right , usually it 's when , um  when the sol similarity measures , um , don't go down as much.

And so , uh  so you stop at that point. And what she found was , sh um , was there were five broad , um  broad categories , uh , corresponding to , uh , things like , uh , fricatives and , uh , vocalic , um , and , uh , stops.


And so , uh  so you stop at that point. And what she found was , sh um , was there were five broad , um  broad categories , uh , corresponding to , uh , things like , uh , fricatives and , uh , vocalic , um , and , uh , stops.

And , uh , one for silence and  and another one for schwa  schwa sounds. Um , and , um , I was thinking about ways to  to generalize this because w you 're  it 's sort of like a  it 's not a completely automatic way of clustering , because yo beforehand you have these  these TRAPS and you 're saying that  that these frames correspond to this particular phoneme. Um , and that 's  that 's constraining your  your clustering to  to the set of phonemes that you already have. Um , whereas maybe we want to just take  take a look at , um , arbitrary windows in time , um , of varying length , um , and cluster those.


And , uh , one for silence and  and another one for schwa  schwa sounds. Um , and , um , I was thinking about ways to  to generalize this because w you 're  it 's sort of like a  it 's not a completely automatic way of clustering , because yo beforehand you have these  these TRAPS and you 're saying that  that these frames correspond to this particular phoneme. Um , and that 's  that 's constraining your  your clustering to  to the set of phonemes that you already have. Um , whereas maybe we want to just take  take a look at , um , arbitrary windows in time , um , of varying length , um , and cluster those.

And I 'm thinking if we  if we do that , then we would probably , um , at some point in the clustering algorithm find that we 've clustered things like , OK , thi this is a transition , um , this is a relatively stable  stable point.


And I 'm thinking if we  if we do that , then we would probably , um , at some point in the clustering algorithm find that we 've clustered things like , OK , thi this is a transition , um , this is a relatively stable  stable point.

Um , and I 'm hoping to find other things of  of similarity and maybe use these things as the intermediate , um  intermediate categories that , uh , um , I 'll later classify.


Um , and I 'm hoping to find other things of  of similarity and maybe use these things as the intermediate , um  intermediate categories that , uh , um , I 'll later classify.

Are you looking at these in narrow bands ?


Are you looking at these in narrow bands ?

Cuz that 's what you 're gonna be using , right ?


Are you looking at these in narrow bands ?

Cuz that 's what you 're gonna be using , right ?

Yeah , yeah. I  I haven't exactly figured out , um , the exact details for that but , uh , the  the representation of the data that I was thinking of , was using , um , critical band , um , energies ,  um , over different lengths of time. So  Yeah.


Cuz that 's what you 're gonna be using , right ?

Yeah , yeah. I  I haven't exactly figured out , um , the exact details for that but , uh , the  the representation of the data that I was thinking of , was using , um , critical band , um , energies ,  um , over different lengths of time. So  Yeah.

Yeah , I mean , it seems somehow that needs th uh , there 's a couple things that I wonder about with this. I mean , so one is  is ,  again , looking at the same representation ,


Yeah , yeah. I  I haven't exactly figured out , um , the exact details for that but , uh , the  the representation of the data that I was thinking of , was using , um , critical band , um , energies ,  um , over different lengths of time. So  Yeah.

Yeah , I mean , it seems somehow that needs th uh , there 's a couple things that I wonder about with this. I mean , so one is  is ,  again , looking at the same representation ,

I mean , if you 're going for this sort of thing where you have  uh , little detectors that are looking at narrow bands , then what you 're going to be looking for should be some category that you can find with the narrow bands.


Yeah , I mean , it seems somehow that needs th uh , there 's a couple things that I wonder about with this. I mean , so one is  is ,  again , looking at the same representation ,

I mean , if you 're going for this sort of thing where you have  uh , little detectors that are looking at narrow bands , then what you 're going to be looking for should be some category that you can find with the narrow bands.


I mean , if you 're going for this sort of thing where you have  uh , little detectors that are looking at narrow bands , then what you 're going to be looking for should be some category that you can find with the narrow bands.

That  that seems to be kind of fundamental to it. Um , and then the other thing , uh , is  that I wonder about with it , and  and don't take this in the wrong way , like I  I know what I 'm doing or anything ,


That  that seems to be kind of fundamental to it. Um , and then the other thing , uh , is  that I wonder about with it , and  and don't take this in the wrong way , like I  I know what I 'm doing or anything ,

but , I mean.  Um , just wondering really.


but , I mean.  Um , just wondering really.

Um , the sort of standard answer about this sort of thing is that if you 're trying to find  the right system in some sense , whether you 're trying by categories or  or parameters  um , and your goal is discrimination , then having choices based on discrimination as opposed to , um , unsupervised nearness of things , um , is actually better.


Um , the sort of standard answer about this sort of thing is that if you 're trying to find  the right system in some sense , whether you 're trying by categories or  or parameters  um , and your goal is discrimination , then having choices based on discrimination as opposed to , um , unsupervised nearness of things , um , is actually better.

Um , and I don't know if that  I mean , since you 're dealing with issues of robustness , you know , maybe  maybe this isn't right , but it 'd be something I 'd be concerned about. Because , for instance , you can imagine , uh , uh , i i if you remember from  from , uh  from your  your quals , John Ohala saying that , uh , " buh "  and " puh "  differed , uh , not really cuz of voicing but because of aspiration. I mean , in as far as wha what 's really there in the acoustics.


Um , and I don't know if that  I mean , since you 're dealing with issues of robustness , you know , maybe  maybe this isn't right , but it 'd be something I 'd be concerned about. Because , for instance , you can imagine , uh , uh , i i if you remember from  from , uh  from your  your quals , John Ohala saying that , uh , " buh "  and " puh "  differed , uh , not really cuz of voicing but because of aspiration. I mean , in as far as wha what 's really there in the acoustics.

So , um , if you looked  if you were doing some coarse clustering , you probably would put those two sounds together. And yet , I would gue I would guess that many of your recognition errors were coming from , uh , um , pfft ,  screwing up on this distinction.


So , um , if you looked  if you were doing some coarse clustering , you probably would put those two sounds together. And yet , I would gue I would guess that many of your recognition errors were coming from , uh , um , pfft ,  screwing up on this distinction.

So , in fact , it 's a little hard because recognizers , to first order , sort of work. And the reasons we 're doing the things we 're doing is because they don't work as well as we 'd like. And since they sort of work , uh , it means that they are already doing  if you go and take any recognizer that 's already out there and you say , " how well is it distinguishing between  schwas and stops ? "


So , in fact , it 's a little hard because recognizers , to first order , sort of work. And the reasons we 're doing the things we 're doing is because they don't work as well as we 'd like. And since they sort of work , uh , it means that they are already doing  if you go and take any recognizer that 's already out there and you say , " how well is it distinguishing between  schwas and stops ? "

Boy , I bet they 're all doing nearly perfectly on this , right ?


Boy , I bet they 're all doing nearly perfectly on this , right ?

So these  these big categories that differ in huge obvious ways , we already know how to do. So , what are we bringing to the party ? I mean , in fact what we wanna do is have something that , particularly in the presence of noise , uh , is better at distinguishing between , uh , categories that are actually close to one another , and hence , would probably be clustered together.


So these  these big categories that differ in huge obvious ways , we already know how to do. So , what are we bringing to the party ? I mean , in fact what we wanna do is have something that , particularly in the presence of noise , uh , is better at distinguishing between , uh , categories that are actually close to one another , and hence , would probably be clustered together.

So that 's th that 's the hard thing. I mean , I understand that there 's this other constraint that you 're considering , is that you wanna have categories that , uh  that would be straightforward for , say , a human being to mark if you had manual annotation. And it 's something that you really think you can pick up. But I think it 's also essential that you wanna look at what are the  confusions that you 're making and how can you come up with , uh , categories that , uh , can clarify these confusions.


So that 's th that 's the hard thing. I mean , I understand that there 's this other constraint that you 're considering , is that you wanna have categories that , uh  that would be straightforward for , say , a human being to mark if you had manual annotation. And it 's something that you really think you can pick up. But I think it 's also essential that you wanna look at what are the  confusions that you 're making and how can you come up with , uh , categories that , uh , can clarify these confusions.

So , I mean , the standard sort of way of doing that is take a look at the algorithms you 're looking at , but then throw in some discriminative aspect to it.  Y y this is more like , you know , how does LDA differ from PCA ? I mean , they 're the same sort of thing. They 're both orthogonalizing.


So , I mean , the standard sort of way of doing that is take a look at the algorithms you 're looking at , but then throw in some discriminative aspect to it.  Y y this is more like , you know , how does LDA differ from PCA ? I mean , they 're the same sort of thing. They 're both orthogonalizing.

But , you know  and  and , um , this is a little harder because you 're not just trying to find parameters. You 're actually trying to find the  the  the  the categories themselves. Uh , so a little more like brain surgery , I think on yourself. Uh. So , uh


But , you know  and  and , um , this is a little harder because you 're not just trying to find parameters. You 're actually trying to find the  the  the  the categories themselves. Uh , so a little more like brain surgery , I think on yourself. Uh. So , uh


You 've been thinking about this problem for a long time actually. I mean , well  W actually , you stopped thinking about it for a long time , but you used to think about it  a lot.


You 've been thinking about this problem for a long time actually. I mean , well  W actually , you stopped thinking about it for a long time , but you used to think about it  a lot.

And you 've been thinking about it more now ,


And you 've been thinking about it more now ,


I guess  I don't  I don't  um , it 's not clear to me how to reconcile , you know , what you 're saying , which I think is right , with  the way I 've been looking at it. That it 's  it 's  it 's all not very clear to me. But it seems to me that the desire  the desirable feature to have is something that , um , is bottom - up. You know , however we do that.


I guess  I don't  I don't  um , it 's not clear to me how to reconcile , you know , what you 're saying , which I think is right , with  the way I 've been looking at it. That it 's  it 's  it 's all not very clear to me. But it seems to me that the desire  the desirable feature to have is something that , um , is bottom - up. You know , however we do that.

And and so I guess what I don't understand is how to do that and still be discriminative , because to be discriminative you have to have categories and the only categories that we know of to use are sort of these human  human sig significant  categories that are significant to humans , like phonemes , things like that.


And and so I guess what I don't understand is how to do that and still be discriminative , because to be discriminative you have to have categories and the only categories that we know of to use are sort of these human  human sig significant  categories that are significant to humans , like phonemes , things like that.

But that 's sort of what you want to avoid. And so that feels  I don't know how to get out of this.


And and so I guess what I don't understand is how to do that and still be discriminative , because to be discriminative you have to have categories and the only categories that we know of to use are sort of these human  human sig significant  categories that are significant to humans , like phonemes , things like that.

But that 's sort of what you want to avoid. And so that feels  I don't know how to get out of this.

Well , here 's a  here 's a , uh , uh Here 's a generic and possibly useless thought , which is ,  um , what do you really  I mean , in a sense the only s s systems that make sense , uh , are ones that  that have something from top - down in th in them. Right ? Because if e even the smallest organism that 's trying to learn to do anything , if it doesn't have any kind of reward for doing  or penal penalty for doing anything , then it 's just going to behave randomly.


But that 's sort of what you want to avoid. And so that feels  I don't know how to get out of this.

Well , here 's a  here 's a , uh , uh Here 's a generic and possibly useless thought , which is ,  um , what do you really  I mean , in a sense the only s s systems that make sense , uh , are ones that  that have something from top - down in th in them. Right ? Because if e even the smallest organism that 's trying to learn to do anything , if it doesn't have any kind of reward for doing  or penal penalty for doing anything , then it 's just going to behave randomly.


But that 's sort of what you want to avoid. And so that feels  I don't know how to get out of this.

Well , here 's a  here 's a , uh , uh Here 's a generic and possibly useless thought , which is ,  um , what do you really  I mean , in a sense the only s s systems that make sense , uh , are ones that  that have something from top - down in th in them. Right ? Because if e even the smallest organism that 's trying to learn to do anything , if it doesn't have any kind of reward for doing  or penal penalty for doing anything , then it 's just going to behave randomly.

So whether you 're talking about something being learned through evolution or being learned through experience , it 's gotta have something come down to it that gives its reward or , you know , at least some reinforcement learning ,


Well , here 's a  here 's a , uh , uh Here 's a generic and possibly useless thought , which is ,  um , what do you really  I mean , in a sense the only s s systems that make sense , uh , are ones that  that have something from top - down in th in them. Right ? Because if e even the smallest organism that 's trying to learn to do anything , if it doesn't have any kind of reward for doing  or penal penalty for doing anything , then it 's just going to behave randomly.

So whether you 're talking about something being learned through evolution or being learned through experience , it 's gotta have something come down to it that gives its reward or , you know , at least some reinforcement learning ,


So whether you 're talking about something being learned through evolution or being learned through experience , it 's gotta have something come down to it that gives its reward or , you know , at least some reinforcement learning ,


We could stop at words , but we don't , right ? We go all the way down to phonemes.


We could stop at words , but we don't , right ? We go all the way down to phonemes.

Right , but I me I  I think that maybe in some ways part of the difficulty is  is trying to deal with the  with these phonemes. You know , and  and  and i it 's almost like you want categories if  if our  if our , uh , um ,  metric of  of goodness , uh , i if our


We could stop at words , but we don't , right ? We go all the way down to phonemes.

Right , but I me I  I think that maybe in some ways part of the difficulty is  is trying to deal with the  with these phonemes. You know , and  and  and i it 's almost like you want categories if  if our  if our , uh , um ,  metric of  of goodness , uh , i if our

correction  if our metric of badness  is word error rate then , um , maybe we should be looking at words.


Right , but I me I  I think that maybe in some ways part of the difficulty is  is trying to deal with the  with these phonemes. You know , and  and  and i it 's almost like you want categories if  if our  if our , uh , um ,  metric of  of goodness , uh , i if our

correction  if our metric of badness  is word error rate then , um , maybe we should be looking at words.


correction  if our metric of badness  is word error rate then , um , maybe we should be looking at words.

I mean , for  for  for very nice , uh , reasons we 've looked for a while at syllables , and they have a lot of good properties , but i i i if you go all the way to words , I mean , that 's really  I mean , d w In many applications you wanna go further. You wanna go to concepts or something , or have  have  have concepts , actions , this sort of thing.


I mean , for  for  for very nice , uh , reasons we 've looked for a while at syllables , and they have a lot of good properties , but i i i if you go all the way to words , I mean , that 's really  I mean , d w In many applications you wanna go further. You wanna go to concepts or something , or have  have  have concepts , actions , this sort of thing.


I mean , for  for  for very nice , uh , reasons we 've looked for a while at syllables , and they have a lot of good properties , but i i i if you go all the way to words , I mean , that 's really  I mean , d w In many applications you wanna go further. You wanna go to concepts or something , or have  have  have concepts , actions , this sort of thing.

Yeah , so the common  right , the common wisdom is you can't do words because there 's too many of them , right ? So you have to have some smaller set that you can use , uh , and  and so everybody goes to phonemes. But the problem is that we  we build models of words in terms of phonemes and these models are  are really cartoon - ish , right ? So when you look at conversational speech , for example , you don't see the phonemes that you  that you have in your word models.


Yeah , so the common  right , the common wisdom is you can't do words because there 's too many of them , right ? So you have to have some smaller set that you can use , uh , and  and so everybody goes to phonemes. But the problem is that we  we build models of words in terms of phonemes and these models are  are really cartoon - ish , right ? So when you look at conversational speech , for example , you don't see the phonemes that you  that you have in your word models.

Yeah. But  but  but we 're not trying for models of words here. See , so her here 's maybe where  If the issue is that we 're trying to come up with , um , some sort of intermediate categories which will then be useful for later stuff , uh , then  maybe it doesn't matter that we can't have enough


Yeah , so the common  right , the common wisdom is you can't do words because there 's too many of them , right ? So you have to have some smaller set that you can use , uh , and  and so everybody goes to phonemes. But the problem is that we  we build models of words in terms of phonemes and these models are  are really cartoon - ish , right ? So when you look at conversational speech , for example , you don't see the phonemes that you  that you have in your word models.

Yeah. But  but  but we 're not trying for models of words here. See , so her here 's maybe where  If the issue is that we 're trying to come up with , um , some sort of intermediate categories which will then be useful for later stuff , uh , then  maybe it doesn't matter that we can't have enough

I mean , what you wanna do is  is build up these categories that are  that are best for word recognition.


Yeah. But  but  but we 're not trying for models of words here. See , so her here 's maybe where  If the issue is that we 're trying to come up with , um , some sort of intermediate categories which will then be useful for later stuff , uh , then  maybe it doesn't matter that we can't have enough

I mean , what you wanna do is  is build up these categories that are  that are best for word recognition.


I mean , what you wanna do is  is build up these categories that are  that are best for word recognition.

And  and somehow if that 's built into the loop of what the categories  I mean , we do this every day in this very gross way of  of running o a thousand experiments


And  and somehow if that 's built into the loop of what the categories  I mean , we do this every day in this very gross way of  of running o a thousand experiments

because we have fast computers and picking the thing that has the best word error rate.


because we have fast computers and picking the thing that has the best word error rate.

In some way  I mean , we derive that all the time. In some ways it 's really not  a bad  bad thing to do because it tells you in fact how your adjustments at the very low level affect the  the final goal.


In some way  I mean , we derive that all the time. In some ways it 's really not  a bad  bad thing to do because it tells you in fact how your adjustments at the very low level affect the  the final goal.

Um , so maybe there 's a way to even put that in in a much more automatic way ,


Um , so maybe there 's a way to even put that in in a much more automatic way ,

where you take , you know , something about the error at the level of the word or some other  it could be syllable  but in some large unit ,


where you take , you know , something about the error at the level of the word or some other  it could be syllable  but in some large unit ,

uh , and uh  yeah , you may not have word models , you have phone models , whatever , but you sort of  don't worry about that , and just somehow feed it back through.


uh , and uh  yeah , you may not have word models , you have phone models , whatever , but you sort of  don't worry about that , and just somehow feed it back through.

You know , so that 's , uh , wh what I called a useless comments because I 'm not really telling you how to do it. But I mean , it 's a   it 's  it 's , you know  it


uh , and uh  yeah , you may not have word models , you have phone models , whatever , but you sort of  don't worry about that , and just somehow feed it back through.

You know , so that 's , uh , wh what I called a useless comments because I 'm not really telling you how to do it. But I mean , it 's a   it 's  it 's , you know  it

No , but I think the important part in there is that , you know , if you want to be discriminative , you have to have uh , you know , categories.


You know , so that 's , uh , wh what I called a useless comments because I 'm not really telling you how to do it. But I mean , it 's a   it 's  it 's , you know  it

No , but I think the important part in there is that , you know , if you want to be discriminative , you have to have uh , you know , categories.


You know , so that 's , uh , wh what I called a useless comments because I 'm not really telling you how to do it. But I mean , it 's a   it 's  it 's , you know  it

No , but I think the important part in there is that , you know , if you want to be discriminative , you have to have uh , you know , categories.

And I think this  the important categories are the words , and  not the phones.


No , but I think the important part in there is that , you know , if you want to be discriminative , you have to have uh , you know , categories.

And I think this  the important categories are the words , and  not the phones.


And I think this  the important categories are the words , and  not the phones.

Maybe. And so  Right. If you can put the words in to the loop somehow for determining goodness of your sets of clusters  Uh


And I think this  the important categories are the words , and  not the phones.

Maybe. And so  Right. If you can put the words in to the loop somehow for determining goodness of your sets of clusters  Uh

Now , that being said , I think that  that if you have something that is , um  i Once you start dealing with spontaneous speech , all the things you 're saying are  are really true.


Maybe. And so  Right. If you can put the words in to the loop somehow for determining goodness of your sets of clusters  Uh

Now , that being said , I think that  that if you have something that is , um  i Once you start dealing with spontaneous speech , all the things you 're saying are  are really true.


Maybe. And so  Right. If you can put the words in to the loop somehow for determining goodness of your sets of clusters  Uh

Now , that being said , I think that  that if you have something that is , um  i Once you start dealing with spontaneous speech , all the things you 're saying are  are really true.

If you  have read speech that 's been manually annotated , like TIMIT , then , you know , i i you the phones are gonna be right , actually ,  for the most part.


Now , that being said , I think that  that if you have something that is , um  i Once you start dealing with spontaneous speech , all the things you 're saying are  are really true.

If you  have read speech that 's been manually annotated , like TIMIT , then , you know , i i you the phones are gonna be right , actually ,  for the most part.


If you  have read speech that 's been manually annotated , like TIMIT , then , you know , i i you the phones are gonna be right , actually ,  for the most part.

So  so , uh , it doesn't really hurt them to  to do that , to put in discrimination at that level.


So  so , uh , it doesn't really hurt them to  to do that , to put in discrimination at that level.

Um , if you go to spontaneous speech then it 's  it 's trickier and  and  and , uh , the phones are  uh , you know , it 's gonna be based on bad pronunciation models that you have of


So  so , uh , it doesn't really hurt them to  to do that , to put in discrimination at that level.

Um , if you go to spontaneous speech then it 's  it 's trickier and  and  and , uh , the phones are  uh , you know , it 's gonna be based on bad pronunciation models that you have of

and , um  And it won't allow for the overlapping phenomenon