and so I went on - line and I uh found a pronunciation dictionary for Italian digits

and just looked at , you know , the number of phones in each one of the digits. Um you know , sort of the canonical way of setting up a  an HMM system is that you use  um three states per phone and um  so then the  the total number of states for a word would just be , you know , the number of phones times three. And so when I did that for the Italian digits , I got a number of states , ranging on the low end from nine to the high end , eighteen. Um.  Now you have to really add two to that because in HTK there 's an initial null and a final null so when they use  uh models that have eighteen states , there 're really sixteen states. They 've got those initial and final null states. And so um   their guess of eighteen states seems to be pretty well matched to the two longest words of the Italian digits , the four and five  which um , according to my , you know , sort of off the cuff calculation , should have eighteen states each.


and just looked at , you know , the number of phones in each one of the digits. Um you know , sort of the canonical way of setting up a  an HMM system is that you use  um three states per phone and um  so then the  the total number of states for a word would just be , you know , the number of phones times three. And so when I did that for the Italian digits , I got a number of states , ranging on the low end from nine to the high end , eighteen. Um.  Now you have to really add two to that because in HTK there 's an initial null and a final null so when they use  uh models that have eighteen states , there 're really sixteen states. They 've got those initial and final null states. And so um   their guess of eighteen states seems to be pretty well matched to the two longest words of the Italian digits , the four and five  which um , according to my , you know , sort of off the cuff calculation , should have eighteen states each.

And so they had sixteen. So that 's pretty close. Um   but for the  most of the words are sh much shorter. So the majority of them wanna have nine states. And so theirs are s sort of twice as long. So  my guess  uh And then if you  I  I printed out a confusion matrix um  uh for the well - matched case , and it turns out that the longest words are actually the ones that do the best. So my guess about what 's happening is that  you know , if you assume a fixed  the same amount of training data for each of these digits and a fixed length model for all of them but the actual words for some of them are half as long you really um have , you know , half as much training data for those models. Because if you have a long word and you 're training it to eighteen states ,   uh you 've got  you know , you 've got the same number of Gaussians , you 've gotta train in each case ,


And so they had sixteen. So that 's pretty close. Um   but for the  most of the words are sh much shorter. So the majority of them wanna have nine states. And so theirs are s sort of twice as long. So  my guess  uh And then if you  I  I printed out a confusion matrix um  uh for the well - matched case , and it turns out that the longest words are actually the ones that do the best. So my guess about what 's happening is that  you know , if you assume a fixed  the same amount of training data for each of these digits and a fixed length model for all of them but the actual words for some of them are half as long you really um have , you know , half as much training data for those models. Because if you have a long word and you 're training it to eighteen states ,   uh you 've got  you know , you 've got the same number of Gaussians , you 've gotta train in each case ,

but for the shorter words , you know , the total number of frames is actually half as many.


but for the shorter words , you know , the total number of frames is actually half as many.

So  it could be that , you know , for the short words there 's  because you have so many states , you just don't have enough data to train all those Gaussians. So um I 'm going to try to um create more word - specific  um uh prototype H M Ms to start training from.


but for the shorter words , you know , the total number of frames is actually half as many.

So  it could be that , you know , for the short words there 's  because you have so many states , you just don't have enough data to train all those Gaussians. So um I 'm going to try to um create more word - specific  um uh prototype H M Ms to start training from.

Yeah , I mean , it 's not at all uncommon you do worse on long word on short words than long words anyway just because you 're accumulating more evidence for the  for the longer word ,


So  it could be that , you know , for the short words there 's  because you have so many states , you just don't have enough data to train all those Gaussians. So um I 'm going to try to um create more word - specific  um uh prototype H M Ms to start training from.

Yeah , I mean , it 's not at all uncommon you do worse on long word on short words than long words anyway just because you 're accumulating more evidence for the  for the longer word ,


Yeah , I mean , it 's not at all uncommon you do worse on long word on short words than long words anyway just because you 're accumulating more evidence for the  for the longer word ,

Yeah so I 'll  I 'll , the next experiment I 'm gonna try is to just um you know create  uh models that seem to be more w matched to my guess about how long they should be.


Yeah so I 'll  I 'll , the next experiment I 'm gonna try is to just um you know create  uh models that seem to be more w matched to my guess about how long they should be.


Yeah so I 'll  I 'll , the next experiment I 'm gonna try is to just um you know create  uh models that seem to be more w matched to my guess about how long they should be.

And as part of that um I wanted to see sort of how the um  how these models were coming out , you know , what w  when we train up uh th you know , the model for " one " , which wants to have nine states , you know , what is the  uh what do the transition probabilities look like  in the self - loops ,  look like in  in those models ? And so I talked to Andreas and he explained to me how you can  calculate the expected duration of an HMM just by looking at the transition matrix


And as part of that um I wanted to see sort of how the um  how these models were coming out , you know , what w  when we train up uh th you know , the model for " one " , which wants to have nine states , you know , what is the  uh what do the transition probabilities look like  in the self - loops ,  look like in  in those models ? And so I talked to Andreas and he explained to me how you can  calculate the expected duration of an HMM just by looking at the transition matrix

and so I wrote a little Matlab script that calculates that and so I 'm gonna sort of print those out for each of the words to see what 's happening , you know , how these models are training up ,


and so I wrote a little Matlab script that calculates that and so I 'm gonna sort of print those out for each of the words to see what 's happening , you know , how these models are training up ,

you know , the long ones versus the short ones. I d I did  quickly , I did the silence model and   and um that 's coming out with about one point two seconds as its average duration and the silence model 's the one that 's used at the beginning and the end of each of the  string of digits.


you know , the long ones versus the short ones. I d I did  quickly , I did the silence model and   and um that 's coming out with about one point two seconds as its average duration and the silence model 's the one that 's used at the beginning and the end of each of the  string of digits.

Yeah , yeah. And so the S P model , which is what they put in between digits , I  I haven't calculated that for that one yet , but um. So they basically  their   their model for a whole digit string is silence  digit , SP , digit , SP blah - blah - blah and then silence at the end. And so.


you know , the long ones versus the short ones. I d I did  quickly , I did the silence model and   and um that 's coming out with about one point two seconds as its average duration and the silence model 's the one that 's used at the beginning and the end of each of the  string of digits.

Yeah , yeah. And so the S P model , which is what they put in between digits , I  I haven't calculated that for that one yet , but um. So they basically  their   their model for a whole digit string is silence  digit , SP , digit , SP blah - blah - blah and then silence at the end. And so.

Are the SP 's optional ? I mean skip them ?


Yeah , yeah. And so the S P model , which is what they put in between digits , I  I haven't calculated that for that one yet , but um. So they basically  their   their model for a whole digit string is silence  digit , SP , digit , SP blah - blah - blah and then silence at the end. And so.

Are the SP 's optional ? I mean skip them ?

I have to look at that , but I 'm not sure that they are. Now the one thing about the S P model is really it only has a single s emitting state to it.


Are the SP 's optional ? I mean skip them ?

I have to look at that , but I 'm not sure that they are. Now the one thing about the S P model is really it only has a single s emitting state to it.

So if it 's not optional , you know , it 's  it 's not gonna hurt a whole lot


I have to look at that , but I 'm not sure that they are. Now the one thing about the S P model is really it only has a single s emitting state to it.

So if it 's not optional , you know , it 's  it 's not gonna hurt a whole lot


So if it 's not optional , you know , it 's  it 's not gonna hurt a whole lot

and it 's tied to the center state of the silence model so it 's not its own  um It doesn't require its own training data ,


and it 's tied to the center state of the silence model so it 's not its own  um It doesn't require its own training data ,


So it , I mean , it 's pretty good the way that they have it set up , but um i So I wanna play with that a little bit more. I 'm curious about looking at , you know  how these models have trained and looking at the expected durations of the models and I wanna compare that in the  the well - matched case f to the unmatched case , and see if you can get an idea of  just from looking at the  durations of these models , you know , what what 's happening.


So it , I mean , it 's pretty good the way that they have it set up , but um i So I wanna play with that a little bit more. I 'm curious about looking at , you know  how these models have trained and looking at the expected durations of the models and I wanna compare that in the  the well - matched case f to the unmatched case , and see if you can get an idea of  just from looking at the  durations of these models , you know , what what 's happening.

Yeah , I mean , I think that uh , as much as you can , it 's good to d sort of not do anything really tricky.


So it , I mean , it 's pretty good the way that they have it set up , but um i So I wanna play with that a little bit more. I 'm curious about looking at , you know  how these models have trained and looking at the expected durations of the models and I wanna compare that in the  the well - matched case f to the unmatched case , and see if you can get an idea of  just from looking at the  durations of these models , you know , what what 's happening.

Yeah , I mean , I think that uh , as much as you can , it 's good to d sort of not do anything really tricky.

Not do anything that 's really finely tuned , but just sort of eh you know you t you i z


Yeah , I mean , I think that uh , as much as you can , it 's good to d sort of not do anything really tricky.

Not do anything that 's really finely tuned , but just sort of eh you know you t you i z


Not do anything that 's really finely tuned , but just sort of eh you know you t you i z

The premise is kind of you have a  a good person look at this for a few weeks and what do you come up with ?


The premise is kind of you have a  a good person look at this for a few weeks and what do you come up with ?


The premise is kind of you have a  a good person look at this for a few weeks and what do you come up with ?

And Hynek , when I wa told him about this , he had an interesting point , and that was th um  the  the final models that they end up training up have I think probably something on the order of six Gaussians per state. So they 're fairly , you know , hefty models. And Hynek was saying that well , probably in a real application ,  you wouldn't have enough compute to handle models that are very big or complicated. So in fact what we may want are simpler models.


And Hynek , when I wa told him about this , he had an interesting point , and that was th um  the  the final models that they end up training up have I think probably something on the order of six Gaussians per state. So they 're fairly , you know , hefty models. And Hynek was saying that well , probably in a real application ,  you wouldn't have enough compute to handle models that are very big or complicated. So in fact what we may want are simpler models.


And Hynek , when I wa told him about this , he had an interesting point , and that was th um  the  the final models that they end up training up have I think probably something on the order of six Gaussians per state. So they 're fairly , you know , hefty models. And Hynek was saying that well , probably in a real application ,  you wouldn't have enough compute to handle models that are very big or complicated. So in fact what we may want are simpler models.

And compare how they perform to that. But  you know , it depends on what the actual application is and it 's really hard to know what your limits are in terms of how many Gaussians you can have.


And Hynek , when I wa told him about this , he had an interesting point , and that was th um  the  the final models that they end up training up have I think probably something on the order of six Gaussians per state. So they 're fairly , you know , hefty models. And Hynek was saying that well , probably in a real application ,  you wouldn't have enough compute to handle models that are very big or complicated. So in fact what we may want are simpler models.

And compare how they perform to that. But  you know , it depends on what the actual application is and it 's really hard to know what your limits are in terms of how many Gaussians you can have.

Right. And that , I mean , at the moment that 's not the limitation , so.


And compare how they perform to that. But  you know , it depends on what the actual application is and it 's really hard to know what your limits are in terms of how many Gaussians you can have.

Right. And that , I mean , at the moment that 's not the limitation , so.


And compare how they perform to that. But  you know , it depends on what the actual application is and it 's really hard to know what your limits are in terms of how many Gaussians you can have.

Right. And that , I mean , at the moment that 's not the limitation , so.

I mean , I  I  I  what I thought you were gonna say i but which I was thinking was um where did six come from ? Probably came from the same place eighteen came from. You know , so.


Right. And that , I mean , at the moment that 's not the limitation , so.

I mean , I  I  I  what I thought you were gonna say i but which I was thinking was um where did six come from ? Probably came from the same place eighteen came from. You know , so.


I mean , I  I  I  what I thought you were gonna say i but which I was thinking was um where did six come from ? Probably came from the same place eighteen came from. You know , so.

Uh  that 's another parameter , right ? that  that maybe , you know , uh  you really want three or nine or


I mean , I  I  I  what I thought you were gonna say i but which I was thinking was um where did six come from ? Probably came from the same place eighteen came from. You know , so.

Uh  that 's another parameter , right ? that  that maybe , you know , uh  you really want three or nine or

Yeah , yeah. Well one thing  I mean , if I  if  if I start um reducing the number of states for some of these shorter models  that 's gonna reduce the total number of Gaussians.


Uh  that 's another parameter , right ? that  that maybe , you know , uh  you really want three or nine or

Yeah , yeah. Well one thing  I mean , if I  if  if I start um reducing the number of states for some of these shorter models  that 's gonna reduce the total number of Gaussians.


Uh  that 's another parameter , right ? that  that maybe , you know , uh  you really want three or nine or

Yeah , yeah. Well one thing  I mean , if I  if  if I start um reducing the number of states for some of these shorter models  that 's gonna reduce the total number of Gaussians.

So in a sense it 'll be a simpler system.


Yeah , yeah. Well one thing  I mean , if I  if  if I start um reducing the number of states for some of these shorter models  that 's gonna reduce the total number of Gaussians.

So in a sense it 'll be a simpler system.

Yeah. Yeah. But I think right now again the idea is doing just very simple things


So in a sense it 'll be a simpler system.

Yeah. Yeah. But I think right now again the idea is doing just very simple things


So in a sense it 'll be a simpler system.

Yeah. Yeah. But I think right now again the idea is doing just very simple things

how much better can you make it ? And um since they 're only simple things there 's nothing that you 're gonna do that is going to blow up the amount of computation


Yeah. Yeah. But I think right now again the idea is doing just very simple things

how much better can you make it ? And um since they 're only simple things there 's nothing that you 're gonna do that is going to blow up the amount of computation


how much better can you make it ? And um since they 're only simple things there 's nothing that you 're gonna do that is going to blow up the amount of computation


if you found that nine was better than six that would be O K , I think , actually.


if you found that nine was better than six that would be O K , I think , actually.

Yeah. I really wasn't even gonna play with that part of the system yet ,


Yeah. I really wasn't even gonna play with that part of the system yet ,


Yeah. I really wasn't even gonna play with that part of the system yet ,

Yeah , just work with the models , yeah.


Yeah , just work with the models , yeah.

yeah , just look at the length of the models and just see what happens.


yeah , just look at the length of the models and just see what happens.


We 're going ? OK. Sh - Close your door on  door on the way out ?


Yeah. Probably wanna get this other door , too. OK. So. Um.   What are we talking about today ?


Yeah. Probably wanna get this other door , too. OK. So. Um.   What are we talking about today ?

Uh , well , first there are perhaps these uh Meeting Recorder digits that we tested.


Yeah. Probably wanna get this other door , too. OK. So. Um.   What are we talking about today ?

Uh , well , first there are perhaps these uh Meeting Recorder digits that we tested.

Oh , yeah. That was kind of uh interesting.


Uh , well , first there are perhaps these uh Meeting Recorder digits that we tested.

Oh , yeah. That was kind of uh interesting.

The  both the uh   the SRI System and the oth


Oh , yeah. That was kind of uh interesting.

The  both the uh   the SRI System and the oth


The  both the uh   the SRI System and the oth

And for one thing that  that sure shows the  difference between having a lot of uh training data  or not ,


And for one thing that  that sure shows the  difference between having a lot of uh training data  or not ,

uh , the uh   The best kind of number we have on the English uh  on near microphone only is  is uh three or four percent.


uh , the uh   The best kind of number we have on the English uh  on near microphone only is  is uh three or four percent.

And uh it 's significantly better than that , using fairly simple front - ends  on   on the uh   uh , with the SRI system.


And uh it 's significantly better than that , using fairly simple front - ends  on   on the uh   uh , with the SRI system.

So I th I think that the uh  But that 's  that 's using uh a  a pretty huge amount of data , mostly not digits , of course , but  but then again  Well , yeah. In fact , mostly not digits for the actual training the H M Ms whereas uh in this case we 're just using digits for training the H M


So I th I think that the uh  But that 's  that 's using uh a  a pretty huge amount of data , mostly not digits , of course , but  but then again  Well , yeah. In fact , mostly not digits for the actual training the H M Ms whereas uh in this case we 're just using digits for training the H M

Did anybody mention about whether the  the SRI system is a   is  is doing the digits um the wor as a word model or as uh a sub s sub - phone states ?


So I th I think that the uh  But that 's  that 's using uh a  a pretty huge amount of data , mostly not digits , of course , but  but then again  Well , yeah. In fact , mostly not digits for the actual training the H M Ms whereas uh in this case we 're just using digits for training the H M

Did anybody mention about whether the  the SRI system is a   is  is doing the digits um the wor as a word model or as uh a sub s sub - phone states ?

I guess it 's  it 's uh allophone models ,


Did anybody mention about whether the  the SRI system is a   is  is doing the digits um the wor as a word model or as uh a sub s sub - phone states ?

I guess it 's  it 's uh allophone models ,


I guess it 's  it 's uh allophone models ,


Yeah. I think so , because it 's their very d huge , their huge system.


Yeah. I think so , because it 's their very d huge , their huge system.

And. But. So. There is one difference  Well , the SRI system  the result for the SRI system that are represented here are with adaptation. So there is  It 's their complete system and  including on - line uh unsupervised adaptation.


And. But. So. There is one difference  Well , the SRI system  the result for the SRI system that are represented here are with adaptation. So there is  It 's their complete system and  including on - line uh unsupervised adaptation.

And if you don't use adaptation , the error rate is around fifty percent worse , I think , if I remember.


And if you don't use adaptation , the error rate is around fifty percent worse , I think , if I remember.


Nnn. It 's  Yeah. It 's quite significant.


But  but uh what  what I think I 'd be interested to do given that , is that we  we should uh  take  I guess that somebody 's gonna do this , right ?  is to take some of these tandem things and feed it into the SRI system , right ?


Yeah. But  But I guess the main point is the data because uh  I am not sure. Our back - end is  is fairly simple but until now , well , the attempts to improve it or  have fail Ah , well , I mean uh what Chuck tried to  to  to do


Yeah. But  But I guess the main point is the data because uh  I am not sure. Our back - end is  is fairly simple but until now , well , the attempts to improve it or  have fail Ah , well , I mean uh what Chuck tried to  to  to do

Yeah , but he 's doing it with the same data , right ? I mean so to   So there 's  there 's  there 's two things being affected.


Yeah. But  But I guess the main point is the data because uh  I am not sure. Our back - end is  is fairly simple but until now , well , the attempts to improve it or  have fail Ah , well , I mean uh what Chuck tried to  to  to do

Yeah , but he 's doing it with the same data , right ? I mean so to   So there 's  there 's  there 's two things being affected.

I mean. One is that  that , you know , there 's something simple that 's wrong with the back - end. We 've been playing a number of states


Yeah , but he 's doing it with the same data , right ? I mean so to   So there 's  there 's  there 's two things being affected.

I mean. One is that  that , you know , there 's something simple that 's wrong with the back - end. We 've been playing a number of states


I mean. One is that  that , you know , there 's something simple that 's wrong with the back - end. We 've been playing a number of states

uh I  I don't know if he got to the point of playing with the uh number of Gaussians yet


uh I  I don't know if he got to the point of playing with the uh number of Gaussians yet

but  but uh , uh , you know. But , yeah , so far he hadn't gotten any big improvement ,


but  but uh , uh , you know. But , yeah , so far he hadn't gotten any big improvement ,

but that 's all with the same amount of data which is pretty small.


but that 's all with the same amount of data which is pretty small.


but that 's all with the same amount of data which is pretty small.

Mmm. So , yeah , we could retrain some of these tandem on  on huge


Mmm. So , yeah , we could retrain some of these tandem on  on huge

Well , you could do that , but I 'm saying even with it not  with that part not retrained , just  just using  having the H M Ms  much better H M


Well , you could do that , but I 'm saying even with it not  with that part not retrained , just  just using  having the H M Ms  much better H M


Um.  But just train those H M Ms using different features , the features coming from our Aurora stuff.


Um.  But just train those H M Ms using different features , the features coming from our Aurora stuff.

Yeah. But  what would be interesting to see also is what  what  perhaps it 's not related , the amount of data but the um recording conditions. I don't know. Because  it 's probably not a problem of noise , because our features are supposed to be robust to noise.


Yeah. But  what would be interesting to see also is what  what  perhaps it 's not related , the amount of data but the um recording conditions. I don't know. Because  it 's probably not a problem of noise , because our features are supposed to be robust to noise.


Yeah. But  what would be interesting to see also is what  what  perhaps it 's not related , the amount of data but the um recording conditions. I don't know. Because  it 's probably not a problem of noise , because our features are supposed to be robust to noise.

It 's not a problem of channel , because there is um   normalization with respect to the channel. So


Yeah. But  what would be interesting to see also is what  what  perhaps it 's not related , the amount of data but the um recording conditions. I don't know. Because  it 's probably not a problem of noise , because our features are supposed to be robust to noise.

It 's not a problem of channel , because there is um   normalization with respect to the channel. So

I  I  I 'm sorry. What  what is the problem that you 're trying to explain ?


It 's not a problem of channel , because there is um   normalization with respect to the channel. So

I  I  I 'm sorry. What  what is the problem that you 're trying to explain ?

The  the fact that  the result with the tandem and Aurora system are  uh so much worse.


I  I  I 'm sorry. What  what is the problem that you 're trying to explain ?

The  the fact that  the result with the tandem and Aurora system are  uh so much worse.


The  the fact that  the result with the tandem and Aurora system are  uh so much worse.

I uh but I 'm  I 'm almost certain that it  it   I mean , that it has to do with the um amount of training data.


I uh but I 'm  I 'm almost certain that it  it   I mean , that it has to do with the um amount of training data.


I uh but I 'm  I 'm almost certain that it  it   I mean , that it has to do with the um amount of training data.

It  it 's  it 's orders of magnitude off.


I uh but I 'm  I 'm almost certain that it  it   I mean , that it has to do with the um amount of training data.

It  it 's  it 's orders of magnitude off.

Yeah but  Yeah. Yeah but we train only on digits and it 's  it 's a digit task , so. Well.


It  it 's  it 's orders of magnitude off.

Yeah but  Yeah. Yeah but we train only on digits and it 's  it 's a digit task , so. Well.

But  but having a huge  If   if you look at what commercial places do , they use a huge amount of data.


Yeah but  Yeah. Yeah but we train only on digits and it 's  it 's a digit task , so. Well.

But  but having a huge  If   if you look at what commercial places do , they use a huge amount of data.


But  but having a huge  If   if you look at what commercial places do , they use a huge amount of data.


So.  I mean , ordinarily you would say " well , given that you have enough occurrences of the digits , you can just train with digits rather than with , you know "


So.  I mean , ordinarily you would say " well , given that you have enough occurrences of the digits , you can just train with digits rather than with , you know "

But the thing is , if you have a huge  in other words , do word models  But if you have a huge amount of data then you 're going to have many occurrences of similar uh allophones.


But the thing is , if you have a huge  in other words , do word models  But if you have a huge amount of data then you 're going to have many occurrences of similar uh allophones.

And that 's just a huge amount of training for it.


And that 's just a huge amount of training for it.

So it 's  um   I  I think it has to be that , because , as you say , this is , you know , this is near - microphone ,


So it 's  um   I  I think it has to be that , because , as you say , this is , you know , this is near - microphone ,


Um. Now , some of it could be the fact that uh  let 's see , in the  in these multi - train things did we include noisy data in the training ?


Um. Now , some of it could be the fact that uh  let 's see , in the  in these multi - train things did we include noisy data in the training ?

I mean , that could be hurting us actually , for the clean case.


Um. Now , some of it could be the fact that uh  let 's see , in the  in these multi - train things did we include noisy data in the training ?

I mean , that could be hurting us actually , for the clean case.

Yeah. Well , actually we see that the clean train for the Aurora proposals are  are better than the multi - train ,


I mean , that could be hurting us actually , for the clean case.

Yeah. Well , actually we see that the clean train for the Aurora proposals are  are better than the multi - train ,


Yeah. Well , actually we see that the clean train for the Aurora proposals are  are better than the multi - train ,

Yeah. Cuz this is clean data , and so that 's not too surprising.


Yeah. Cuz this is clean data , and so that 's not too surprising.


Yeah. Cuz this is clean data , and so that 's not too surprising.

Well , o I guess what I meant is that well , let 's say if we  if we add enough data to train on the um on the Meeting Recorder digits , I guess we could have better results than this.


Well , o I guess what I meant is that well , let 's say if we  if we add enough data to train on the um on the Meeting Recorder digits , I guess we could have better results than this.


Well , o I guess what I meant is that well , let 's say if we  if we add enough data to train on the um on the Meeting Recorder digits , I guess we could have better results than this.

And. What I meant is that perhaps we can learn something uh from this , what 's  what 's wrong uh what  what is different between TI - digits and these digits and


Well , o I guess what I meant is that well , let 's say if we  if we add enough data to train on the um on the Meeting Recorder digits , I guess we could have better results than this.

And. What I meant is that perhaps we can learn something uh from this , what 's  what 's wrong uh what  what is different between TI - digits and these digits and

What kind of numbers are we getting on TI - digits ?


And. What I meant is that perhaps we can learn something uh from this , what 's  what 's wrong uh what  what is different between TI - digits and these digits and

What kind of numbers are we getting on TI - digits ?


What kind of numbers are we getting on TI - digits ?


So in the actual TI - digits database we 're getting point eight percent ,


So in the actual TI - digits database we 're getting point eight percent ,

and here we 're getting three or four  three , let 's see , three for this ?


and here we 're getting three or four  three , let 's see , three for this ?

Yeah. Sure , but I mean , um point eight percent is something like double uh or triple what people have gotten who 've worked very hard at doing that.


Yeah. Sure , but I mean , um point eight percent is something like double uh or triple what people have gotten who 've worked very hard at doing that.

And  and also , as you point out , there 's adaptation in these numbers also. So if you , you know , put the ad adap take the adaptation off , then it  for the English - Near you get something like two percent.


And  and also , as you point out , there 's adaptation in these numbers also. So if you , you know , put the ad adap take the adaptation off , then it  for the English - Near you get something like two percent.

And here you had , you know , something like three point four. And I could easily see that difference coming from this huge amount of data that it was trained on.


And here you had , you know , something like three point four. And I could easily see that difference coming from this huge amount of data that it was trained on.


You know , I don't think there 's anything magical here.


You know , I don't think there 's anything magical here.

It 's , you know , we used a simple HTK system with a modest amount of data. And this is a  a , you know , modern  uh system uh has  has a lot of nice points to it.


It 's , you know , we used a simple HTK system with a modest amount of data. And this is a  a , you know , modern  uh system uh has  has a lot of nice points to it.

Um. So. I mean , the HTK is an older HTK , even. So. Yeah it  it 's not that surprising.


Um. So. I mean , the HTK is an older HTK , even. So. Yeah it  it 's not that surprising.

But to me it just  it just meant a practical  point that um if we want to  publish results on digits that  that people pay  attention to we probably should uh  Cuz we 've had the problem before that you get  show some  nice improvement on something that 's  that 's uh , uh  it seems like too large a number , and uh  uh people don't necessarily take it so seriously.


But to me it just  it just meant a practical  point that um if we want to  publish results on digits that  that people pay  attention to we probably should uh  Cuz we 've had the problem before that you get  show some  nice improvement on something that 's  that 's uh , uh  it seems like too large a number , and uh  uh people don't necessarily take it so seriously.

Um. Yeah. Yeah. So the three point four percent for this uh is  is uh  So why is it  It 's an interesting question though , still. Why is  why is it three point four percent for the d the digits recorded in this environment as opposed to  the uh point eight percent for  for  for the original TI - digits database ? Um.


Um. Yeah. Yeah. So the three point four percent for this uh is  is uh  So why is it  It 's an interesting question though , still. Why is  why is it three point four percent for the d the digits recorded in this environment as opposed to  the uh point eight percent for  for  for the original TI - digits database ? Um.

Given  given the same  Yeah. So ignore  ignoring the  the  the SRI system for a moment ,


Given  given the same  Yeah. So ignore  ignoring the  the  the SRI system for a moment ,

just looking at  the TI - di the uh tandem system , if we 're getting point eight percent , which , yes , it 's high. It 's , you know , it  it 's not awfully high ,


just looking at  the TI - di the uh tandem system , if we 're getting point eight percent , which , yes , it 's high. It 's , you know , it  it 's not awfully high ,

but it 's , you know  it 's  it 's high. Um.  Why is it  uh four times as high , or more ?


but it 's , you know  it 's  it 's high. Um.  Why is it  uh four times as high , or more ?

Right ? I mean , there 's   even though it 's close - miked there 's still  there really is background noise.


Right ? I mean , there 's   even though it 's close - miked there 's still  there really is background noise.

Um. And  uh I suspect when the TI - digits were recorded if somebody fumbled or said something wrong or something that they probably made them take it over.


Um. And  uh I suspect when the TI - digits were recorded if somebody fumbled or said something wrong or something that they probably made them take it over.

It was not  I mean there was no attempt to have it be realistic in any  in any sense at all.


Um. And  uh I suspect when the TI - digits were recorded if somebody fumbled or said something wrong or something that they probably made them take it over.

It was not  I mean there was no attempt to have it be realistic in any  in any sense at all.

Well. Yeah. And acoustically , it 's q it 's  I listened. It 's quite different. TI - digit is  it 's very , very clean and it 's like studio recording


It was not  I mean there was no attempt to have it be realistic in any  in any sense at all.

Well. Yeah. And acoustically , it 's q it 's  I listened. It 's quite different. TI - digit is  it 's very , very clean and it 's like studio recording


It was not  I mean there was no attempt to have it be realistic in any  in any sense at all.

Well. Yeah. And acoustically , it 's q it 's  I listened. It 's quite different. TI - digit is  it 's very , very clean and it 's like studio recording

whereas these Meeting Recorder digits sometimes you have breath noise and Mmm.


Well. Yeah. And acoustically , it 's q it 's  I listened. It 's quite different. TI - digit is  it 's very , very clean and it 's like studio recording

whereas these Meeting Recorder digits sometimes you have breath noise and Mmm.


whereas these Meeting Recorder digits sometimes you have breath noise and Mmm.


It 's  I think it 's  it 's the indication it 's harder.


It 's  I think it 's  it 's the indication it 's harder.

Uh.  Yeah and again , you know , i that 's true either way. I mean so take a look at the uh   um , the SRI results. I mean , they 're much much better , but still you 're getting something like one point three percent for uh things that are same data as in T  TI - digits the same  same text.


Uh.  Yeah and again , you know , i that 's true either way. I mean so take a look at the uh   um , the SRI results. I mean , they 're much much better , but still you 're getting something like one point three percent for uh things that are same data as in T  TI - digits the same  same text.

Uh. And uh , I 'm sure the same  same system would  would get , you know , point  point three or point four or something  on the actual TI - digits. So this  I think , on both systems the  these digits are showing up as harder.


Uh. And uh , I 'm sure the same  same system would  would get , you know , point  point three or point four or something  on the actual TI - digits. So this  I think , on both systems the  these digits are showing up as harder.


Which I find sort of interesting cause I think this is closer to  uh I mean it 's still read. But I still think it 's much closer to  to what  what people actually face ,  um when they 're  they 're dealing with people saying digits over the telephone. I mean.  I don't think uh  I mean , I 'm sure they wouldn't release the numbers , but I don't think that uh  the uh  the  the companies that  that do telephone  speech get anything like point four percent on their  digits. I 'm  I 'm  I 'm sure they get  Uh , I mean , for one thing people do phone up who don't have uh uh Middle America accents and it 's a we we it 's  it 's  it 's US.


Which I find sort of interesting cause I think this is closer to  uh I mean it 's still read. But I still think it 's much closer to  to what  what people actually face ,  um when they 're  they 're dealing with people saying digits over the telephone. I mean.  I don't think uh  I mean , I 'm sure they wouldn't release the numbers , but I don't think that uh  the uh  the  the companies that  that do telephone  speech get anything like point four percent on their  digits. I 'm  I 'm  I 'm sure they get  Uh , I mean , for one thing people do phone up who don't have uh uh Middle America accents and it 's a we we it 's  it 's  it 's US.

it has  has many people   who sound in many different ways. So. Um. I mean. OK. That was that topic. What else we got ?


it has  has many people   who sound in many different ways. So. Um. I mean. OK. That was that topic. What else we got ?

Did we end up giving up on  on , any Eurospeech submissions ,


Did we end up giving up on  on , any Eurospeech submissions ,

or  ? I know Thilo and Dan Ellis are  are submitting something , but uh.


Did we end up giving up on  on , any Eurospeech submissions ,

or  ? I know Thilo and Dan Ellis are  are submitting something , but uh.

Yeah. I   I guess e the only thing with these  the Meeting Recorder and , well ,  So , I think , yeah  I think we basically gave up.


or  ? I know Thilo and Dan Ellis are  are submitting something , but uh.

Yeah. I   I guess e the only thing with these  the Meeting Recorder and , well ,  So , I think , yeah  I think we basically gave up.

Um.  Now , actually for the  for the Aur - uh


Yeah. I   I guess e the only thing with these  the Meeting Recorder and , well ,  So , I think , yeah  I think we basically gave up.

Um.  Now , actually for the  for the Aur - uh

we do have stuff for Aurora , right ? Because  because we have ano an extra month or something.


Um.  Now , actually for the  for the Aur - uh

we do have stuff for Aurora , right ? Because  because we have ano an extra month or something.

Yeah. Yeah. Yeah. So. Yeah , for sure we will do something for the special session.


we do have stuff for Aurora , right ? Because  because we have ano an extra month or something.

Yeah. Yeah. Yeah. So. Yeah , for sure we will do something for the special session.

Yeah. Well , that 's fine. So th so  so we have a couple  a couple little things on Meeting Recorder


Yeah. Yeah. Yeah. So. Yeah , for sure we will do something for the special session.

Yeah. Well , that 's fine. So th so  so we have a couple  a couple little things on Meeting Recorder

and we have   We don't  we don't have to flood it with papers. We 're not trying to prove anything to anybody. so. That 's fine. Um. Anything else ?


Yeah. Well , that 's fine. So th so  so we have a couple  a couple little things on Meeting Recorder

and we have   We don't  we don't have to flood it with papers. We 're not trying to prove anything to anybody. so. That 's fine. Um. Anything else ?

Yeah. Well. So. Perhaps the point is that we 've been working on  is , yeah , we have put the um the good VAD in the system and  it really makes a huge difference. Um. So , yeah. I think , yeah , this is perhaps one of the reason why our system was not   not the best , because with the new VAD , it 's very  the results are similar to the France Telecom results and perhaps even better sometimes.


and we have   We don't  we don't have to flood it with papers. We 're not trying to prove anything to anybody. so. That 's fine. Um. Anything else ?

Yeah. Well. So. Perhaps the point is that we 've been working on  is , yeah , we have put the um the good VAD in the system and  it really makes a huge difference. Um. So , yeah. I think , yeah , this is perhaps one of the reason why our system was not   not the best , because with the new VAD , it 's very  the results are similar to the France Telecom results and perhaps even better sometimes.


Yeah. Well. So. Perhaps the point is that we 've been working on  is , yeah , we have put the um the good VAD in the system and  it really makes a huge difference. Um. So , yeah. I think , yeah , this is perhaps one of the reason why our system was not   not the best , because with the new VAD , it 's very  the results are similar to the France Telecom results and perhaps even better sometimes.

Um. So there is this point. Uh. The problem is that it 's very big and   we still have to think how to  where to put it and   um ,


Um. So there is this point. Uh. The problem is that it 's very big and   we still have to think how to  where to put it and   um ,


Um. So there is this point. Uh. The problem is that it 's very big and   we still have to think how to  where to put it and   um ,

because it  it  well , this VAD uh either some delay and we  if we put it on the server side , it doesn't work , because on the server side features you already have LDA applied  from the f from the terminal side and  so you accumulate the delay so the VAD should be before the LDA which means perhaps on the terminal side and then smaller  and


Um. So there is this point. Uh. The problem is that it 's very big and   we still have to think how to  where to put it and   um ,

because it  it  well , this VAD uh either some delay and we  if we put it on the server side , it doesn't work , because on the server side features you already have LDA applied  from the f from the terminal side and  so you accumulate the delay so the VAD should be before the LDA which means perhaps on the terminal side and then smaller  and

So wha where did this good VAD come from ?


because it  it  well , this VAD uh either some delay and we  if we put it on the server side , it doesn't work , because on the server side features you already have LDA applied  from the f from the terminal side and  so you accumulate the delay so the VAD should be before the LDA which means perhaps on the terminal side and then smaller  and

So wha where did this good VAD come from ?

So. It 's um from OGI. So it 's the network trained  it 's the network with the huge amounts on hidden  of hidden units , and um nine input frames compared to the VAD that was in the proposal which has a very small amount of hidden units and fewer inputs.


So wha where did this good VAD come from ?

So. It 's um from OGI. So it 's the network trained  it 's the network with the huge amounts on hidden  of hidden units , and um nine input frames compared to the VAD that was in the proposal which has a very small amount of hidden units and fewer inputs.


So. It 's um from OGI. So it 's the network trained  it 's the network with the huge amounts on hidden  of hidden units , and um nine input frames compared to the VAD that was in the proposal which has a very small amount of hidden units and fewer inputs.

Oh. Yeah , but they had to  get rid of it because of the space , didn't they ?


Oh. Yeah , but they had to  get rid of it because of the space , didn't they ?

Yeah. So. Yeah. But the abso assumption is that we will be able to make a VAD that 's small and that works fine. And. So we can


Yeah. So. Yeah. But the abso assumption is that we will be able to make a VAD that 's small and that works fine. And. So we can

But the other thing is uh to use a different VAD entirely. I mean , uh i if  if there 's a  if  if  I  I don't know what the thinking was amongst the  the  the  the ETSI folk but um if everybody agreed sure let 's use this VAD and take that out of there


But the other thing is uh to use a different VAD entirely. I mean , uh i if  if there 's a  if  if  I  I don't know what the thinking was amongst the  the  the  the ETSI folk but um if everybody agreed sure let 's use this VAD and take that out of there

Mm - hmm. Mm - hmm. They just want , apparently  they don't want to fix the VAD because they think there is some interaction between feature extraction and  and VAD or frame dropping But they still  want to  just to give some um  requirement for this VAD because it 's  it will not be part of  they don't want it to be part of the standard.


But the other thing is uh to use a different VAD entirely. I mean , uh i if  if there 's a  if  if  I  I don't know what the thinking was amongst the  the  the  the ETSI folk but um if everybody agreed sure let 's use this VAD and take that out of there

Mm - hmm. Mm - hmm. They just want , apparently  they don't want to fix the VAD because they think there is some interaction between feature extraction and  and VAD or frame dropping But they still  want to  just to give some um  requirement for this VAD because it 's  it will not be part of  they don't want it to be part of the standard.

So. So it must be at least uh somewhat fixed but not completely. So there just will be some requirements that are still not  uh not yet uh ready I think.


Mm - hmm. Mm - hmm. They just want , apparently  they don't want to fix the VAD because they think there is some interaction between feature extraction and  and VAD or frame dropping But they still  want to  just to give some um  requirement for this VAD because it 's  it will not be part of  they don't want it to be part of the standard.

So. So it must be at least uh somewhat fixed but not completely. So there just will be some requirements that are still not  uh not yet uh ready I think.

Determined. I see. But I was thinking that  that uh  s " Sure , there may be some interaction ,


So. So it must be at least uh somewhat fixed but not completely. So there just will be some requirements that are still not  uh not yet uh ready I think.

Determined. I see. But I was thinking that  that uh  s " Sure , there may be some interaction ,


So. So it must be at least uh somewhat fixed but not completely. So there just will be some requirements that are still not  uh not yet uh ready I think.

Determined. I see. But I was thinking that  that uh  s " Sure , there may be some interaction ,

but I don't think we need to be stuck on using our or OGI 's  VAD. We could use somebody else 's if it 's smaller or


Determined. I see. But I was thinking that  that uh  s " Sure , there may be some interaction ,

but I don't think we need to be stuck on using our or OGI 's  VAD. We could use somebody else 's if it 's smaller or


but I don't think we need to be stuck on using our or OGI 's  VAD. We could use somebody else 's if it 's smaller or


Uh. So there is this thing. There is um  Yeah. Uh I designed a new  a new filter because when I designed other filters with shorter delay from the LDA filters ,  there was one filter with fif sixty millisecond delay and the other with ten milliseconds


Uh. So there is this thing. There is um  Yeah. Uh I designed a new  a new filter because when I designed other filters with shorter delay from the LDA filters ,  there was one filter with fif sixty millisecond delay and the other with ten milliseconds

and  uh Hynek suggested that both could have sixty - five sixty - s I think it 's sixty - five.


and  uh Hynek suggested that both could have sixty - five sixty - s I think it 's sixty - five.

Yeah. Both should have sixty - five because


Yeah. Both should have sixty - five because

Yeah. And. So I did that and uh it 's running. So ,  let 's see what will happen. Uh but the filter is of course closer to the reference filter.


Yeah. And. So I did that and uh it 's running. So ,  let 's see what will happen. Uh but the filter is of course closer to the reference filter.


Yeah. And. So I did that and uh it 's running. So ,  let 's see what will happen. Uh but the filter is of course closer to the reference filter.

So that means logically , in principle , it should be better. So probably it 'll be worse.


So that means logically , in principle , it should be better. So probably it 'll be worse.


So that means logically , in principle , it should be better. So probably it 'll be worse.

Or in the basic perverse nature uh of reality. Yeah. OK.


Or in the basic perverse nature uh of reality. Yeah. OK.


Yeah , and then we 've started to work with this of um voiced - unvoiced stuff.


Yeah , and then we 've started to work with this of um voiced - unvoiced stuff.

And next week I think we will  perhaps try to have um a new system with uh uh MSG stream also see what  what happens. So , something that 's similar to the proposal too , but with MSG stream.


And next week I think we will  perhaps try to have um a new system with uh uh MSG stream also see what  what happens. So , something that 's similar to the proposal too , but with MSG stream.


No , I w  I begin to play  with Matlab and to found some parameter robust for voiced - unvoiced decision. But only to play. And we   they  we found that maybe w is a classical parameter , the  sq the variance  between the um FFT of the signal and the small spectrum of time  we  after the um mel filter bank.


No , I w  I begin to play  with Matlab and to found some parameter robust for voiced - unvoiced decision. But only to play. And we   they  we found that maybe w is a classical parameter , the  sq the variance  between the um FFT of the signal and the small spectrum of time  we  after the um mel filter bank.

And , well , is more or less robust. Is good for clean speech. Is quite good  for noisy speech.


And , well , is more or less robust. Is good for clean speech. Is quite good  for noisy speech.

but um we must to have bigger statistic with TIMIT ,


but um we must to have bigger statistic with TIMIT ,


Yeah. So , basically we wa want to look at something like the ex the ex excitation signal and


I have here. I have here for one signal , for one frame.


I have here. I have here for one signal , for one frame.

The  the mix of the two , noise and unnoise , and the signal is this. Clean , and this noise.


The  the mix of the two , noise and unnoise , and the signal is this. Clean , and this noise.


The  the mix of the two , noise and unnoise , and the signal is this. Clean , and this noise.

These are the two  the mixed , the big signal is for clean.


The  the mix of the two , noise and unnoise , and the signal is this. Clean , and this noise.

These are the two  the mixed , the big signal is for clean.

Well , I 'm s uh  There 's  None of these axes are labeled , so I don't know what this  What 's this axis ?


These are the two  the mixed , the big signal is for clean.

Well , I 'm s uh  There 's  None of these axes are labeled , so I don't know what this  What 's this axis ?

Uh this is uh  this axis is  nnn , " frame ".


Well , I 'm s uh  There 's  None of these axes are labeled , so I don't know what this  What 's this axis ?

Uh this is uh  this axis is  nnn , " frame ".


Uh this is uh  this axis is  nnn , " frame ".


Uh , this is uh energy , log - energy of the spectrum. Of the this is the variance , the difference  between the spectrum of the signal and FFT of each frame of the signal and this mouth spectrum of time after the f may fit for the two ,


Uh , this is uh energy , log - energy of the spectrum. Of the this is the variance , the difference  between the spectrum of the signal and FFT of each frame of the signal and this mouth spectrum of time after the f may fit for the two ,

this big , to here , they are to signal. This is for clean and this is for noise.


Uh , this is uh energy , log - energy of the spectrum. Of the this is the variance , the difference  between the spectrum of the signal and FFT of each frame of the signal and this mouth spectrum of time after the f may fit for the two ,

this big , to here , they are to signal. This is for clean and this is for noise.

Oh. There 's two things on the same graph.


this big , to here , they are to signal. This is for clean and this is for noise.

Oh. There 's two things on the same graph.

Yeah. I don't know. I  I think that I have d another graph , but I 'm not sure.


this big , to here , they are to signal. This is for clean and this is for noise.

Oh. There 's two things on the same graph.

Yeah. I don't know. I  I think that I have d another graph , but I 'm not sure.

So w which is clean and which is noise ?


Oh. There 's two things on the same graph.

Yeah. I don't know. I  I think that I have d another graph , but I 'm not sure.

So w which is clean and which is noise ?


Yeah. I don't know. I  I think that I have d another graph , but I 'm not sure.

So w which is clean and which is noise ?

The lower is noise and the height is clean.


So w which is clean and which is noise ?

The lower is noise and the height is clean.


The lower is noise and the height is clean.


So this should the  the  the t voiced portions.


So this should the  the  the t voiced portions.

Yeah , it is the height is voiced portion.


So this should the  the  the t voiced portions.

Yeah , it is the height is voiced portion.

The p the peaks should be voiced portion.


Yeah , it is the height is voiced portion.

The p the peaks should be voiced portion.


The p the peaks should be voiced portion.

And this is more or less like this. But I meant to have see @ @ two  two the picture.


And this is more or less like this. But I meant to have see @ @ two  two the picture.


the  the spectrum of the signal. And this is the small version of the spectrum after ML mel filter bank.


the  the spectrum of the signal. And this is the small version of the spectrum after ML mel filter bank.

And this is I don't know. This is not the different. This is trying to obtain  with LPC model the spectrum but using Matlab without going factor and s


And this is I don't know. This is not the different. This is trying to obtain  with LPC model the spectrum but using Matlab without going factor and s


And this is I don't know. This is not the different. This is trying to obtain  with LPC model the spectrum but using Matlab without going factor and s

Yeah so it 's  doesn't do too well there.


Yeah so it 's  doesn't do too well there.

And the  I think that this is good. This is quite similar. this is   this is another frame. ho how I obtained the  envelope ,  this envelope , with the mel filter bank.


Yeah so it 's  doesn't do too well there.

And the  I think that this is good. This is quite similar. this is   this is another frame. ho how I obtained the  envelope ,  this envelope , with the mel filter bank.

Right. So now I wonder  I mean , do you want to  I know you want to get at something orthogonal from what you get with the smooth spectrum Um. But if you were to really try and get a voiced - unvoiced , do you  do you want to totally ignore that ? I mean , do you  do you  I mean , clearly a  a very big  very big cues  for voiced - unvoiced come from uh spectral slope and so on , right ?


And the  I think that this is good. This is quite similar. this is   this is another frame. ho how I obtained the  envelope ,  this envelope , with the mel filter bank.

Right. So now I wonder  I mean , do you want to  I know you want to get at something orthogonal from what you get with the smooth spectrum Um. But if you were to really try and get a voiced - unvoiced , do you  do you want to totally ignore that ? I mean , do you  do you  I mean , clearly a  a very big  very big cues  for voiced - unvoiced come from uh spectral slope and so on , right ?


Right. So now I wonder  I mean , do you want to  I know you want to get at something orthogonal from what you get with the smooth spectrum Um. But if you were to really try and get a voiced - unvoiced , do you  do you want to totally ignore that ? I mean , do you  do you  I mean , clearly a  a very big  very big cues  for voiced - unvoiced come from uh spectral slope and so on , right ?

Yeah. Well , this would be  this would be perhaps an additional parameter ,


Yeah. Well , this would be  this would be perhaps an additional parameter ,


Yeah because when did noise clear  in these section is clear


Yeah because when did noise clear  in these section is clear

if s @ @  val value is indicative that is a voice frame and it 's low values


if s @ @  val value is indicative that is a voice frame and it 's low values

Yeah. Yeah. Well , you probably want  I mean ,  certainly if  you want to do good voiced - unvoiced detection , you need a few features. Each  each feature is  by itself not enough. But , you know , people look at  at slope and  uh first auto - correlation coefficient , divided by power.


if s @ @  val value is indicative that is a voice frame and it 's low values

Yeah. Yeah. Well , you probably want  I mean ,  certainly if  you want to do good voiced - unvoiced detection , you need a few features. Each  each feature is  by itself not enough. But , you know , people look at  at slope and  uh first auto - correlation coefficient , divided by power.

Or  or uh um there 's uh  I guess we prob probably don't have enough computation to do a simple pitch detector or something ? I mean with a pitch detector you could have a   have a  an estimate of  of what the


Yeah. Yeah. Well , you probably want  I mean ,  certainly if  you want to do good voiced - unvoiced detection , you need a few features. Each  each feature is  by itself not enough. But , you know , people look at  at slope and  uh first auto - correlation coefficient , divided by power.

Or  or uh um there 's uh  I guess we prob probably don't have enough computation to do a simple pitch detector or something ? I mean with a pitch detector you could have a   have a  an estimate of  of what the


Or  or uh um there 's uh  I guess we prob probably don't have enough computation to do a simple pitch detector or something ? I mean with a pitch detector you could have a   have a  an estimate of  of what the

Uh. Or maybe you could you just do it going through the P FFT 's figuring out some um probable  um harmonic structure. Right. And  and uh.


Uh. Or maybe you could you just do it going through the P FFT 's figuring out some um probable  um harmonic structure. Right. And  and uh.

you have read up and  you have a paper ,  the paper that you s give me yesterday. they say that yesterday  they are some  problem


Uh. Or maybe you could you just do it going through the P FFT 's figuring out some um probable  um harmonic structure. Right. And  and uh.

you have read up and  you have a paper ,  the paper that you s give me yesterday. they say that yesterday  they are some  problem

Oh , yeah. But  Yeah , but it 's not  it 's , yeah , it 's  it 's another problem.


you have read up and  you have a paper ,  the paper that you s give me yesterday. they say that yesterday  they are some  problem

Oh , yeah. But  Yeah , but it 's not  it 's , yeah , it 's  it 's another problem.


you have read up and  you have a paper ,  the paper that you s give me yesterday. they say that yesterday  they are some  problem

Oh , yeah. But  Yeah , but it 's not  it 's , yeah , it 's  it 's another problem.

Yeah Um. Yeah , there is th this fact actually. If you look at this um spectrum ,


Oh , yeah. But  Yeah , but it 's not  it 's , yeah , it 's  it 's another problem.

Yeah Um. Yeah , there is th this fact actually. If you look at this um spectrum ,


Yeah Um. Yeah , there is th this fact actually. If you look at this um spectrum ,

What 's this again ? Is it  the mel - filters ?


What 's this again ? Is it  the mel - filters ?

Yeah. OK. So the envelope here is the output of the mel - filters


Yeah. OK. So the envelope here is the output of the mel - filters

and what we clearly see is that in some cases , and it clearly appears here , and the  the harmonics are resolved by the f Well , there are still appear after mel - filtering ,


and what we clearly see is that in some cases , and it clearly appears here , and the  the harmonics are resolved by the f Well , there are still appear after mel - filtering ,

and it happens  for high pitched voice because the width of the lower frequency mel - filters  is sometimes even smaller than the pitch.


and it happens  for high pitched voice because the width of the lower frequency mel - filters  is sometimes even smaller than the pitch.

It 's around one hundred , one hundred and fifty hertz  Nnn.


It 's around one hundred , one hundred and fifty hertz  Nnn.

And so what happens is that this uh , add additional variability to this envelope and   um


And so what happens is that this uh , add additional variability to this envelope and   um

so we were thinking to modify the mel - spectrum to have something that  that 's smoother on low frequencies.


so we were thinking to modify the mel - spectrum to have something that  that 's smoother on low frequencies.


Yeah. Maybe so. Um. Yeah. So , what  Yeah. What I was talking about was just , starting with the FFT you could  you could uh do a very rough thing to estimate  estimate uh pitch.


Yeah. Maybe so. Um. Yeah. So , what  Yeah. What I was talking about was just , starting with the FFT you could  you could uh do a very rough thing to estimate  estimate uh pitch.

And uh uh , given  you know , given that , uh  you could uh uh come up with some kind of estimate of how much of the low frequency energy was  was explained by   by uh uh those harmonics.


And uh uh , given  you know , given that , uh  you could uh uh come up with some kind of estimate of how much of the low frequency energy was  was explained by   by uh uh those harmonics.

Uh. It 's uh a variant on what you 're s what you 're doing. The  I mean , the  the  the mel does give a smooth thing. But as you say it 's not that smooth here. And  and so if you   if you just you know subtracted off uh your guess of the harmonics then something like this would end up with  quite a bit lower energy in the first fifteen hundred hertz or so and  and our first kilohertz , even.


Uh. It 's uh a variant on what you 're s what you 're doing. The  I mean , the  the  the mel does give a smooth thing. But as you say it 's not that smooth here. And  and so if you   if you just you know subtracted off uh your guess of the harmonics then something like this would end up with  quite a bit lower energy in the first fifteen hundred hertz or so and  and our first kilohertz , even.

And um  if was uh noisy , the proportion that it would go down would be if it was  if it was unvoiced or something.


And um  if was uh noisy , the proportion that it would go down would be if it was  if it was unvoiced or something.

So you oughta be able to  pick out voiced segments. At least it should be another  another cue. So.  Anyway.


So you oughta be able to  pick out voiced segments. At least it should be another  another cue. So.  Anyway.

OK ? That 's what 's going on. Uh. What 's up with you ?


So you oughta be able to  pick out voiced segments. At least it should be another  another cue. So.  Anyway.

OK ? That 's what 's going on. Uh. What 's up with you ?

Um  our t I went to  talk with uh Mike Jordan this  this week


OK ? That 's what 's going on. Uh. What 's up with you ?

Um  our t I went to  talk with uh Mike Jordan this  this week


OK ? That 's what 's going on. Uh. What 's up with you ?

Um  our t I went to  talk with uh Mike Jordan this  this week

um  and uh  shared with him the ideas about um  extending the Larry Saul work and um I asked him some questions about factorial H M so like later down the line when  we 've come up with these  these feature detectors , how do we   how do we uh  you know , uh model the time series that  that happens um   and  and we talked a little bit about  factorial H M Ms and how  um when you 're doing inference  or w when you 're doing recognition , there 's like simple Viterbi stuff that you can do for   for these H M and  the uh   the great advantages that um a lot of times the factorial H M Ms don't  um  don't over - alert the problem there they have a limited number of parameters and they focus directly on   on uh the sub - problems at hand so  you can imagine  um  five or so parallel  um features um transitioning independently and then  at the end you  you uh couple these factorial H M Ms with uh   with uh undirected links um based on   based on some more data.


Um  our t I went to  talk with uh Mike Jordan this  this week

um  and uh  shared with him the ideas about um  extending the Larry Saul work and um I asked him some questions about factorial H M so like later down the line when  we 've come up with these  these feature detectors , how do we   how do we uh  you know , uh model the time series that  that happens um   and  and we talked a little bit about  factorial H M Ms and how  um when you 're doing inference  or w when you 're doing recognition , there 's like simple Viterbi stuff that you can do for   for these H M and  the uh   the great advantages that um a lot of times the factorial H M Ms don't  um  don't over - alert the problem there they have a limited number of parameters and they focus directly on   on uh the sub - problems at hand so  you can imagine  um  five or so parallel  um features um transitioning independently and then  at the end you  you uh couple these factorial H M Ms with uh   with uh undirected links um based on   based on some more data.


um  and uh  shared with him the ideas about um  extending the Larry Saul work and um I asked him some questions about factorial H M so like later down the line when  we 've come up with these  these feature detectors , how do we   how do we uh  you know , uh model the time series that  that happens um   and  and we talked a little bit about  factorial H M Ms and how  um when you 're doing inference  or w when you 're doing recognition , there 's like simple Viterbi stuff that you can do for   for these H M and  the uh   the great advantages that um a lot of times the factorial H M Ms don't  um  don't over - alert the problem there they have a limited number of parameters and they focus directly on   on uh the sub - problems at hand so  you can imagine  um  five or so parallel  um features um transitioning independently and then  at the end you  you uh couple these factorial H M Ms with uh   with uh undirected links um based on   based on some more data.

So he  he seemed  he seemed like really interested in   in um  in this and said  said this is  this is something very do - able and can learn a lot and um yeah , I 've just been  continue reading um about certain things.


So he  he seemed  he seemed like really interested in   in um  in this and said  said this is  this is something very do - able and can learn a lot and um yeah , I 've just been  continue reading um about certain things.

um thinking of maybe using um  um m modulation spectrum stuff to  um  as features um also in the  in the sub - bands


um thinking of maybe using um  um m modulation spectrum stuff to  um  as features um also in the  in the sub - bands

because  it seems like  the modulation um spectrum tells you a lot about the intelligibility of  of certain um words and stuff So , um. Yeah. Just that 's about it.


because  it seems like  the modulation um spectrum tells you a lot about the intelligibility of  of certain um words and stuff So , um. Yeah. Just that 's about it.

OK. And um so I 've been looking at Avendano 's work and um uh I 'll try to write up in my next stat status report a nice description of  what he 's doing , but it 's  it 's an approach to deal with  reverberation or that  the aspect of his work that I 'm interested in the idea is that um    normally an analysis frames are um  too short to encompass reverberation effects um in full. You miss most of the reverberation tail in a ten millisecond window and so   you  you 'd like it to be that  um  the reverberation responses um simply convolved um in , but it 's not really with these ten millisecond frames cuz you j But if you take , say , a two millisecond  um window  I 'm sorry a two second window then in a room like this , most of the reverberation response  is included in the window and the  then it um  then things are l more linear. It is  it is more like the reverberation response is simply c convolved and um   and you can use channel normalization techniques  like uh in his thesis he 's assuming that the reverberation response is fixed. He just does um  mean subtraction , which is like removing the DC component of the modulation spectrum and  that 's supposed to d um deal  uh deal pretty well with the um reverberation and um  the neat thing is you can't take these two second frames and feed them to a speech recognizer um  so he does this  um  method training trading the um  the spectral resolution for time resolution  and um  come ca uh synthesizes a new representation which is with say ten second frames but a lower s um  frequency resolution. So I don't really know the theory. I guess it 's  these are called " time frequency representations " and h he 's making the  the time sh um finer grained and the frequency resolution um less fine grained.


OK. And um so I 've been looking at Avendano 's work and um uh I 'll try to write up in my next stat status report a nice description of  what he 's doing , but it 's  it 's an approach to deal with  reverberation or that  the aspect of his work that I 'm interested in the idea is that um    normally an analysis frames are um  too short to encompass reverberation effects um in full. You miss most of the reverberation tail in a ten millisecond window and so   you  you 'd like it to be that  um  the reverberation responses um simply convolved um in , but it 's not really with these ten millisecond frames cuz you j But if you take , say , a two millisecond  um window  I 'm sorry a two second window then in a room like this , most of the reverberation response  is included in the window and the  then it um  then things are l more linear. It is  it is more like the reverberation response is simply c convolved and um   and you can use channel normalization techniques  like uh in his thesis he 's assuming that the reverberation response is fixed. He just does um  mean subtraction , which is like removing the DC component of the modulation spectrum and  that 's supposed to d um deal  uh deal pretty well with the um reverberation and um  the neat thing is you can't take these two second frames and feed them to a speech recognizer um  so he does this  um  method training trading the um  the spectral resolution for time resolution  and um  come ca uh synthesizes a new representation which is with say ten second frames but a lower s um  frequency resolution. So I don't really know the theory. I guess it 's  these are called " time frequency representations " and h he 's making the  the time sh um finer grained and the frequency resolution um less fine grained.

s so I 'm  I guess my first stab actually in continuing  his work is to um  re - implement this  this thing which um  changes the time and frequency resolutions cuz he doesn't have code for me. So that that 'll take some reading about the theory. I don't really know the theory.


s so I 'm  I guess my first stab actually in continuing  his work is to um  re - implement this  this thing which um  changes the time and frequency resolutions cuz he doesn't have code for me. So that that 'll take some reading about the theory. I don't really know the theory.

Oh , and um ,  another f first step is um , so the  the way I want to extend his work is make it able to deal with a time varying reverberation response um  and um we don't really know  how fast the um  the reverberation response is varying the Meeting Recorder data um so um  we  we have this um block least squares um imp echo canceller implementation and um  I want to try  finding  the  the response , say , between a near mike and the table mike for someone using the echo canceller and looking at the echo canceller taps and then  see how fast that varies  from block to block.


Oh , and um ,  another f first step is um , so the  the way I want to extend his work is make it able to deal with a time varying reverberation response um  and um we don't really know  how fast the um  the reverberation response is varying the Meeting Recorder data um so um  we  we have this um block least squares um imp echo canceller implementation and um  I want to try  finding  the  the response , say , between a near mike and the table mike for someone using the echo canceller and looking at the echo canceller taps and then  see how fast that varies  from block to block.

That should give an idea of how fast the reverberation response is changing.


That should give an idea of how fast the reverberation response is changing.


Um. S so um y you do  I think you read some of the  the zeros as O 's and some as zeros.


Um. S so um y you do  I think you read some of the  the zeros as O 's and some as zeros.

Is there a particular way we 're supposed to read them ?


Is there a particular way we 're supposed to read them ?

No. " O "  " O "  " O " " O "  " O "  " O " and " zero " are two ways that we say that digit.


No. " O "  " O "  " O " " O "  " O "  " O " and " zero " are two ways that we say that digit.


Perhaps in the sheets there should be another sign for the  if we want to  the  the guy to say " O " or


Perhaps in the sheets there should be another sign for the  if we want to  the  the guy to say " O " or

No. I mean. I think people will do what they say.


No. I mean. I think people will do what they say.


I mean in digit recognition we 've done before , you have  you have two pronunciations for that value , " O " and " zero ".


But it 's perhaps more difficult for the people to prepare the database then , if  because here you only have zeros


But it 's perhaps more difficult for the people to prepare the database then , if  because here you only have zeros

they  they write down OH. or they write down ZERO a and they  and they each have their own pronunciation.


they  they write down OH. or they write down ZERO a and they  and they each have their own pronunciation.

Yeah but if the sh the sheet was prepared with a different sign for the " O ".


they  they write down OH. or they write down ZERO a and they  and they each have their own pronunciation.

Yeah but if the sh the sheet was prepared with a different sign for the " O ".

But people wouldn't know what that wa I mean  there is no convention for it.


Yeah but if the sh the sheet was prepared with a different sign for the " O ".

But people wouldn't know what that wa I mean  there is no convention for it.

See. I mean , you 'd have to tell them  " OK when we write this , say it tha " ,


But people wouldn't know what that wa I mean  there is no convention for it.

See. I mean , you 'd have to tell them  " OK when we write this , say it tha " ,


See. I mean , you 'd have to tell them  " OK when we write this , say it tha " ,

you know , and you just  They just want people to read the digits as you ordinarily would


you know , and you just  They just want people to read the digits as you ordinarily would


OK. Is this a change from the last batch of  of um forms ? Because in the last batch it was spelled out which one you should read.


OK. Is this a change from the last batch of  of um forms ? Because in the last batch it was spelled out which one you should read.

Yes. That 's right. It was  it was spelled out , and they decided they wanted to get at more the way people would really say things.


Yes. That 's right. It was  it was spelled out , and they decided they wanted to get at more the way people would really say things.

That 's also why they 're  they 're bunched together in these different groups. So  so it 's


That 's also why they 're  they 're bunched together in these different groups. So  so it 's

Yeah. So it 's  it 's  Everything 's fine.


Yeah. So it 's  it 's  Everything 's fine.

OK. Actually , let me just s since  since you brought it up , I was just  it was hard not to be self - conscious about that when it  after we  since we just discussed it. But I realized that  that um  when I 'm talking on the phone , certainly , and  and saying these numbers ,  I almost always say zero. And uh  cuz  because uh i it 's two syllables. It 's  it 's more likely they 'll understand what I said. So that  that  that 's the habit I 'm in , but some people say " O " and


Yeah. So it 's  it 's  Everything 's fine.

OK. Actually , let me just s since  since you brought it up , I was just  it was hard not to be self - conscious about that when it  after we  since we just discussed it. But I realized that  that um  when I 'm talking on the phone , certainly , and  and saying these numbers ,  I almost always say zero. And uh  cuz  because uh i it 's two syllables. It 's  it 's more likely they 'll understand what I said. So that  that  that 's the habit I 'm in , but some people say " O " and

Yeah I normally say " O " cuz it 's easier to say.


OK. Actually , let me just s since  since you brought it up , I was just  it was hard not to be self - conscious about that when it  after we  since we just discussed it. But I realized that  that um  when I 'm talking on the phone , certainly , and  and saying these numbers ,  I almost always say zero. And uh  cuz  because uh i it 's two syllables. It 's  it 's more likely they 'll understand what I said. So that  that  that 's the habit I 'm in , but some people say " O " and

Yeah I normally say " O " cuz it 's easier to say.

Yeah it 's shorter. Yeah. So it 's  So.  So uh.


Yeah I normally say " O " cuz it 's easier to say.

Yeah it 's shorter. Yeah. So it 's  So.  So uh.


Yeah it 's shorter. Yeah. So it 's  So.  So uh.


I 'm  What ? Really ? Oh , that 's horrible ! Disincentive !


Channel fi OK. OK. Are you doing something ? OK , then I guess I 'm doing something. So , um , So basically the result of m much thinking since the last time we met , um , but not as much writing , um , is a sheet that I have a lot of , like , thoughts and justification of comments on but I 'll just pass out as is right now. So , um , here. If you could pass this around ? And there 's two things. And so one on one side is  on one side is a sort of the revised sort of updated semantic specification.


Channel fi OK. OK. Are you doing something ? OK , then I guess I 'm doing something. So , um , So basically the result of m much thinking since the last time we met , um , but not as much writing , um , is a sheet that I have a lot of , like , thoughts and justification of comments on but I 'll just pass out as is right now. So , um , here. If you could pass this around ? And there 's two things. And so one on one side is  on one side is a sort of the revised sort of updated semantic specification.

And the other side is , um , sort of a revised construction formalism.


And the other side is , um , sort of a revised construction formalism.


Um , Enough to go around ? OK. And in some ways it 's  it 's  it 's very similar to  There are very few changes in some ways from what we 've , um , uh , b done before but I don't think everyone here has seen all of this. So , uh , I 'm not sure where to begin. Um , as usual the disclaimers are there are  all these things are  it 's only slightly more stable than it was before.


Um , Enough to go around ? OK. And in some ways it 's  it 's  it 's very similar to  There are very few changes in some ways from what we 've , um , uh , b done before but I don't think everyone here has seen all of this. So , uh , I 'm not sure where to begin. Um , as usual the disclaimers are there are  all these things are  it 's only slightly more stable than it was before.

And , um , after a little bit more discussion and especially like Keith and I  I have more linguistic things to settle in the next few days , um , it 'll probably change again some more.


And , um , after a little bit more discussion and especially like Keith and I  I have more linguistic things to settle in the next few days , um , it 'll probably change again some more.

Um , maybe I will  let 's start b let 's start on number two actually on the notation , um , because that 's , I 'm thinking , possibly a little more familiar to , um  to people. OK , so the top block is just sort of a  sort of abstract nota it 's sort of like , um , listings of the kinds of things that we can have. And certain things that have , um , changed , have changed back to this. There  there 's been a little bit of , um , going back and forth. But basically obviously all constructions have some kind of name. I forgot to include that you could have a type included in this line.


Um , maybe I will  let 's start b let 's start on number two actually on the notation , um , because that 's , I 'm thinking , possibly a little more familiar to , um  to people. OK , so the top block is just sort of a  sort of abstract nota it 's sort of like , um , listings of the kinds of things that we can have. And certain things that have , um , changed , have changed back to this. There  there 's been a little bit of , um , going back and forth. But basically obviously all constructions have some kind of name. I forgot to include that you could have a type included in this line.

So something like , um  Well , there 's an example  the textual example at the end has clausal construction. So , um , just to show it doesn't have to be beautiful It could be , you know , simple old text as well. Um , there are a couple of  Uh , these three have various ways of doing certain things. So I 'll just try to go through them. So they could all have a type at the beginning. Um , and then they say the key word construction


So something like , um  Well , there 's an example  the textual example at the end has clausal construction. So , um , just to show it doesn't have to be beautiful It could be , you know , simple old text as well. Um , there are a couple of  Uh , these three have various ways of doing certain things. So I 'll just try to go through them. So they could all have a type at the beginning. Um , and then they say the key word construction


So something like , um  Well , there 's an example  the textual example at the end has clausal construction. So , um , just to show it doesn't have to be beautiful It could be , you know , simple old text as well. Um , there are a couple of  Uh , these three have various ways of doing certain things. So I 'll just try to go through them. So they could all have a type at the beginning. Um , and then they say the key word construction

So  so the current syntax is if it s if there 's a type it 's before construct


So  so the current syntax is if it s if there 's a type it 's before construct


So  so the current syntax is if it s if there 's a type it 's before construct

OK , and then it has a block that is constituents. And as usual I guess all the constructions her all the examples here have only , um , tsk  one type of constituent , that is a constructional constituent. I think that 's actually gonna turn out to m be certainly the most common kind. But in general instead of the word " construct " , th here you might have " meaning " or " form " as well. OK ? So if there 's some element that doesn't  that isn't yet constructional in the sense that it maps form and meaning. OK , um , the main change with the constructs which  each of which has , um , the key word " construct " and then some name , and then some type specification , is that it 's  it 's pro it 's often  sometimes the case in the first case here that you know what kind of construction it is. So for example whatever I have here is gonna be a form of the word " throw " , or it 's gonna be a form of the word , you know , I don't know , " happy " , or something like that. Or , you know , some it 'll be a specific word or maybe you 'll have the type. You 'll say " I need a p uh spatial relation phrase here " or " I need a directional specifier here ". So - uh you could have a j a actual type here. Um , or you could just say in the second case that you only know the meaning type. So a very common example of this is that , you know , in directed motion , the first person to do something should be an agent of some kind , often a human. Right ? So if I  you know , the um , uh , run down the street then I  I  I run down the street , it 's typed , uh , " I " , meaning category is what 's there. The  the new kind is this one that is sort of a pair and , um , sort of skipping fonts and whatever. The idea is that sometimes there are , um , general constructions that you know , that you 're going to need. It 's  it 's the equivalent of a noun phrase or a prepositional phrase , or something like that there.


OK , and then it has a block that is constituents. And as usual I guess all the constructions her all the examples here have only , um , tsk  one type of constituent , that is a constructional constituent. I think that 's actually gonna turn out to m be certainly the most common kind. But in general instead of the word " construct " , th here you might have " meaning " or " form " as well. OK ? So if there 's some element that doesn't  that isn't yet constructional in the sense that it maps form and meaning. OK , um , the main change with the constructs which  each of which has , um , the key word " construct " and then some name , and then some type specification , is that it 's  it 's pro it 's often  sometimes the case in the first case here that you know what kind of construction it is. So for example whatever I have here is gonna be a form of the word " throw " , or it 's gonna be a form of the word , you know , I don't know , " happy " , or something like that. Or , you know , some it 'll be a specific word or maybe you 'll have the type. You 'll say " I need a p uh spatial relation phrase here " or " I need a directional specifier here ". So - uh you could have a j a actual type here. Um , or you could just say in the second case that you only know the meaning type. So a very common example of this is that , you know , in directed motion , the first person to do something should be an agent of some kind , often a human. Right ? So if I  you know , the um , uh , run down the street then I  I  I run down the street , it 's typed , uh , " I " , meaning category is what 's there. The  the new kind is this one that is sort of a pair and , um , sort of skipping fonts and whatever. The idea is that sometimes there are , um , general constructions that you know , that you 're going to need. It 's  it 's the equivalent of a noun phrase or a prepositional phrase , or something like that there.


OK , and then it has a block that is constituents. And as usual I guess all the constructions her all the examples here have only , um , tsk  one type of constituent , that is a constructional constituent. I think that 's actually gonna turn out to m be certainly the most common kind. But in general instead of the word " construct " , th here you might have " meaning " or " form " as well. OK ? So if there 's some element that doesn't  that isn't yet constructional in the sense that it maps form and meaning. OK , um , the main change with the constructs which  each of which has , um , the key word " construct " and then some name , and then some type specification , is that it 's  it 's pro it 's often  sometimes the case in the first case here that you know what kind of construction it is. So for example whatever I have here is gonna be a form of the word " throw " , or it 's gonna be a form of the word , you know , I don't know , " happy " , or something like that. Or , you know , some it 'll be a specific word or maybe you 'll have the type. You 'll say " I need a p uh spatial relation phrase here " or " I need a directional specifier here ". So - uh you could have a j a actual type here. Um , or you could just say in the second case that you only know the meaning type. So a very common example of this is that , you know , in directed motion , the first person to do something should be an agent of some kind , often a human. Right ? So if I  you know , the um , uh , run down the street then I  I  I run down the street , it 's typed , uh , " I " , meaning category is what 's there. The  the new kind is this one that is sort of a pair and , um , sort of skipping fonts and whatever. The idea is that sometimes there are , um , general constructions that you know , that you 're going to need. It 's  it 's the equivalent of a noun phrase or a prepositional phrase , or something like that there.

And usually it has formal um , considerations that will go along with it.


And usually it has formal um , considerations that will go along with it.

And then uh , you might know something much more specific depending on what construction you 're talking about , about what meaning  what specific meaning you want. So the example again at the bottom , which is directed motion , you might need a nominal expression to take the place of , you know , um , " the big th " , you you know , " the big  the tall dark man " , you know , " walked into the room ".


And then uh , you might know something much more specific depending on what construction you 're talking about , about what meaning  what specific meaning you want. So the example again at the bottom , which is directed motion , you might need a nominal expression to take the place of , you know , um , " the big th " , you you know , " the big  the tall dark man " , you know , " walked into the room ".

But because of the nature of this particular construction you know not just that it 's nominal of some kind but in particular , that it 's some kind of animate nominal , and which will apply just as well to like , you know , a per you know , a simple proper noun or to some complicated expression. Um , so I don't know if the syntax will hold but something that gives you a way to do both constructional and meaning types. So. OK , then I don't think the ,  um  at least  Yeah.  None of these examples have anything different for formal constraints ? But you can refer to any of the , um , sort of available elements and scope , right ? which here are the constructs ,  to say something about the relation. And I think i if you not if you compare like the top block and the textual block , um , we dropped like the little F subscript. The F subscripts refer to the " form " piece of the construct.


But because of the nature of this particular construction you know not just that it 's nominal of some kind but in particular , that it 's some kind of animate nominal , and which will apply just as well to like , you know , a per you know , a simple proper noun or to some complicated expression. Um , so I don't know if the syntax will hold but something that gives you a way to do both constructional and meaning types. So. OK , then I don't think the ,  um  at least  Yeah.  None of these examples have anything different for formal constraints ? But you can refer to any of the , um , sort of available elements and scope , right ? which here are the constructs ,  to say something about the relation. And I think i if you not if you compare like the top block and the textual block , um , we dropped like the little F subscript. The F subscripts refer to the " form " piece of the construct.

And I think that , um , in general it 'll be unambiguous. Like if you were giving a formal constraint then you 're referring to the formal pole of that. So  so by saying  if I just said " Name one " then that means name one formal and we 're talking about formal struc  Which  which makes sense. Uh , there are certain times when we 'll have an exception to that , in which case you could just indicate " here I mean the meaningful for some reason ". Right ? Or  Actually it 's more often that , only to handle this one special case of , you know , " George and Jerry walk into the room in that order ".


And I think that , um , in general it 'll be unambiguous. Like if you were giving a formal constraint then you 're referring to the formal pole of that. So  so by saying  if I just said " Name one " then that means name one formal and we 're talking about formal struc  Which  which makes sense. Uh , there are certain times when we 'll have an exception to that , in which case you could just indicate " here I mean the meaningful for some reason ". Right ? Or  Actually it 's more often that , only to handle this one special case of , you know , " George and Jerry walk into the room in that order ".

So we have a few funny things where something in the meaning might refer to something in the form. But  but s we 're not gonna really worry about that for right now and there are way We can be more specific if we have to later on. OK , and so in terms of the  the relations , you know , as usual they 're before and ends. I should have put an example in of something that isn't an interval relation but in form you might also have a value binding. You know , you could say that , um , you know , " name - one dot " , t you know , " number equals " , you know , a plural or something like that.


So we have a few funny things where something in the meaning might refer to something in the form. But  but s we 're not gonna really worry about that for right now and there are way We can be more specific if we have to later on. OK , and so in terms of the  the relations , you know , as usual they 're before and ends. I should have put an example in of something that isn't an interval relation but in form you might also have a value binding. You know , you could say that , um , you know , " name - one dot " , t you know , " number equals " , you know , a plural or something like that.

There are certain things that are attribute - value , similar to the bindings below but I mean they 're just  us usually they 're going to be value  value fillers , right ? OK , and then again semantic constraints here are just  are just bindings. There was talk of changing the name of that. And Johno and I  I  you  you and I can like fight about that if you like ? but about changing it to " semantic  n effects " , which I thought was a little bit too order - biased


There are certain things that are attribute - value , similar to the bindings below but I mean they 're just  us usually they 're going to be value  value fillers , right ? OK , and then again semantic constraints here are just  are just bindings. There was talk of changing the name of that. And Johno and I  I  you  you and I can like fight about that if you like ? but about changing it to " semantic  n effects " , which I thought was a little bit too order - biased

and " semantic bindings " , which I thought might be too restrictive in case we don't have only bindings. And so it was an issue whether constraints  um , there were some linguists who reacted against " constraints " , saying , " oh , if it 's not used for matching , then it shouldn't be called a constraint ". But I think we want to be uncommitted about whether it 's used for matching or not. Right ? Cuz there are  I think we thought of some situations where it would be useful to use whatever the c bindings are , for actual , you know , sort of like modified constraining purposes.