OK , did you solve speech recognition last week ?


OK. It 's April fifth. Actually , Hynek should be getting back in town shortly if he isn't already.


OK. It 's April fifth. Actually , Hynek should be getting back in town shortly if he isn't already.

Uh. Well , we 'll drag him here. I know where he is.


OK. It 's April fifth. Actually , Hynek should be getting back in town shortly if he isn't already.

Uh. Well , we 'll drag him here. I know where he is.

So when you said " in town " , you mean  Oregon.


Uh. Well , we 'll drag him here. I know where he is.

So when you said " in town " , you mean  Oregon.

U u u u uh , I meant , you know , this end of the world , yeah ,  is really what I meant ,


So when you said " in town " , you mean  Oregon.

U u u u uh , I meant , you know , this end of the world , yeah ,  is really what I meant ,


U u u u uh , I meant , you know , this end of the world , yeah ,  is really what I meant ,


I have something just fairly brief to report on.


I have something just fairly brief to report on.

Um , I did some  experim uh , uh , just a few more experiments before I had to ,  uh , go away for the w well , that week.


Um , I did some  experim uh , uh , just a few more experiments before I had to ,  uh , go away for the w well , that week.

Was it last week or whenever ? Um , so what I was started playing with was the  th again , this is the HTK back - end. And , um , I was curious because the way that they train up the models ,  they go through about four sort of rounds of  of training. And in the first round they do  uh , I think it 's three iterations , and for the last three rounds e e they do seven iterations of re - estimation in each of those three. And so , you know , that 's part of what takes so long to train the  the  the back - end for this.


Um , I did some  experim uh , uh , just a few more experiments before I had to ,  uh , go away for the w well , that week.

Was it last week or whenever ? Um , so what I was started playing with was the  th again , this is the HTK back - end. And , um , I was curious because the way that they train up the models ,  they go through about four sort of rounds of  of training. And in the first round they do  uh , I think it 's three iterations , and for the last three rounds e e they do seven iterations of re - estimation in each of those three. And so , you know , that 's part of what takes so long to train the  the  the back - end for this.

I 'm sorry , I didn't quite get that. There 's  there 's four and there 's seven and  I  I 'm sorry.


Was it last week or whenever ? Um , so what I was started playing with was the  th again , this is the HTK back - end. And , um , I was curious because the way that they train up the models ,  they go through about four sort of rounds of  of training. And in the first round they do  uh , I think it 's three iterations , and for the last three rounds e e they do seven iterations of re - estimation in each of those three. And so , you know , that 's part of what takes so long to train the  the  the back - end for this.

I 'm sorry , I didn't quite get that. There 's  there 's four and there 's seven and  I  I 'm sorry.

Yeah. Uh , maybe I should write it on the board. So ,  there 's four rounds of training. Um , I g I g I guess you could say iterations. The first one is three , then seven , seven , and seven. And what these numbers refer to is the number of times that the , uh , HMM re - estimation is run. It 's this program called H E


Was it last week or whenever ? Um , so what I was started playing with was the  th again , this is the HTK back - end. And , um , I was curious because the way that they train up the models ,  they go through about four sort of rounds of  of training. And in the first round they do  uh , I think it 's three iterations , and for the last three rounds e e they do seven iterations of re - estimation in each of those three. And so , you know , that 's part of what takes so long to train the  the  the back - end for this.

I 'm sorry , I didn't quite get that. There 's  there 's four and there 's seven and  I  I 'm sorry.

Yeah. Uh , maybe I should write it on the board. So ,  there 's four rounds of training. Um , I g I g I guess you could say iterations. The first one is three , then seven , seven , and seven. And what these numbers refer to is the number of times that the , uh , HMM re - estimation is run. It 's this program called H E

But in HTK , what 's the difference between , uh , a  an inner loop and an outer loop in these iterations ?


I 'm sorry , I didn't quite get that. There 's  there 's four and there 's seven and  I  I 'm sorry.

Yeah. Uh , maybe I should write it on the board. So ,  there 's four rounds of training. Um , I g I g I guess you could say iterations. The first one is three , then seven , seven , and seven. And what these numbers refer to is the number of times that the , uh , HMM re - estimation is run. It 's this program called H E

But in HTK , what 's the difference between , uh , a  an inner loop and an outer loop in these iterations ?

OK. So what happens is , um , at each one of these points , you increase the number of Gaussians in the model.


Yeah. Uh , maybe I should write it on the board. So ,  there 's four rounds of training. Um , I g I g I guess you could say iterations. The first one is three , then seven , seven , and seven. And what these numbers refer to is the number of times that the , uh , HMM re - estimation is run. It 's this program called H E

But in HTK , what 's the difference between , uh , a  an inner loop and an outer loop in these iterations ?

OK. So what happens is , um , at each one of these points , you increase the number of Gaussians in the model.

Yeah. Oh , right ! This was the mix up stuff.


But in HTK , what 's the difference between , uh , a  an inner loop and an outer loop in these iterations ?

OK. So what happens is , um , at each one of these points , you increase the number of Gaussians in the model.

Yeah. Oh , right ! This was the mix up stuff.


OK. So what happens is , um , at each one of these points , you increase the number of Gaussians in the model.

Yeah. Oh , right ! This was the mix up stuff.


Yeah. Oh , right ! This was the mix up stuff.


And so , in the final one here , you end up with , uh  for all of the  the digit words , you end up with , uh , three  mixtures per state ,


And so , in the final one here , you end up with , uh  for all of the  the digit words , you end up with , uh , three  mixtures per state ,

eh , in the final  thing. So I had done some experiments where I was  I  I want to play with the number of mixtures.


eh , in the final  thing. So I had done some experiments where I was  I  I want to play with the number of mixtures.

But , um , uh , I wanted to first test to see if we actually need to do  this many iterations early on.


But , um , uh , I wanted to first test to see if we actually need to do  this many iterations early on.


But , um , uh , I wanted to first test to see if we actually need to do  this many iterations early on.

And so , um , I  I ran a couple of experiments where I  reduced that to l to be three , two , two ,  uh , five , I think , and I got almost the exact same results.


And so , um , I  I ran a couple of experiments where I  reduced that to l to be three , two , two ,  uh , five , I think , and I got almost the exact same results.


And so , um , I  I ran a couple of experiments where I  reduced that to l to be three , two , two ,  uh , five , I think , and I got almost the exact same results.

And  but it runs much much faster. So , um , I  I think m  it only took something like , uh , three or four hours to do the full training ,


And  but it runs much much faster. So , um , I  I think m  it only took something like , uh , three or four hours to do the full training ,


And  but it runs much much faster. So , um , I  I think m  it only took something like , uh , three or four hours to do the full training ,

as opposed to wh what , sixteen hours or something like that ? I mean , it takes  you have to do an overnight basically , the way it is set up now.


as opposed to wh what , sixteen hours or something like that ? I mean , it takes  you have to do an overnight basically , the way it is set up now.


So , uh , even we don't do anything else , doing something like this could allow us to turn experiments around a lot faster.


So , uh , even we don't do anything else , doing something like this could allow us to turn experiments around a lot faster.

And then when you have your final thing , do a full one , so it 's


So , uh , even we don't do anything else , doing something like this could allow us to turn experiments around a lot faster.

And then when you have your final thing , do a full one , so it 's

And when you have your final thing , we go back to this.


And then when you have your final thing , do a full one , so it 's

And when you have your final thing , we go back to this.

So , um , and it 's a real simple change to make. I mean , it 's like one little text file you edit and change those numbers , and you don't do anything else.


And when you have your final thing , we go back to this.

So , um , and it 's a real simple change to make. I mean , it 's like one little text file you edit and change those numbers , and you don't do anything else.


So , um , and it 's a real simple change to make. I mean , it 's like one little text file you edit and change those numbers , and you don't do anything else.


So it 's a very simple change to make and it doesn't seem to hurt all that much.


So it 's a very simple change to make and it doesn't seem to hurt all that much.

So you  you run with three , two , two , five ? That 's a


So it 's a very simple change to make and it doesn't seem to hurt all that much.

So you  you run with three , two , two , five ? That 's a

So I  Uh , I  I have to look to see what the exact numbers were.


So you  you run with three , two , two , five ? That 's a

So I  Uh , I  I have to look to see what the exact numbers were.

I  I thought was , like , three , two , two , five ,


So I  Uh , I  I have to look to see what the exact numbers were.

I  I thought was , like , three , two , two , five ,


I  I thought was , like , three , two , two , five ,

but I I 'll  I 'll double check. It was  over a week ago that I did it ,


but I I 'll  I 'll double check. It was  over a week ago that I did it ,


um , but it 's so much faster. I it makes a big difference.


um , but it 's so much faster. I it makes a big difference.

So we could do a lot more experiments and throw a lot more stuff in there.


So we could do a lot more experiments and throw a lot more stuff in there.


So we could do a lot more experiments and throw a lot more stuff in there.

Um. Oh , the other thing that I did was , um ,  I compiled  the HTK stuff for the Linux boxes. So we have this big thing that we got from IBM , which is a five - processor machine. Really fast , but it 's running Linux. So , you can now run your experiments on that machine and you can run five at a time and it runs ,  uh , as fast as , you know , uh , five different machines.


Um. Oh , the other thing that I did was , um ,  I compiled  the HTK stuff for the Linux boxes. So we have this big thing that we got from IBM , which is a five - processor machine. Really fast , but it 's running Linux. So , you can now run your experiments on that machine and you can run five at a time and it runs ,  uh , as fast as , you know , uh , five different machines.


Um. Oh , the other thing that I did was , um ,  I compiled  the HTK stuff for the Linux boxes. So we have this big thing that we got from IBM , which is a five - processor machine. Really fast , but it 's running Linux. So , you can now run your experiments on that machine and you can run five at a time and it runs ,  uh , as fast as , you know , uh , five different machines.

So , um , I 've forgotten now what the name of that machine is but I can  I can send email around about it.


So , um , I 've forgotten now what the name of that machine is but I can  I can send email around about it.


So , um , I 've forgotten now what the name of that machine is but I can  I can send email around about it.

And so we 've got it  now HTK 's compiled for both the Linux and for , um , the Sparcs. Um , you have to make  you have to make sure that in your dot CSHRC ,  um , it detects whether you 're running on the Linux or a  a Sparc and points to the right executables. Uh , and you may not have had that in your dot CSHRC before , if you were always just running the Sparc. So , um ,


And so we 've got it  now HTK 's compiled for both the Linux and for , um , the Sparcs. Um , you have to make  you have to make sure that in your dot CSHRC ,  um , it detects whether you 're running on the Linux or a  a Sparc and points to the right executables. Uh , and you may not have had that in your dot CSHRC before , if you were always just running the Sparc. So , um ,

uh , I can  I can tell you exactly what you need to do to get all of that to work. But it 'll  it really increases what we can run on.


uh , I can  I can tell you exactly what you need to do to get all of that to work. But it 'll  it really increases what we can run on.

So ,  together with the fact that we 've got these  faster Linux boxes and that it takes less time to do  these , um , we should be able to crank through a lot more experiments.


So ,  together with the fact that we 've got these  faster Linux boxes and that it takes less time to do  these , um , we should be able to crank through a lot more experiments.


So after I did that , then what I wanted to do  was try  increasing the number of mixtures , just to see , um  see how  how that affects performance.


So after I did that , then what I wanted to do  was try  increasing the number of mixtures , just to see , um  see how  how that affects performance.

Yeah. In fact , you could do something like  keep exactly the same procedure and then add a fifth thing onto it


Yeah. In fact , you could do something like  keep exactly the same procedure and then add a fifth thing onto it


So at  at the middle o where the arrows are showing , that 's  you 're adding one more mixture per state ,


So at  at the middle o where the arrows are showing , that 's  you 're adding one more mixture per state ,

let 's see , uh. It goes from this  uh , try to go it backwards  this  at this point it 's two mixtures  per state. So this just adds one. Except that , uh , actually for the silence model , it 's six mixtures per state.


let 's see , uh. It goes from this  uh , try to go it backwards  this  at this point it 's two mixtures  per state. So this just adds one. Except that , uh , actually for the silence model , it 's six mixtures per state.


Might be between , uh , shared , uh  shared variances or something ,


Might be between , uh , shared , uh  shared variances or something ,

Uh , yeah. It 's , uh  Shoot. I  I  I can't remember now what happens at that first one. Uh , I have to look it up and see.


Uh , yeah. It 's , uh  Shoot. I  I  I can't remember now what happens at that first one. Uh , I have to look it up and see.


Uh , yeah. It 's , uh  Shoot. I  I  I can't remember now what happens at that first one. Uh , I have to look it up and see.

Um , there  because they start off with , uh , an initial model which is just this global model , and then they split it to the individuals. And so ,  it may be that that 's what 's happening here. I  I   I have to look it up and see. I  I don't exactly remember.


Um , there  because they start off with , uh , an initial model which is just this global model , and then they split it to the individuals. And so ,  it may be that that 's what 's happening here. I  I   I have to look it up and see. I  I don't exactly remember.


Um. Yeah. There was a conference call this Tuesday. Um. I don't know yet the   what happened  Tuesday , but  the points that they were supposed to discuss is still ,  uh , things like  the weights , uh


Um. Yeah. There was a conference call this Tuesday. Um. I don't know yet the   what happened  Tuesday , but  the points that they were supposed to discuss is still ,  uh , things like  the weights , uh

Oh , this is a conference call for , uh , uh , Aurora participant sort of thing.


Oh , this is a conference call for , uh , uh , Aurora participant sort of thing.


Do you know who was  who was  since we weren't in on it , uh , do you know who was in from OGI ? Was   was  was Hynek involved or was it Sunil


Um , yeah. So the points were the  the weights  how to weight the different error rates  that are obtained from different language and  and conditions. Um , it 's not clear that they will keep the same kind of weighting. Right now it 's a weighting on  on improvement.


Um , yeah. So the points were the  the weights  how to weight the different error rates  that are obtained from different language and  and conditions. Um , it 's not clear that they will keep the same kind of weighting. Right now it 's a weighting on  on improvement.

Some people are arguing that it would be better to have weights on uh  well , to  to combine error rates  before computing improvement. Uh , and the fact is that for  right now for  the English , they have weights  they  they combine error rates , but for the other languages they combine improvement. So it 's not very consistent. Um


Some people are arguing that it would be better to have weights on uh  well , to  to combine error rates  before computing improvement. Uh , and the fact is that for  right now for  the English , they have weights  they  they combine error rates , but for the other languages they combine improvement. So it 's not very consistent. Um

Yeah. The , um  Yeah. And so  Well ,  this is a point. And right now actually there is a thing also ,  uh , that happens with the current weight is that a very non - significant improvement  on the well - matched case result in  huge differences in   in the final number.


Yeah. The , um  Yeah. And so  Well ,  this is a point. And right now actually there is a thing also ,  uh , that happens with the current weight is that a very non - significant improvement  on the well - matched case result in  huge differences in   in the final number.

And so , perhaps they will change the weights to


And so , perhaps they will change the weights to


And so , perhaps they will change the weights to

How should that be done ? I mean , it  it seems like there 's a simple way


How should that be done ? I mean , it  it seems like there 's a simple way


How should that be done ? I mean , it  it seems like there 's a simple way

Uh , this seems like an obvious mistake or something.


How should that be done ? I mean , it  it seems like there 's a simple way

Uh , this seems like an obvious mistake or something.

Well , I mean , the fact that it 's inconsistent is an obvious mistake.


Uh , this seems like an obvious mistake or something.

Well , I mean , the fact that it 's inconsistent is an obvious mistake.


Well , I mean , the fact that it 's inconsistent is an obvious mistake.


I don't know I haven't thought it through , but one  one would think that  each  It  it 's like if you say what 's the  what 's the best way to do an average , an arithmetic average or a geometric average ?


Each  each one is gonna have a different characteristic.


Each  each one is gonna have a different characteristic.

Well , it seems like they should do , like , the percentage improvement or something , rather than the  absolute improvement.


Well , it seems like they should do , like , the percentage improvement or something , rather than the  absolute improvement.


No , that is relative. But the question is , do you average the relative improvements  or do you average the error rates and take the relative improvement maybe of that ?


No , that is relative. But the question is , do you average the relative improvements  or do you average the error rates and take the relative improvement maybe of that ?

And the thing is it 's not just a pure average because there are these weightings.


And the thing is it 's not just a pure average because there are these weightings.


And the thing is it 's not just a pure average because there are these weightings.

Yeah. And so when you average the  the relative improvement it tends to   to give a lot of  of , um ,  importance to the well - matched case because  the baseline is already very good and , um , i it 's


Yeah. And so when you average the  the relative improvement it tends to   to give a lot of  of , um ,  importance to the well - matched case because  the baseline is already very good and , um , i it 's

Why don't they not look at improvements but just look at your av your scores ? You know , figure out how to combine the scores


Yeah. And so when you average the  the relative improvement it tends to   to give a lot of  of , um ,  importance to the well - matched case because  the baseline is already very good and , um , i it 's

Why don't they not look at improvements but just look at your av your scores ? You know , figure out how to combine the scores

with a weight or whatever , and then give you a score  here 's your score. And then they can do the same thing for the baseline system  and here 's its score. And then you can look at


Why don't they not look at improvements but just look at your av your scores ? You know , figure out how to combine the scores

with a weight or whatever , and then give you a score  here 's your score. And then they can do the same thing for the baseline system  and here 's its score. And then you can look at


with a weight or whatever , and then give you a score  here 's your score. And then they can do the same thing for the baseline system  and here 's its score. And then you can look at

Well , that 's what he 's seeing as one of the things they could do.


Well , that 's what he 's seeing as one of the things they could do.

It 's just when you  when you get all done , I think that they pro I m I  I wasn't there but I think they started off this process with the notion that  you should be  significantly better than the previous standard.


It 's just when you  when you get all done , I think that they pro I m I  I wasn't there but I think they started off this process with the notion that  you should be  significantly better than the previous standard.

And , um , so they said " how much is significantly better ? what do you  ? " And  and so they said " well ,  you know , you should have half the errors , " or something , " that you had before ".


And , um , so they said " how much is significantly better ? what do you  ? " And  and so they said " well ,  you know , you should have half the errors , " or something , " that you had before ".


i i it does seem like it 's more logical to combine them first and then do the


But there is this  this  is this still this problem of weights. When  when you combine error rate it tends to  give more importance to the difficult cases , and some people think that


But there is this  this  is this still this problem of weights. When  when you combine error rate it tends to  give more importance to the difficult cases , and some people think that

well , they have different ,  um , opinions about this. Some people think that  it 's more important to look at   to have ten percent imp relative improvement on  well - matched case than to have fifty percent on the m mismatched , and other people think that it 's more important to improve a lot on the mismatch and  So , bu


But there is this  this  is this still this problem of weights. When  when you combine error rate it tends to  give more importance to the difficult cases , and some people think that

well , they have different ,  um , opinions about this. Some people think that  it 's more important to look at   to have ten percent imp relative improvement on  well - matched case than to have fifty percent on the m mismatched , and other people think that it 's more important to improve a lot on the mismatch and  So , bu

It sounds like they don't really have a good idea about what the final application is gonna be.


well , they have different ,  um , opinions about this. Some people think that  it 's more important to look at   to have ten percent imp relative improvement on  well - matched case than to have fifty percent on the m mismatched , and other people think that it 's more important to improve a lot on the mismatch and  So , bu

It sounds like they don't really have a good idea about what the final application is gonna be.


well , they have different ,  um , opinions about this. Some people think that  it 's more important to look at   to have ten percent imp relative improvement on  well - matched case than to have fifty percent on the m mismatched , and other people think that it 's more important to improve a lot on the mismatch and  So , bu

It sounds like they don't really have a good idea about what the final application is gonna be.

Well , you know , the  the thing is  that if you look at the numbers on the  on the more difficult cases ,  um , if you really believe that was gonna be the predominant use ,  none of this would be good enough.


It sounds like they don't really have a good idea about what the final application is gonna be.

Well , you know , the  the thing is  that if you look at the numbers on the  on the more difficult cases ,  um , if you really believe that was gonna be the predominant use ,  none of this would be good enough.


Well , you know , the  the thing is  that if you look at the numbers on the  on the more difficult cases ,  um , if you really believe that was gonna be the predominant use ,  none of this would be good enough.


whereas  you sort of with some reasonable error recovery could imagine in the better cases that these  these systems working. So , um , I think the hope would be that it would   uh , it would work well  for the good cases and , uh , it would have reasonable  reas  soft degradation as you got to worse and worse conditions. Um.


whereas  you sort of with some reasonable error recovery could imagine in the better cases that these  these systems working. So , um , I think the hope would be that it would   uh , it would work well  for the good cases and , uh , it would have reasonable  reas  soft degradation as you got to worse and worse conditions. Um.

Yeah. I  I guess what I 'm  I mean , I  I was thinking about it in terms of , if I were building the final product and I was gonna test to see which front - end I 'd   I wanted to use , I would  try to  weight things depending on the exact environment that I was gonna be using the system in.


Yeah. I  I guess what I 'm  I mean , I  I was thinking about it in terms of , if I were building the final product and I was gonna test to see which front - end I 'd   I wanted to use , I would  try to  weight things depending on the exact environment that I was gonna be using the system in.

Well , no  well , no. I mean ,  it isn't the operating theater. I mean , they don they  they don't  they don't really  know , I think.


Well , no  well , no. I mean ,  it isn't the operating theater. I mean , they don they  they don't  they don't really  know , I think.


Well , no  well , no. I mean ,  it isn't the operating theater. I mean , they don they  they don't  they don't really  know , I think.

So if  if they don't know , doesn't that suggest the way for them to go ? Uh , you assume everything 's equal. I mean , y y I mean , you


So if  if they don't know , doesn't that suggest the way for them to go ? Uh , you assume everything 's equal. I mean , y y I mean , you

Well , I mean , I  I think one thing to do is to just not rely on a single number  to maybe have two or three numbers ,


Well , I mean , I  I think one thing to do is to just not rely on a single number  to maybe have two or three numbers ,


and  and  and say  here 's how much you , uh  you improve  the , uh  the  the relatively clean case and here 's  or  or well - matched case , and here 's how  here 's how much you ,


Uh , I had forgotten this , uh , but , uh , well - matched is not actually clean. What it is is just that , u uh , the training and testing are similar.


Uh , I had forgotten this , uh , but , uh , well - matched is not actually clean. What it is is just that , u uh , the training and testing are similar.

So , I guess what you would do in practice is you 'd try to get as many ,  uh , examples of similar sort of stuff as you could , and then ,


So , I guess what you would do in practice is you 'd try to get as many ,  uh , examples of similar sort of stuff as you could , and then ,


So , I guess what you would do in practice is you 'd try to get as many ,  uh , examples of similar sort of stuff as you could , and then ,

uh  So the argument for that being the  the  the more important thing ,  is that you 're gonna try and do that ,  but you wanna see how badly it deviates from that when  when  when the , uh  it 's a little different.


uh  So the argument for that being the  the  the more important thing ,  is that you 're gonna try and do that ,  but you wanna see how badly it deviates from that when  when  when the , uh  it 's a little different.


uh  So the argument for that being the  the  the more important thing ,  is that you 're gonna try and do that ,  but you wanna see how badly it deviates from that when  when  when the , uh  it 's a little different.

so you should weight those other conditions v very  you know , really small.


so you should weight those other conditions v very  you know , really small.

But  No. That 's a  that 's a  that 's an arg


so you should weight those other conditions v very  you know , really small.

But  No. That 's a  that 's a  that 's an arg

I mean , that 's more of an information kind of thing.


so you should weight those other conditions v very  you know , really small.

But  No. That 's a  that 's a  that 's an arg

I mean , that 's more of an information kind of thing.

that 's an ar Well , that 's an argument for it , but let me give you the opposite argument. The opposite argument is you 're never really gonna have a good sample of all these different things.


But  No. That 's a  that 's a  that 's an arg

I mean , that 's more of an information kind of thing.

that 's an ar Well , that 's an argument for it , but let me give you the opposite argument. The opposite argument is you 're never really gonna have a good sample of all these different things.


I mean , that 's more of an information kind of thing.

that 's an ar Well , that 's an argument for it , but let me give you the opposite argument. The opposite argument is you 're never really gonna have a good sample of all these different things.

I mean , are you gonna have w uh , uh , examples with the windows open , half open , full open ? Going seventy , sixty , fifty , forty miles an hour ? On what kind of roads ?


that 's an ar Well , that 's an argument for it , but let me give you the opposite argument. The opposite argument is you 're never really gonna have a good sample of all these different things.

I mean , are you gonna have w uh , uh , examples with the windows open , half open , full open ? Going seventy , sixty , fifty , forty miles an hour ? On what kind of roads ?


I mean , are you gonna have w uh , uh , examples with the windows open , half open , full open ? Going seventy , sixty , fifty , forty miles an hour ? On what kind of roads ?

With what passing you ? With  uh , I mean ,


With what passing you ? With  uh , I mean ,

I  I  I think that you could make the opposite argument that the well - matched case is a fantasy.


I  I  I think that you could make the opposite argument that the well - matched case is a fantasy.


I think the thing is is that if you look at the well - matched case versus the po you know , the  the medium and the  and the fo and then the mismatched case ,  um , we 're seeing really , really big differences in performance. Right ? And  and y you wouldn't like that to be the case. You wouldn't like that as soon as you step outside  You know , a lot of the  the cases it 's  is


I think the thing is is that if you look at the well - matched case versus the po you know , the  the medium and the  and the fo and then the mismatched case ,  um , we 're seeing really , really big differences in performance. Right ? And  and y you wouldn't like that to be the case. You wouldn't like that as soon as you step outside  You know , a lot of the  the cases it 's  is

Well , that 'll teach them to roll their window up.


I think the thing is is that if you look at the well - matched case versus the po you know , the  the medium and the  and the fo and then the mismatched case ,  um , we 're seeing really , really big differences in performance. Right ? And  and y you wouldn't like that to be the case. You wouldn't like that as soon as you step outside  You know , a lot of the  the cases it 's  is

Well , that 'll teach them to roll their window up.

I mean , in these cases , if you go from the  the , uh  I mean , I don't remember the numbers right off , but if you  if you go from the well - matched case to the medium ,  it 's not an enormous difference in the  in the  the training - testing situation , and  and  and it 's a really big  performance drop.


Well , that 'll teach them to roll their window up.

I mean , in these cases , if you go from the  the , uh  I mean , I don't remember the numbers right off , but if you  if you go from the well - matched case to the medium ,  it 's not an enormous difference in the  in the  the training - testing situation , and  and  and it 's a really big  performance drop.

You know , so , um  Yeah , I mean the reference one , for instance  this is back old on , uh  on Italian  uh , was like  six percent error for the well - matched and eighteen for the medium - matched and sixty for the   for highly - mismatched. Uh , and , you know , with these other systems we  we  helped it out quite a bit , but still there 's  there 's something like a factor of two or something between well - matched and medium - matched. And  so I think that  if what you 're   if the goal of this is to come up with robust features , it does mean  So you could argue , in fact , that the well - matched is something you shouldn't be looking at at all , that  that the goal is to come up with features  that will still give you reasonable performance , you know , with again gentle degregra degradation , um , even though the  the testing condition is not the same as the training.


I mean , in these cases , if you go from the  the , uh  I mean , I don't remember the numbers right off , but if you  if you go from the well - matched case to the medium ,  it 's not an enormous difference in the  in the  the training - testing situation , and  and  and it 's a really big  performance drop.

You know , so , um  Yeah , I mean the reference one , for instance  this is back old on , uh  on Italian  uh , was like  six percent error for the well - matched and eighteen for the medium - matched and sixty for the   for highly - mismatched. Uh , and , you know , with these other systems we  we  helped it out quite a bit , but still there 's  there 's something like a factor of two or something between well - matched and medium - matched. And  so I think that  if what you 're   if the goal of this is to come up with robust features , it does mean  So you could argue , in fact , that the well - matched is something you shouldn't be looking at at all , that  that the goal is to come up with features  that will still give you reasonable performance , you know , with again gentle degregra degradation , um , even though the  the testing condition is not the same as the training.


You know , so , um  Yeah , I mean the reference one , for instance  this is back old on , uh  on Italian  uh , was like  six percent error for the well - matched and eighteen for the medium - matched and sixty for the   for highly - mismatched. Uh , and , you know , with these other systems we  we  helped it out quite a bit , but still there 's  there 's something like a factor of two or something between well - matched and medium - matched. And  so I think that  if what you 're   if the goal of this is to come up with robust features , it does mean  So you could argue , in fact , that the well - matched is something you shouldn't be looking at at all , that  that the goal is to come up with features  that will still give you reasonable performance , you know , with again gentle degregra degradation , um , even though the  the testing condition is not the same as the training.

So , you know , I  I could argue strongly that something like the medium mismatch , which is you know not compl pathological but  I mean , what was the  the medium - mismatch condition again ?


You know , so , um  Yeah , I mean the reference one , for instance  this is back old on , uh  on Italian  uh , was like  six percent error for the well - matched and eighteen for the medium - matched and sixty for the   for highly - mismatched. Uh , and , you know , with these other systems we  we  helped it out quite a bit , but still there 's  there 's something like a factor of two or something between well - matched and medium - matched. And  so I think that  if what you 're   if the goal of this is to come up with robust features , it does mean  So you could argue , in fact , that the well - matched is something you shouldn't be looking at at all , that  that the goal is to come up with features  that will still give you reasonable performance , you know , with again gentle degregra degradation , um , even though the  the testing condition is not the same as the training.

So , you know , I  I could argue strongly that something like the medium mismatch , which is you know not compl pathological but  I mean , what was the  the medium - mismatch condition again ?

Um ,  it 's  Yeah. Medium mismatch is everything with the far  microphone , but trained on , like , low noisy condition , like low speed and  or  stopped car and tested on  high - speed conditions , I think , like on a highway and


So , you know , I  I could argue strongly that something like the medium mismatch , which is you know not compl pathological but  I mean , what was the  the medium - mismatch condition again ?

Um ,  it 's  Yeah. Medium mismatch is everything with the far  microphone , but trained on , like , low noisy condition , like low speed and  or  stopped car and tested on  high - speed conditions , I think , like on a highway and


Um ,  it 's  Yeah. Medium mismatch is everything with the far  microphone , but trained on , like , low noisy condition , like low speed and  or  stopped car and tested on  high - speed conditions , I think , like on a highway and

So it 's still the same  same microphone in both cases ,


So it 's still the same  same microphone in both cases ,


So it 's still the same  same microphone in both cases ,

but , uh , it 's  there 's a mismatch between the car conditions. And that 's  uh , you could argue that 's a pretty realistic situation


but , uh , it 's  there 's a mismatch between the car conditions. And that 's  uh , you could argue that 's a pretty realistic situation


but , uh , it 's  there 's a mismatch between the car conditions. And that 's  uh , you could argue that 's a pretty realistic situation

and , uh , I 'd almost argue for weighting that highest. But the way they have it now ,  it 's  I guess it 's  it 's  They  they compute the relative improvement first and then average that with a weighting ?


and , uh , I 'd almost argue for weighting that highest. But the way they have it now ,  it 's  I guess it 's  it 's  They  they compute the relative improvement first and then average that with a weighting ?


and , uh , I 'd almost argue for weighting that highest. But the way they have it now ,  it 's  I guess it 's  it 's  They  they compute the relative improvement first and then average that with a weighting ?

And so then the  that  that makes the highly - matched the really big thing.


And so then the  that  that makes the highly - matched the really big thing.

Um , so , u i since they have these three categories , it seems like the reasonable thing to do  is to go across the languages  and to come up with an improvement for each of those.


Um , so , u i since they have these three categories , it seems like the reasonable thing to do  is to go across the languages  and to come up with an improvement for each of those.

Just say " OK , in the  in the highly - matched case this is what happens , in the   m the , uh  this other m medium if this happens , in the highly - mismatched  that happens ".


Just say " OK , in the  in the highly - matched case this is what happens , in the   m the , uh  this other m medium if this happens , in the highly - mismatched  that happens ".

And , uh , you should see , uh , a gentle degradation  through that.


And , uh , you should see , uh , a gentle degradation  through that.


I think that  that  I  I  I gather that in these meetings it 's  it 's really tricky to make anything  ac  make any  policy change because   everybody has  has , uh , their own opinion


Uh , so  Yeah. Yeah , but there is probably a  a big change that will  be made is that the  the baseline  th they want to have a new baseline , perhaps , which is , um , MFCC but with  a voice activity detector. And apparently ,  uh , some people are pushing to still keep this fifty percent number. So they want  to have at least fifty percent improvement on the baseline , but w which would be a much better baseline.


Uh , so  Yeah. Yeah , but there is probably a  a big change that will  be made is that the  the baseline  th they want to have a new baseline , perhaps , which is , um , MFCC but with  a voice activity detector. And apparently ,  uh , some people are pushing to still keep this fifty percent number. So they want  to have at least fifty percent improvement on the baseline , but w which would be a much better baseline.

And if we look at the result that Sunil sent ,  just putting the VAD in the baseline improved , like , more than twenty percent ,


And if we look at the result that Sunil sent ,  just putting the VAD in the baseline improved , like , more than twenty percent ,

which would mean then  then  mean that fifty percent on this new baseline is like , well , more than sixty percent improvement on  on  o e e uh


And if we look at the result that Sunil sent ,  just putting the VAD in the baseline improved , like , more than twenty percent ,

which would mean then  then  mean that fifty percent on this new baseline is like , well , more than sixty percent improvement on  on  o e e uh

So nobody would  be there , probably. Right ?


which would mean then  then  mean that fifty percent on this new baseline is like , well , more than sixty percent improvement on  on  o e e uh

So nobody would  be there , probably. Right ?

Right now , nobody would be there , but  Yeah.


So nobody would  be there , probably. Right ?

Right now , nobody would be there , but  Yeah.


Right now , nobody would be there , but  Yeah.


Uh , they didn't decide yet. I guess i this was one point of the conference call also , but  mmm , so I don't know. Um , but  Yeah.


Uh , they didn't decide yet. I guess i this was one point of the conference call also , but  mmm , so I don't know. Um , but  Yeah.

Oh , I  I think th that would be  good. I mean , it 's not that the design of the VAD isn't important , but it 's just that it  it  it does seem to be i uh , a lot of  work to do a good job on  on that and as well as being a lot of work to do a good job on the feature  design ,


Oh , I  I think th that would be  good. I mean , it 's not that the design of the VAD isn't important , but it 's just that it  it  it does seem to be i uh , a lot of  work to do a good job on  on that and as well as being a lot of work to do a good job on the feature  design ,


if we can  cut down on that maybe we can make some progress.


if we can  cut down on that maybe we can make some progress.

But I guess perhaps  I don't know w  Yeah. Uh , yeah. Per - e s s someone told that perhaps it 's not fair to do that because the , um  to make a good VAD  you don't have enough to  with the  the features that are  the baseline features. So  mmm , you need more features. So you really need to put more  more in the  in  in the front - end.


But I guess perhaps  I don't know w  Yeah. Uh , yeah. Per - e s s someone told that perhaps it 's not fair to do that because the , um  to make a good VAD  you don't have enough to  with the  the features that are  the baseline features. So  mmm , you need more features. So you really need to put more  more in the  in  in the front - end.


So y so you m s Yeah , but  Well , let 's say for ins see , MFCC for instance doesn't have anything in it , uh , related to the pitch. So just  just for example. So suppose you 've  that  what you really wanna do is put a good pitch detector on there and if it gets an unambiguous


So y so you m s Yeah , but  Well , let 's say for ins see , MFCC for instance doesn't have anything in it , uh , related to the pitch. So just  just for example. So suppose you 've  that  what you really wanna do is put a good pitch detector on there and if it gets an unambiguous

if it gets an unambiguous result then you 're definitely in a  in a  in a voice in a , uh , s region with speech. Uh.


if it gets an unambiguous result then you 're definitely in a  in a  in a voice in a , uh , s region with speech. Uh.

So there 's this assumption that the v the voice activity detector can only use the MFCC ?


So there 's this assumption that the v the voice activity detector can only use the MFCC ?


So  so if you use other features then y But it 's just a question of what is your baseline. Right ? What is it that you 're supposed to do better than ?


So  so if you use other features then y But it 's just a question of what is your baseline. Right ? What is it that you 're supposed to do better than ?

And so having the baseline be the MFCC 's  means that people could  choose to pour their ener their effort into trying to do a really good VAD


So  so if you use other features then y But it 's just a question of what is your baseline. Right ? What is it that you 're supposed to do better than ?

And so having the baseline be the MFCC 's  means that people could  choose to pour their ener their effort into trying to do a really good VAD

I don't s But they seem like two  separate issues.


And so having the baseline be the MFCC 's  means that people could  choose to pour their ener their effort into trying to do a really good VAD

I don't s But they seem like two  separate issues.


I don't s But they seem like two  separate issues.

Unfortunately there 's coupling between them , which is part of what I think Stephane is getting to , is that  you can choose your features in such a way as to improve the VAD.


Unfortunately there 's coupling between them , which is part of what I think Stephane is getting to , is that  you can choose your features in such a way as to improve the VAD.


Unfortunately there 's coupling between them , which is part of what I think Stephane is getting to , is that  you can choose your features in such a way as to improve the VAD.

And you also can choose your features in such a way as to prove  improve recognition. They may not be the same thing.


And you also can choose your features in such a way as to prove  improve recognition. They may not be the same thing.


and  and I  I think that this still makes  I still think this makes sense as a baseline. It 's just saying , as a baseline , we know


and  and I  I think that this still makes  I still think this makes sense as a baseline. It 's just saying , as a baseline , we know

you know , we had the MFCC 's before , lots of people have done voice activity detectors ,


you know , we had the MFCC 's before , lots of people have done voice activity detectors ,

you might as well pick some voice activity detector and make that the baseline , just like you picked some version of HTK and made that the baseline.


you might as well pick some voice activity detector and make that the baseline , just like you picked some version of HTK and made that the baseline.

And then  let 's try and make everything better. Um , and if one of the ways you make it better is by having your features  be better features for the VAD then that 's  so be it.


And then  let 's try and make everything better. Um , and if one of the ways you make it better is by having your features  be better features for the VAD then that 's  so be it.

But , uh , uh , uh , at least you have a starting point that 's  um , cuz i i some of  the some of the people didn't have a VAD at all , I guess. Right ? And  and


But , uh , uh , uh , at least you have a starting point that 's  um , cuz i i some of  the some of the people didn't have a VAD at all , I guess. Right ? And  and

then they  they looked pretty bad and  and in fact what they were doing wasn't so bad at all.


then they  they looked pretty bad and  and in fact what they were doing wasn't so bad at all.


then they  they looked pretty bad and  and in fact what they were doing wasn't so bad at all.

Yeah. It seems like you should try to make your baseline as good as possible. And if it turns out that  you can't improve on that , well , I mean , then , you know , nobody wins and you just use MFCC. Right ?


Yeah. It seems like you should try to make your baseline as good as possible. And if it turns out that  you can't improve on that , well , I mean , then , you know , nobody wins and you just use MFCC. Right ?

Yeah. I mean , it seems like , uh , it should include sort of the current state of the art  that you want  are trying to improve , and MFCC 's , you know , or PLP or something  it seems like  reasonable baseline for the features , and anybody doing this task ,  uh , is gonna have some sort of voice activity detection at some level , in some way. They might use the whole recognizer to do it  but  rather than  a separate thing , but   but they 'll have it on some level. So , um.


Yeah. It seems like you should try to make your baseline as good as possible. And if it turns out that  you can't improve on that , well , I mean , then , you know , nobody wins and you just use MFCC. Right ?

Yeah. I mean , it seems like , uh , it should include sort of the current state of the art  that you want  are trying to improve , and MFCC 's , you know , or PLP or something  it seems like  reasonable baseline for the features , and anybody doing this task ,  uh , is gonna have some sort of voice activity detection at some level , in some way. They might use the whole recognizer to do it  but  rather than  a separate thing , but   but they 'll have it on some level. So , um.

It seems like whatever they choose they shouldn't ,  you know , purposefully brain - damage a part of the system to  make a worse baseline , or


Yeah. I mean , it seems like , uh , it should include sort of the current state of the art  that you want  are trying to improve , and MFCC 's , you know , or PLP or something  it seems like  reasonable baseline for the features , and anybody doing this task ,  uh , is gonna have some sort of voice activity detection at some level , in some way. They might use the whole recognizer to do it  but  rather than  a separate thing , but   but they 'll have it on some level. So , um.

It seems like whatever they choose they shouldn't ,  you know , purposefully brain - damage a part of the system to  make a worse baseline , or


It seems like whatever they choose they shouldn't ,  you know , purposefully brain - damage a part of the system to  make a worse baseline , or

it wasn't that they purposely brain - damaged it. I think people hadn't really thought through  about the , uh  the VAD issue.


it wasn't that they purposely brain - damaged it. I think people hadn't really thought through  about the , uh  the VAD issue.


it wasn't that they purposely brain - damaged it. I think people hadn't really thought through  about the , uh  the VAD issue.

And  and then when the  the  the proposals actually came in and half of them had V A Ds and half of them didn't , and the half that did did well and the  half that didn't did poorly.


And  and then when the  the  the proposals actually came in and half of them had V A Ds and half of them didn't , and the half that did did well and the  half that didn't did poorly.


Yeah. So we 'll see what happen with this. And  Yeah. So what happened since , um ,  last week is  well , from OGI , these experiments on  putting VAD on the baseline. And these experiments also are using , uh , some kind of noise compensation , so spectral subtraction , and putting on - line normalization , um , just after this. So I think spectral subtraction , LDA filtering , and on - line normalization , so which is similar to  the pro proposal - one , but with  spectral subtraction in addition , and it seems that on - line normalization doesn't help further when you have spectral subtraction.


Yeah. So we 'll see what happen with this. And  Yeah. So what happened since , um ,  last week is  well , from OGI , these experiments on  putting VAD on the baseline. And these experiments also are using , uh , some kind of noise compensation , so spectral subtraction , and putting on - line normalization , um , just after this. So I think spectral subtraction , LDA filtering , and on - line normalization , so which is similar to  the pro proposal - one , but with  spectral subtraction in addition , and it seems that on - line normalization doesn't help further when you have spectral subtraction.

Is this related to the issue that you brought up a couple of meetings ago with the  the  musical tones


Yeah. So we 'll see what happen with this. And  Yeah. So what happened since , um ,  last week is  well , from OGI , these experiments on  putting VAD on the baseline. And these experiments also are using , uh , some kind of noise compensation , so spectral subtraction , and putting on - line normalization , um , just after this. So I think spectral subtraction , LDA filtering , and on - line normalization , so which is similar to  the pro proposal - one , but with  spectral subtraction in addition , and it seems that on - line normalization doesn't help further when you have spectral subtraction.

Is this related to the issue that you brought up a couple of meetings ago with the  the  musical tones

I have no idea , because the issue I brought up was with a very simple spectral subtraction approach ,


Is this related to the issue that you brought up a couple of meetings ago with the  the  musical tones

I have no idea , because the issue I brought up was with a very simple spectral subtraction approach ,


I have no idea , because the issue I brought up was with a very simple spectral subtraction approach ,

and the one that  they use at OGI is one from  from  the proposed  the  the  the Aurora prop uh , proposals , which might be much better. So , yeah. I asked  Sunil for more information about that , but , uh , I don't know yet. Um. And what 's happened here is that we  so we have this kind of new , um , reference system which  use a nice  a  a clean downsampling - upsampling , which use a new filter  that 's much shorter and which also cuts the frequency below sixty - four hertz ,


and the one that  they use at OGI is one from  from  the proposed  the  the  the Aurora prop uh , proposals , which might be much better. So , yeah. I asked  Sunil for more information about that , but , uh , I don't know yet. Um. And what 's happened here is that we  so we have this kind of new , um , reference system which  use a nice  a  a clean downsampling - upsampling , which use a new filter  that 's much shorter and which also cuts the frequency below sixty - four hertz ,

which was not done on our first proposal.


and the one that  they use at OGI is one from  from  the proposed  the  the  the Aurora prop uh , proposals , which might be much better. So , yeah. I asked  Sunil for more information about that , but , uh , I don't know yet. Um. And what 's happened here is that we  so we have this kind of new , um , reference system which  use a nice  a  a clean downsampling - upsampling , which use a new filter  that 's much shorter and which also cuts the frequency below sixty - four hertz ,

which was not done on our first proposal.

When you say " we have that " , does Sunil have it now , too ,


which was not done on our first proposal.

When you say " we have that " , does Sunil have it now , too ,


When you say " we have that " , does Sunil have it now , too ,


Because we 're still testing. So we have the result for ,  uh , just the features


Because we 're still testing. So we have the result for ,  uh , just the features

and we are currently testing with putting the neural network in the KLT. Um , it seems to improve on the well - matched case , um ,  but it 's a little bit worse on the mismatch and highly - mismatched  I mean when we put the neural network. And with the current weighting I think it 's sh it will be better because the well - matched case is better. Mmm.


Because we 're still testing. So we have the result for ,  uh , just the features

and we are currently testing with putting the neural network in the KLT. Um , it seems to improve on the well - matched case , um ,  but it 's a little bit worse on the mismatch and highly - mismatched  I mean when we put the neural network. And with the current weighting I think it 's sh it will be better because the well - matched case is better. Mmm.

But how much worse  since the weighting might change  how  how much worse is it on the other conditions , when you say it 's a little worse ?


and we are currently testing with putting the neural network in the KLT. Um , it seems to improve on the well - matched case , um ,  but it 's a little bit worse on the mismatch and highly - mismatched  I mean when we put the neural network. And with the current weighting I think it 's sh it will be better because the well - matched case is better. Mmm.

But how much worse  since the weighting might change  how  how much worse is it on the other conditions , when you say it 's a little worse ?

It 's like , uh , fff , fff    um ,     ten percent relative. Yeah.


But how much worse  since the weighting might change  how  how much worse is it on the other conditions , when you say it 's a little worse ?

It 's like , uh , fff , fff    um ,     ten percent relative. Yeah.


It 's like , uh , fff , fff    um ,     ten percent relative. Yeah.

But it has the , uh  the latencies are much shorter. That 's


But it has the , uh  the latencies are much shorter. That 's

Uh - y w when I say it 's worse , it 's not  it 's when I  I  uh , compare proposal - two to proposal - one , so , r uh , y putting neural network  compared to n not having any neural network. I mean , this new system is  is  is better ,


But it has the , uh  the latencies are much shorter. That 's

Uh - y w when I say it 's worse , it 's not  it 's when I  I  uh , compare proposal - two to proposal - one , so , r uh , y putting neural network  compared to n not having any neural network. I mean , this new system is  is  is better ,

because it has  um , this sixty - four hertz cut - off , uh , clean  downsampling , and , um  what else ? Uh , yeah , a good VAD. We put the good VAD. So. Yeah , I don't know. I  I  j uh , uh  pr


Uh - y w when I say it 's worse , it 's not  it 's when I  I  uh , compare proposal - two to proposal - one , so , r uh , y putting neural network  compared to n not having any neural network. I mean , this new system is  is  is better ,

because it has  um , this sixty - four hertz cut - off , uh , clean  downsampling , and , um  what else ? Uh , yeah , a good VAD. We put the good VAD. So. Yeah , I don't know. I  I  j uh , uh  pr

But the latencies  but you 've got the latency shorter now.


because it has  um , this sixty - four hertz cut - off , uh , clean  downsampling , and , um  what else ? Uh , yeah , a good VAD. We put the good VAD. So. Yeah , I don't know. I  I  j uh , uh  pr

But the latencies  but you 've got the latency shorter now.


But the latencies  but you 've got the latency shorter now.


So it 's better than the system that we had before.


So it 's better than the system that we had before.

Yeah. Mainly because   of  the sixty - four hertz and the good VAD.


So it 's better than the system that we had before.

Yeah. Mainly because   of  the sixty - four hertz and the good VAD.

And then I took this system and ,  mmm , w uh , I p we put the old filters also. So we have this good system , with good VAD , with the short filter and with the long filter , and , um , with the short filter it 's not worse. So  well , is it


Yeah. Mainly because   of  the sixty - four hertz and the good VAD.

And then I took this system and ,  mmm , w uh , I p we put the old filters also. So we have this good system , with good VAD , with the short filter and with the long filter , and , um , with the short filter it 's not worse. So  well , is it


And then I took this system and ,  mmm , w uh , I p we put the old filters also. So we have this good system , with good VAD , with the short filter and with the long filter , and , um , with the short filter it 's not worse. So  well , is it


But what you 're saying is that when you do these  So let me try to understand. When  when you do these same improvements  to proposal - one ,


But what you 're saying is that when you do these  So let me try to understand. When  when you do these same improvements  to proposal - one ,

that , uh , on the  i things are somewhat better , uh , in proposal - two for the well - matched case and somewhat worse for the other two cases.


that , uh , on the  i things are somewhat better , uh , in proposal - two for the well - matched case and somewhat worse for the other two cases.

So does , uh  when you say , uh  So  The th now that these other things are in there , is it the case maybe that the additions of proposal - two over proposal - one are  less im important ?


So does , uh  when you say , uh  So  The th now that these other things are in there , is it the case maybe that the additions of proposal - two over proposal - one are  less im important ?


So does , uh  when you say , uh  So  The th now that these other things are in there , is it the case maybe that the additions of proposal - two over proposal - one are  less im important ?

Um  So , yeah. Uh. Yeah , but it 's a good thing anyway to have  shorter delay. Then we tried , um ,  to do something like proposal - two but having , um , e using also MSG features. So there is this KLT part , which use just the standard features ,


Um  So , yeah. Uh. Yeah , but it 's a good thing anyway to have  shorter delay. Then we tried , um ,  to do something like proposal - two but having , um , e using also MSG features. So there is this KLT part , which use just the standard features ,


Mmm , and it doesn't seem to help. Um , however , we just have  one result , which is the Italian mismatch , so. Uh. We have to wait for that to fill the whole table , but


Mmm , and it doesn't seem to help. Um , however , we just have  one result , which is the Italian mismatch , so. Uh. We have to wait for that to fill the whole table , but

OK. There was a  start of some effort on something related to voicing or something. Is that  ?


Mmm , and it doesn't seem to help. Um , however , we just have  one result , which is the Italian mismatch , so. Uh. We have to wait for that to fill the whole table , but

OK. There was a  start of some effort on something related to voicing or something. Is that  ?

Yeah. Um ,  yeah. So basically we try to ,   uh , find  good features that could be used for voicing detection , uh , but it 's still , uh  on the , um  t


OK. There was a  start of some effort on something related to voicing or something. Is that  ?

Yeah. Um ,  yeah. So basically we try to ,   uh , find  good features that could be used for voicing detection , uh , but it 's still , uh  on the , um  t

we  w basically we are still playing with Matlab to   to look at  at what happened ,


Yeah. Um ,  yeah. So basically we try to ,   uh , find  good features that could be used for voicing detection , uh , but it 's still , uh  on the , um  t

we  w basically we are still playing with Matlab to   to look at  at what happened ,


we  w basically we are still playing with Matlab to   to look at  at what happened ,


what sorts of features are you looking at ?


what sorts of features are you looking at ?

So we would be looking at , um , the  variance of the spectrum of the excitation ,


So we would be looking at , um , the  variance of the spectrum of the excitation ,

something like this , which is  should be high for voiced sounds. Uh , we


So we would be looking at , um , the  variance of the spectrum of the excitation ,

something like this , which is  should be high for voiced sounds. Uh , we

Wait a minute. I  what does that mean ? The variance of the spectrum of excitation.


something like this , which is  should be high for voiced sounds. Uh , we

Wait a minute. I  what does that mean ? The variance of the spectrum of excitation.

Yeah. So the  So basically the spectrum of the excitation  for a purely periodic sig signal shou sh


something like this , which is  should be high for voiced sounds. Uh , we

Wait a minute. I  what does that mean ? The variance of the spectrum of excitation.

Yeah. So the  So basically the spectrum of the excitation  for a purely periodic sig signal shou sh

OK. Yeah , w what yo what you 're calling the excitation , as I recall , is you 're subtracting the  the , um  the mel  mel   mel filter , uh , spectrum from the FFT spectrum.


Wait a minute. I  what does that mean ? The variance of the spectrum of excitation.

Yeah. So the  So basically the spectrum of the excitation  for a purely periodic sig signal shou sh

OK. Yeah , w what yo what you 're calling the excitation , as I recall , is you 're subtracting the  the , um  the mel  mel   mel filter , uh , spectrum from the FFT spectrum.


Yeah. So the  So basically the spectrum of the excitation  for a purely periodic sig signal shou sh

OK. Yeah , w what yo what you 're calling the excitation , as I recall , is you 're subtracting the  the , um  the mel  mel   mel filter , uh , spectrum from the FFT spectrum.


OK. Yeah , w what yo what you 're calling the excitation , as I recall , is you 're subtracting the  the , um  the mel  mel   mel filter , uh , spectrum from the FFT spectrum.


So we have the mel f filter bank , we have the FFT , so we  just


So we have the mel f filter bank , we have the FFT , so we  just

So it 's  it 's not really an excitation ,


So we have the mel f filter bank , we have the FFT , so we  just

So it 's  it 's not really an excitation ,

but it 's something that hopefully tells you something about the excitation.


So it 's  it 's not really an excitation ,

but it 's something that hopefully tells you something about the excitation.


but it 's something that hopefully tells you something about the excitation.


but it 's  it 's still  Yeah. So , well , for unvoiced portion we have something tha  that has a mean around O point three , and for voiced portion the mean is O point fifty - nine. But the variance seem quite  high.


but it 's  it 's still  Yeah. So , well , for unvoiced portion we have something tha  that has a mean around O point three , and for voiced portion the mean is O point fifty - nine. But the variance seem quite  high.

How did you get your  voiced and unvoiced truth data ?


How did you get your  voiced and unvoiced truth data ?

We used , uh , TIMIT and we used canonical mappings between the phones


We used , uh , TIMIT and we used canonical mappings between the phones


But if we look at it in one sentence , it  apparently it 's good , I think.


But if we look at it in one sentence , it  apparently it 's good , I think.

Yeah , but  Yeah. Uh , so it 's noisy TIMIT. That 's right. Yeah.


Yeah , but  Yeah. Uh , so it 's noisy TIMIT. That 's right. Yeah.

It seems quite robust to noise , so when we take  we draw its parameters across time for a clean sentence and then nois the same noisy sentence , it 's very close.


It seems quite robust to noise , so when we take  we draw its parameters across time for a clean sentence and then nois the same noisy sentence , it 's very close.


It seems quite robust to noise , so when we take  we draw its parameters across time for a clean sentence and then nois the same noisy sentence , it 's very close.

Yeah. So there are  there is this. There could be also the , um   something like the maximum of the auto - correlation function or  which


It seems quite robust to noise , so when we take  we draw its parameters across time for a clean sentence and then nois the same noisy sentence , it 's very close.

Yeah. So there are  there is this. There could be also the , um   something like the maximum of the auto - correlation function or  which

Is this a  a s a trained system ? Or is it a system where you just pick some thresholds ? Ho - how does it work ?


Yeah. So there are  there is this. There could be also the , um   something like the maximum of the auto - correlation function or  which

Is this a  a s a trained system ? Or is it a system where you just pick some thresholds ? Ho - how does it work ?

Right now we just are trying to find some features. And ,


Is this a  a s a trained system ? Or is it a system where you just pick some thresholds ? Ho - how does it work ?

Right now we just are trying to find some features. And ,

uh  Yeah. Hopefully , I think what we want to have is to put these features in s some kind of , um  well , to  to obtain a statistical model on these features and to  or just to use a neural network and hopefully these features w would help


Right now we just are trying to find some features. And ,

uh  Yeah. Hopefully , I think what we want to have is to put these features in s some kind of , um  well , to  to obtain a statistical model on these features and to  or just to use a neural network and hopefully these features w would help

Because it seems like what you said about the mean of the  the voiced and the unvoiced    that seemed pretty encouraging.


uh  Yeah. Hopefully , I think what we want to have is to put these features in s some kind of , um  well , to  to obtain a statistical model on these features and to  or just to use a neural network and hopefully these features w would help

Because it seems like what you said about the mean of the  the voiced and the unvoiced    that seemed pretty encouraging.


uh  Yeah. Hopefully , I think what we want to have is to put these features in s some kind of , um  well , to  to obtain a statistical model on these features and to  or just to use a neural network and hopefully these features w would help

Because it seems like what you said about the mean of the  the voiced and the unvoiced    that seemed pretty encouraging.

Well , yeah , except the variance was big.


Because it seems like what you said about the mean of the  the voiced and the unvoiced    that seemed pretty encouraging.

Well , yeah , except the variance was big.


Well , yeah , except the variance was big.

Yeah. Except the variance is quite high.


Yeah. Except the variance is quite high.


Well , y I  I don't know that I would trust that so much because you 're doing these canonical mappings from TIMIT labellings.


Well , y I  I don't know that I would trust that so much because you 're doing these canonical mappings from TIMIT labellings.

Right ? So , really that 's sort of a cartoon picture about what 's voiced and unvoiced. So that could be giving you a lot of variance.


Right ? So , really that 's sort of a cartoon picture about what 's voiced and unvoiced. So that could be giving you a lot of variance.

I mean , i it  it may be that  that you 're finding something good and that the variance is sort of artificial because of how you 're getting your truth.


I mean , i it  it may be that  that you 're finding something good and that the variance is sort of artificial because of how you 're getting your truth.

Yeah. But another way of looking at it  might be that  I mean , what w we we are coming up with feature sets after all. So another way of looking at it is that  um , the mel cepstru mel  spectrum , mel cepstrum ,  any of these variants , um , give you the smooth spectrum. It 's the spectral envelope. By going back to the FFT ,  you 're getting something that is  more like the raw data. So the question is , what characterization  and you 're playing around with this  another way of looking at it is what characterization  of the difference between  the raw data  and this smooth version  is something that you 're missing that could help ? So , I mean , looking at different statistical measures of that difference , coming up with some things and just trying them out and seeing if you add them onto the feature vector does that make things better or worse in noise , where you 're really just i i the way I 'm looking at it is not so much you 're trying to f find the best  the world 's best voiced - unvoiced , uh , uh , classifier ,


Yeah. But another way of looking at it  might be that  I mean , what w we we are coming up with feature sets after all. So another way of looking at it is that  um , the mel cepstru mel  spectrum , mel cepstrum ,  any of these variants , um , give you the smooth spectrum. It 's the spectral envelope. By going back to the FFT ,  you 're getting something that is  more like the raw data. So the question is , what characterization  and you 're playing around with this  another way of looking at it is what characterization  of the difference between  the raw data  and this smooth version  is something that you 're missing that could help ? So , I mean , looking at different statistical measures of that difference , coming up with some things and just trying them out and seeing if you add them onto the feature vector does that make things better or worse in noise , where you 're really just i i the way I 'm looking at it is not so much you 're trying to f find the best  the world 's best voiced - unvoiced , uh , uh , classifier ,


Yeah. But another way of looking at it  might be that  I mean , what w we we are coming up with feature sets after all. So another way of looking at it is that  um , the mel cepstru mel  spectrum , mel cepstrum ,  any of these variants , um , give you the smooth spectrum. It 's the spectral envelope. By going back to the FFT ,  you 're getting something that is  more like the raw data. So the question is , what characterization  and you 're playing around with this  another way of looking at it is what characterization  of the difference between  the raw data  and this smooth version  is something that you 're missing that could help ? So , I mean , looking at different statistical measures of that difference , coming up with some things and just trying them out and seeing if you add them onto the feature vector does that make things better or worse in noise , where you 're really just i i the way I 'm looking at it is not so much you 're trying to f find the best  the world 's best voiced - unvoiced , uh , uh , classifier ,

but it 's more that ,  you know , uh , uh , try some different statistical characterizations of that difference back to the raw data


but it 's more that ,  you know , uh , uh , try some different statistical characterizations of that difference back to the raw data


but it 's more that ,  you know , uh , uh , try some different statistical characterizations of that difference back to the raw data

and  and m maybe there 's something there that  the system can use.


and  and m maybe there 's something there that  the system can use.

Yeah. Yeah , but ther more obvious is that  Yeah. The  the more obvious is that  that  well , using the  th the FFT , um ,  you just  it gives you just information about if it 's voiced or not voiced , ma mainly , I mean. But  So ,


Yeah. Yeah , but ther more obvious is that  Yeah. The  the more obvious is that  that  well , using the  th the FFT , um ,  you just  it gives you just information about if it 's voiced or not voiced , ma mainly , I mean. But  So ,

this is why we  we started to look  by having sort of voiced phonemes


Yeah. Yeah , but ther more obvious is that  Yeah. The  the more obvious is that  that  well , using the  th the FFT , um ,  you just  it gives you just information about if it 's voiced or not voiced , ma mainly , I mean. But  So ,

this is why we  we started to look  by having sort of voiced phonemes

Well , that 's the rea w w what I 'm arguing is that 's Yeah. I mean , uh , what I 'm arguing is that that  that 's givi you  gives you your intuition.


this is why we  we started to look  by having sort of voiced phonemes

Well , that 's the rea w w what I 'm arguing is that 's Yeah. I mean , uh , what I 'm arguing is that that  that 's givi you  gives you your intuition.


this is why we  we started to look  by having sort of voiced phonemes

Well , that 's the rea w w what I 'm arguing is that 's Yeah. I mean , uh , what I 'm arguing is that that  that 's givi you  gives you your intuition.

But in  in reality , it 's  you know , there 's all of this  this overlap and so forth ,


Well , that 's the rea w w what I 'm arguing is that 's Yeah. I mean , uh , what I 'm arguing is that that  that 's givi you  gives you your intuition.

But in  in reality , it 's  you know , there 's all of this  this overlap and so forth ,


But in  in reality , it 's  you know , there 's all of this  this overlap and so forth ,

and  But what I 'm saying is that may be OK , because what you 're really getting is not actually voiced versus unvoiced , both for the fac the reason of the overlap and  and then , uh , th you know , structural reasons , uh , uh , like the one that Chuck said , that  that in fact , well , the data itself is   that you 're working with is not perfect.


and  But what I 'm saying is that may be OK , because what you 're really getting is not actually voiced versus unvoiced , both for the fac the reason of the overlap and  and then , uh , th you know , structural reasons , uh , uh , like the one that Chuck said , that  that in fact , well , the data itself is   that you 're working with is not perfect.

So , what I 'm saying is maybe that 's not a killer because you 're just getting some characterization , one that 's driven by your intuition about voiced - unvoiced certainly ,


So , what I 'm saying is maybe that 's not a killer because you 're just getting some characterization , one that 's driven by your intuition about voiced - unvoiced certainly ,

but it 's just some characterization  of something back in the  in the  in the almost raw data , rather than the smooth version.


but it 's just some characterization  of something back in the  in the  in the almost raw data , rather than the smooth version.

And your intuition is driving you towards particular kinds of ,  uh , statistical characterizations of , um , what 's missing from the spectral envelope.


And your intuition is driving you towards particular kinds of ,  uh , statistical characterizations of , um , what 's missing from the spectral envelope.

Um , obviously you have something about the excitation , um , and what is it about the excitation , and , you know  and you 're not getting the excitation anyway , you know. So  so I  I would almost take a  uh , especially if  if these trainings and so forth are faster , I would almost just take a  uh , a scattershot at a few different  ways of look of characterizing that difference and , uh , you could have one of them but  and  and see , you know , which of them helps.


Um , obviously you have something about the excitation , um , and what is it about the excitation , and , you know  and you 're not getting the excitation anyway , you know. So  so I  I would almost take a  uh , especially if  if these trainings and so forth are faster , I would almost just take a  uh , a scattershot at a few different  ways of look of characterizing that difference and , uh , you could have one of them but  and  and see , you know , which of them helps.

So i is the idea that you 're going to take  whatever features you develop and  and just add them onto the future vector ? Or , what 's the use of the  the voiced - unvoiced detector ?


Um , obviously you have something about the excitation , um , and what is it about the excitation , and , you know  and you 're not getting the excitation anyway , you know. So  so I  I would almost take a  uh , especially if  if these trainings and so forth are faster , I would almost just take a  uh , a scattershot at a few different  ways of look of characterizing that difference and , uh , you could have one of them but  and  and see , you know , which of them helps.

So i is the idea that you 're going to take  whatever features you develop and  and just add them onto the future vector ? Or , what 's the use of the  the voiced - unvoiced detector ?

Uh , I guess we don't know exactly yet. But ,  um  Yeah. Th


So i is the idea that you 're going to take  whatever features you develop and  and just add them onto the future vector ? Or , what 's the use of the  the voiced - unvoiced detector ?

Uh , I guess we don't know exactly yet. But ,  um  Yeah. Th

It 's not part of a VAD system that you 're doing ?


Uh , I guess we don't know exactly yet. But ,  um  Yeah. Th

It 's not part of a VAD system that you 're doing ?


It 's not part of a VAD system that you 're doing ?


No , the idea was , I guess , to  to use them as  as features.


No , the idea was , I guess , to  to use them as  as features.

Uh  Yeah , it could be , uh  it could be  a neural network that does voiced and unvoiced detection ,


Uh  Yeah , it could be , uh  it could be  a neural network that does voiced and unvoiced detection ,

but it could be in the  also the big neural network that does phoneme classification.


but it could be in the  also the big neural network that does phoneme classification.


but it could be in the  also the big neural network that does phoneme classification.

But each one of the mixture components  I mean , you have , uh , uh , variance only , so it 's kind of like you 're just multiplying together these , um , probabilities from the individual features  within each mixture. So it 's  so , uh , it seems l you know


But each one of the mixture components  I mean , you have , uh , uh , variance only , so it 's kind of like you 're just multiplying together these , um , probabilities from the individual features  within each mixture. So it 's  so , uh , it seems l you know

I think it 's a neat thing. Uh , it seems like a good idea.


But each one of the mixture components  I mean , you have , uh , uh , variance only , so it 's kind of like you 're just multiplying together these , um , probabilities from the individual features  within each mixture. So it 's  so , uh , it seems l you know

I think it 's a neat thing. Uh , it seems like a good idea.

Yeah. Um. Yeah. I mean ,  I know that , um , people doing some robustness things a ways back were  were just doing  just being gross and just throwing in the FFT and actually it wasn't  wasn't  wasn't so bad. Uh , so it would s and  and you know that i it 's gotta hurt you a little bit to not have a   a spectral , uh  a s a smooth spectral envelope , so there must be something else that you get  in return for that


I think it 's a neat thing. Uh , it seems like a good idea.

Yeah. Um. Yeah. I mean ,  I know that , um , people doing some robustness things a ways back were  were just doing  just being gross and just throwing in the FFT and actually it wasn't  wasn't  wasn't so bad. Uh , so it would s and  and you know that i it 's gotta hurt you a little bit to not have a   a spectral , uh  a s a smooth spectral envelope , so there must be something else that you get  in return for that


Yeah. Um. Yeah. I mean ,  I know that , um , people doing some robustness things a ways back were  were just doing  just being gross and just throwing in the FFT and actually it wasn't  wasn't  wasn't so bad. Uh , so it would s and  and you know that i it 's gotta hurt you a little bit to not have a   a spectral , uh  a s a smooth spectral envelope , so there must be something else that you get  in return for that

So how does  uh , maybe I 'm going in too much detail , but  how exactly do you make the difference between the FFT and the smoothed  spectral envelope ? Wha - wh i i uh , how is that , uh  ?


So how does  uh , maybe I 'm going in too much detail , but  how exactly do you make the difference between the FFT and the smoothed  spectral envelope ? Wha - wh i i uh , how is that , uh  ?

Um , we just  How did we do it up again ?


So how does  uh , maybe I 'm going in too much detail , but  how exactly do you make the difference between the FFT and the smoothed  spectral envelope ? Wha - wh i i uh , how is that , uh  ?

Um , we just  How did we do it up again ?

Uh , we distend the  we have the twenty - three coefficient af after the mel f  filter ,


Um , we just  How did we do it up again ?

Uh , we distend the  we have the twenty - three coefficient af after the mel f  filter ,

and we extend these coefficient between the  all the frequency range.


Uh , we distend the  we have the twenty - three coefficient af after the mel f  filter ,

and we extend these coefficient between the  all the frequency range.


and we extend these coefficient between the  all the frequency range.

And i the interpolation i between the point  is  give for the triang triangular filter , the value of the triangular filter and of this way we obtained this mode this model speech.


and we extend these coefficient between the  all the frequency range.

And i the interpolation i between the point  is  give for the triang triangular filter , the value of the triangular filter and of this way we obtained this mode this model speech.

So you essentially take the values that  th that you get from the triangular filter and extend them to sor sort of like a rectangle , that 's at that m value.


And i the interpolation i between the point  is  give for the triang triangular filter , the value of the triangular filter and of this way we obtained this mode this model speech.

So you essentially take the values that  th that you get from the triangular filter and extend them to sor sort of like a rectangle , that 's at that m value.


And i the interpolation i between the point  is  give for the triang triangular filter , the value of the triangular filter and of this way we obtained this mode this model speech.

So you essentially take the values that  th that you get from the triangular filter and extend them to sor sort of like a rectangle , that 's at that m value.

Yeah. I think we have linear interpolation.


So you essentially take the values that  th that you get from the triangular filter and extend them to sor sort of like a rectangle , that 's at that m value.

Yeah. I think we have linear interpolation.


Yeah. I think we have linear interpolation.

So we have  we have one point for  one energy for each filter bank ,


So we have  we have one point for  one energy for each filter bank ,


which is  the energy  that 's centered on  on  on the triangle


which is  the energy  that 's centered on  on  on the triangle

Yeah. At the n at the center of the filter


which is  the energy  that 's centered on  on  on the triangle

Yeah. At the n at the center of the filter

So you  you end up with a vector that 's the same length as the FFT  vector ?


Yeah. At the n at the center of the filter

So you  you end up with a vector that 's the same length as the FFT  vector ?


So you  you end up with a vector that 's the same length as the FFT  vector ?

And then you just , uh , compute differences


And then you just , uh , compute differences

Yeah. I have here one example if you  if you want see something like that.


Yeah. I have here one example if you  if you want see something like that.


So. And I think the variance is computed only from , like , two hundred hertz to  one  to fifteen hundred.


Above , um   it seems that  Well , some voiced sound can have also ,  like , a noisy  part on high frequencies , and  But


Above , um   it seems that  Well , some voiced sound can have also ,  like , a noisy  part on high frequencies , and  But

No , it 's  makes sense to look at  low frequencies.


No , it 's  makes sense to look at  low frequencies.

So this is  uh , basically this is comparing  an original version of the signal to a smoothed version of the same signal ?


No , it 's  makes sense to look at  low frequencies.

So this is  uh , basically this is comparing  an original version of the signal to a smoothed version of the same signal ?

Right. So i so i i this is  I mean , i you could argue about whether it should be linear interpolation or  or  or  or zeroeth order , but  but


So this is  uh , basically this is comparing  an original version of the signal to a smoothed version of the same signal ?

Right. So i so i i this is  I mean , i you could argue about whether it should be linear interpolation or  or  or  or zeroeth order , but  but


Right. So i so i i this is  I mean , i you could argue about whether it should be linear interpolation or  or  or  or zeroeth order , but  but

at any rate something like this  is what you 're feeding your recognizer , typically.


at any rate something like this  is what you 're feeding your recognizer , typically.

No. Uh , so the mel cepstrum is the  is the  is the cepstrum of this   this , uh , spectrum or log spectrum ,


No. Uh , so the mel cepstrum is the  is the  is the cepstrum of this   this , uh , spectrum or log spectrum ,


No. Uh , so the mel cepstrum is the  is the  is the cepstrum of this   this , uh , spectrum or log spectrum ,

whatever it  You - you 're subtracting in  in  in  power domain or log domain ?


whatever it  You - you 're subtracting in  in  in  power domain or log domain ?


whatever it  You - you 're subtracting in  in  in  power domain or log domain ?

OK. So it 's sort of like division , when you do the  yeah , the spectra.


OK. So it 's sort of like division , when you do the  yeah , the spectra.


Um. Yeah. But , anyway , um  and that 's


Um. Yeah. But , anyway , um  and that 's

So what 's th uh , what 's the intuition behind this kind of a thing ? I  I don't know really know the signal - processing well enough to understand what   what is that doing.


Um. Yeah. But , anyway , um  and that 's

So what 's th uh , what 's the intuition behind this kind of a thing ? I  I don't know really know the signal - processing well enough to understand what   what is that doing.

So. Yeah. What happen if  what we have  have  what we would like to have is  some spectrum of the excitation signal ,


So what 's th uh , what 's the intuition behind this kind of a thing ? I  I don't know really know the signal - processing well enough to understand what   what is that doing.

So. Yeah. What happen if  what we have  have  what we would like to have is  some spectrum of the excitation signal ,

which is for voiced sound ideally a  a pulse train


So. Yeah. What happen if  what we have  have  what we would like to have is  some spectrum of the excitation signal ,

which is for voiced sound ideally a  a pulse train


which is for voiced sound ideally a  a pulse train

and for unvoiced it 's something that 's more flat.


and for unvoiced it 's something that 's more flat.

And the way to do this  is that  well , we have the  we have the FFT because it 's computed in  in the  in the system , and we have  the mel  filter banks ,


And the way to do this  is that  well , we have the  we have the FFT because it 's computed in  in the  in the system , and we have  the mel  filter banks ,

and so if we  if we , like , remove the mel filter bank from the FFT ,  we have something that 's  close to the  excitation signal.


and so if we  if we , like , remove the mel filter bank from the FFT ,  we have something that 's  close to the  excitation signal.

It 's something that 's like  a  a a train of p a pulse train for voiced sound


It 's something that 's like  a  a a train of p a pulse train for voiced sound


I see. So do you have a picture that sh ?


this picture ? What does it look like for unvoiced ?


but between the frequency that we are considered for the excitation


but between the frequency that we are considered for the excitation

for the difference and this is the difference.


for the difference and this is the difference.


Yeah. Because we begin ,  uh , in fifteen  point  the fifteen point.


Yeah. Because we begin ,  uh , in fifteen  point  the fifteen point.

So , does  does the periodicity of this signal say something about the  the


So , does  does the periodicity of this signal say something about the  the


I mean , to first order  what you 'd  what you 're doing  I mean , ignore all the details and all the ways which is  that these are complete lies. Uh , the  the  you know , what you 're doing in feature extraction for speech recognition is you have ,  uh , in your head a  a  a  a simplified production model for speech ,


I mean , to first order  what you 'd  what you 're doing  I mean , ignore all the details and all the ways which is  that these are complete lies. Uh , the  the  you know , what you 're doing in feature extraction for speech recognition is you have ,  uh , in your head a  a  a  a simplified production model for speech ,

in which you have a periodic or aperiodic source that 's driving some filters.


in which you have a periodic or aperiodic source that 's driving some filters.

Yeah. This is the  the auto - correlation  the R - zero energy.


in which you have a periodic or aperiodic source that 's driving some filters.

Yeah. This is the  the auto - correlation  the R - zero energy.

Do you have the mean  do you have the mean for the auto - correlation  ?


Yeah. This is the  the auto - correlation  the R - zero energy.

Do you have the mean  do you have the mean for the auto - correlation  ?

Uh , first order for speech recognition , you say " I don't care about the source ".


Do you have the mean  do you have the mean for the auto - correlation  ?

Uh , first order for speech recognition , you say " I don't care about the source ".


Uh , first order for speech recognition , you say " I don't care about the source ".


And so you just want to find out what the filters are.


And so you just want to find out what the filters are.

The filters  roughly act like a , um   a , uh   a an overall resonant  you know , f some resonances and so forth that th that 's processing excitation.


The filters  roughly act like a , um   a , uh   a an overall resonant  you know , f some resonances and so forth that th that 's processing excitation.


The filters  roughly act like a , um   a , uh   a an overall resonant  you know , f some resonances and so forth that th that 's processing excitation.

Ah , no. This is this ? More close. Is this ? And this.


Ah , no. This is this ? More close. Is this ? And this.


So they are  this is  there is less difference.


So they are  this is  there is less difference.

So if you look at the spectral envelope , just the very smooth properties of it ,  you get something closer to that.


So if you look at the spectral envelope , just the very smooth properties of it ,  you get something closer to that.


And the notion is if you have the full spectrum , with all the little nitty - gritty details ,  that that has the effect of both ,


And the notion is if you have the full spectrum , with all the little nitty - gritty details ,  that that has the effect of both ,

and it would be a multiplication in  in frequency domain


and it would be a multiplication in  in frequency domain

so that would be like an addition in log   power spectrum domain.


so that would be like an addition in log   power spectrum domain.

And so this is saying , well , if you really do have that  sort of vocal tract envelope , and you subtract that off , what you get is the excitation. And I call that lies because you don't really have that , you just have some kind of  signal - processing trickery to get something that 's kind of smooth. It 's not really what 's happening in the vocal tract


And so this is saying , well , if you really do have that  sort of vocal tract envelope , and you subtract that off , what you get is the excitation. And I call that lies because you don't really have that , you just have some kind of  signal - processing trickery to get something that 's kind of smooth. It 's not really what 's happening in the vocal tract

so you 're not really getting the vocal excitation.


so you 're not really getting the vocal excitation.

That 's why I was going to the  why I was referring to it in a more   a more , uh ,  uh ,  conservative way , when I was saying " well , it 's  yeah , it 's the excitation ". But it 's not really the excitation. It 's whatever it is that 's different between


That 's why I was going to the  why I was referring to it in a more   a more , uh ,  uh ,  conservative way , when I was saying " well , it 's  yeah , it 's the excitation ". But it 's not really the excitation. It 's whatever it is that 's different between

So  so , stand standing back from that , you sort of say there 's this very detailed representation.


So  so , stand standing back from that , you sort of say there 's this very detailed representation.


You go to a smooth representation cuz this typically generalizes better.


You go to a smooth representation cuz this typically generalizes better.

Um , but whenever you smooth you lose something , so the question is have you lost something you can you use ?


Um , but whenever you smooth you lose something , so the question is have you lost something you can you use ?

Um , probably you wouldn't want to go to the extreme of just ta saying " OK , our feature set will be the FFT " , cuz we really think we do gain something in robustness from going to something smoother , but maybe there 's something that we missed.


Um , probably you wouldn't want to go to the extreme of just ta saying " OK , our feature set will be the FFT " , cuz we really think we do gain something in robustness from going to something smoother , but maybe there 's something that we missed.


And then you go back to the intuition that , well , you don't really get the excitation , but you get something related to it.


And then you go back to the intuition that , well , you don't really get the excitation , but you get something related to it.

And it  and as you can see from those pictures , you do get something  that shows some periodicity , uh , in frequency ,