and  and it was  uh , I mean , there the point of interest to the group was primarily that , um ,  the , uh  the system that we had that was based on H T K , that 's used by , you know ,  all the participants in Aurora ,  was so much worse  than the  than the S R

And the interesting thing is that even though ,  yes , it 's a digits task and that 's a relatively small number of words and there 's a bunch of digits that you train on ,  it 's just not as good as having a  a l very large amount of data and training up a  a  a nice good big  HMM. Um , also you had the adaptation in the SRI system , which we didn't have in this. Um. So. Um.


and  and it was  uh , I mean , there the point of interest to the group was primarily that , um ,  the , uh  the system that we had that was based on H T K , that 's used by , you know ,  all the participants in Aurora ,  was so much worse  than the  than the S R

And the interesting thing is that even though ,  yes , it 's a digits task and that 's a relatively small number of words and there 's a bunch of digits that you train on ,  it 's just not as good as having a  a l very large amount of data and training up a  a  a nice good big  HMM. Um , also you had the adaptation in the SRI system , which we didn't have in this. Um. So. Um.

And we know  Di - did I send you some results without adaptation ?


And the interesting thing is that even though ,  yes , it 's a digits task and that 's a relatively small number of words and there 's a bunch of digits that you train on ,  it 's just not as good as having a  a l very large amount of data and training up a  a  a nice good big  HMM. Um , also you had the adaptation in the SRI system , which we didn't have in this. Um. So. Um.

And we know  Di - did I send you some results without adaptation ?


And the interesting thing is that even though ,  yes , it 's a digits task and that 's a relatively small number of words and there 's a bunch of digits that you train on ,  it 's just not as good as having a  a l very large amount of data and training up a  a  a nice good big  HMM. Um , also you had the adaptation in the SRI system , which we didn't have in this. Um. So. Um.

And we know  Di - did I send you some results without adaptation ?

I s I think Stephane , uh , had seen them.


And we know  Di - did I send you some results without adaptation ?

I s I think Stephane , uh , had seen them.

Or if you did , I didn't include them , cuz it was


I s I think Stephane , uh , had seen them.

Or if you did , I didn't include them , cuz it was


I s I think Stephane , uh , had seen them.

Or if you did , I didn't include them , cuz it was

Yeah , I think I did , actually. So there was a significant loss from not doing the adaptation.


Or if you did , I didn't include them , cuz it was

Yeah , I think I did , actually. So there was a significant loss from not doing the adaptation.


Yeah , I think I did , actually. So there was a significant loss from not doing the adaptation.

Um. A  a  a couple percent or some I mean  Well , I don't know it  Overall  Uh , I  I don't remember , but there was   there was a significant , um , loss or win  from adaptation  with  with adaptation. And , um , that was the phone - loop adaptation. And then there was a very small  like point one percent on the natives  uh , win from doing , um , you know , adaptation to  the recognition hypotheses. And  I tried both means adaptation and means and variances , and the variances added another  or subtracted another point one percent. So ,  it 's , um  that 's the number there. Point six , I believe , is what you get with both , uh , means and variance adaptation.


Um. A  a  a couple percent or some I mean  Well , I don't know it  Overall  Uh , I  I don't remember , but there was   there was a significant , um , loss or win  from adaptation  with  with adaptation. And , um , that was the phone - loop adaptation. And then there was a very small  like point one percent on the natives  uh , win from doing , um , you know , adaptation to  the recognition hypotheses. And  I tried both means adaptation and means and variances , and the variances added another  or subtracted another point one percent. So ,  it 's , um  that 's the number there. Point six , I believe , is what you get with both , uh , means and variance adaptation.

But I think one thing is that , uh , I would presume  Hav - Have you ever t  Have you ever tried this exact same recognizer out on the actual TI - digits test set ?


But I think one thing is that , uh , I would presume  Hav - Have you ever t  Have you ever tried this exact same recognizer out on the actual TI - digits test set ?

It might be interesting to do that. Cuz my  my  cuz my sense , um


But I think one thing is that , uh , I would presume  Hav - Have you ever t  Have you ever tried this exact same recognizer out on the actual TI - digits test set ?

It might be interesting to do that. Cuz my  my  cuz my sense , um

But  but , I have  I mean , people  people at SRI are actually working on digits.


It might be interesting to do that. Cuz my  my  cuz my sense , um

But  but , I have  I mean , people  people at SRI are actually working on digits.


It might be interesting to do that. Cuz my  my  cuz my sense , um

But  but , I have  I mean , people  people at SRI are actually working on digits.

I could  and they are using a system that 's , um  you know , h is actually trained on digits , um , but h h otherwise uses the same , you know , decoder , the same , uh , training methods , and so forth ,


But  but , I have  I mean , people  people at SRI are actually working on digits.

I could  and they are using a system that 's , um  you know , h is actually trained on digits , um , but h h otherwise uses the same , you know , decoder , the same , uh , training methods , and so forth ,


I could  and they are using a system that 's , um  you know , h is actually trained on digits , um , but h h otherwise uses the same , you know , decoder , the same , uh , training methods , and so forth ,

and I could ask them what they get  on TI - digits.


I could  and they are using a system that 's , um  you know , h is actually trained on digits , um , but h h otherwise uses the same , you know , decoder , the same , uh , training methods , and so forth ,

and I could ask them what they get  on TI - digits.

Yeah , bu although I 'd be  I think it 'd be interesting to just take this exact actual system so that these numbers were comparable


and I could ask them what they get  on TI - digits.

Yeah , bu although I 'd be  I think it 'd be interesting to just take this exact actual system so that these numbers were comparable


Yeah , bu although I 'd be  I think it 'd be interesting to just take this exact actual system so that these numbers were comparable


Yeah. Yeah. Cuz our sense from the other  from the Aurora , uh , task is that


Yeah. Yeah. Cuz our sense from the other  from the Aurora , uh , task is that

I mean , cuz we were getting sub one percent  numbers on TI - digits also with the tandem thing.


I mean , cuz we were getting sub one percent  numbers on TI - digits also with the tandem thing.


I mean , cuz we were getting sub one percent  numbers on TI - digits also with the tandem thing.

So ,  one  so there were a number of things we noted from this.


So ,  one  so there were a number of things we noted from this.

One is , yeah , the SRI system is a lot better than the HTK


One is , yeah , the SRI system is a lot better than the HTK

this , you know , very limited training HTK system.


this , you know , very limited training HTK system.

Uh , but the other is that , um , the digits  recorded here in this room with these close mikes , i uh , are actually a lot harder than the  studio - recording TI - digits. I think , you know , one reason for that , uh , might be that there 's still  even though it 's close - talking , there still is some noise and some room acoustics.


Uh , but the other is that , um , the digits  recorded here in this room with these close mikes , i uh , are actually a lot harder than the  studio - recording TI - digits. I think , you know , one reason for that , uh , might be that there 's still  even though it 's close - talking , there still is some noise and some room acoustics.

And another might be that , uh , I 'd  I would presume that in the studio , uh , uh , situation recording read speech that if somebody did something a little funny or n pronounced something a little funny or made a little  that they didn't include it ,


And another might be that , uh , I 'd  I would presume that in the studio , uh , uh , situation recording read speech that if somebody did something a little funny or n pronounced something a little funny or made a little  that they didn't include it ,


And another might be that , uh , I 'd  I would presume that in the studio , uh , uh , situation recording read speech that if somebody did something a little funny or n pronounced something a little funny or made a little  that they didn't include it ,

Whereas , I took out  the ones that I noticed that were blatant  that were correctable.


Whereas , I took out  the ones that I noticed that were blatant  that were correctable.


Whereas , I took out  the ones that I noticed that were blatant  that were correctable.

So that , if someone just read the wrong digit , I corrected it.


So that , if someone just read the wrong digit , I corrected it.

And then there was another one where Jose couldn't tell whether  I couldn't tell whether he was saying zero or six. And I asked him and he couldn't tell either.


And then there was another one where Jose couldn't tell whether  I couldn't tell whether he was saying zero or six. And I asked him and he couldn't tell either.


You know , so I just e edited out the first , i uh , word of the utterance. Um , so there 's a little bit of correction but it 's definitely not as clean as TI - digits. So my expectations is TI - digits would , especially  I think TI - digits is all  American English.


You know , so I just e edited out the first , i uh , word of the utterance. Um , so there 's a little bit of correction but it 's definitely not as clean as TI - digits. So my expectations is TI - digits would , especially  I think TI - digits is all  American English.

Right ? So it would probably do even a little better still on the SRI system , but we could give it a try.


You know , so I just e edited out the first , i uh , word of the utterance. Um , so there 's a little bit of correction but it 's definitely not as clean as TI - digits. So my expectations is TI - digits would , especially  I think TI - digits is all  American English.

Right ? So it would probably do even a little better still on the SRI system , but we could give it a try.

Well. But  remember , we 're using a telephone bandwidth front - end here , uh , on this , uh  on this SRI system , so ,  um , I was  I thought that maybe that 's actually a good thing because it  it gets rid of some of the  uh , the noises , um , you know , in the  the  below and above the  um , the , you know , speech bandwidth


Right ? So it would probably do even a little better still on the SRI system , but we could give it a try.

Well. But  remember , we 're using a telephone bandwidth front - end here , uh , on this , uh  on this SRI system , so ,  um , I was  I thought that maybe that 's actually a good thing because it  it gets rid of some of the  uh , the noises , um , you know , in the  the  below and above the  um , the , you know , speech bandwidth


Right ? So it would probably do even a little better still on the SRI system , but we could give it a try.

Well. But  remember , we 're using a telephone bandwidth front - end here , uh , on this , uh  on this SRI system , so ,  um , I was  I thought that maybe that 's actually a good thing because it  it gets rid of some of the  uh , the noises , um , you know , in the  the  below and above the  um , the , you know , speech bandwidth

and , um , I suspect that to get sort of the last bit out of these higher - quality recordings you would have to in fact , uh , use models that , uh , were trained on wider - band data. And of course we can't do that or


Well. But  remember , we 're using a telephone bandwidth front - end here , uh , on this , uh  on this SRI system , so ,  um , I was  I thought that maybe that 's actually a good thing because it  it gets rid of some of the  uh , the noises , um , you know , in the  the  below and above the  um , the , you know , speech bandwidth

and , um , I suspect that to get sort of the last bit out of these higher - quality recordings you would have to in fact , uh , use models that , uh , were trained on wider - band data. And of course we can't do that or


and , um , I suspect that to get sort of the last bit out of these higher - quality recordings you would have to in fact , uh , use models that , uh , were trained on wider - band data. And of course we can't do that or

It 's wide - band , yeah. It 's  in  in fact , we looked it up


It 's wide - band , yeah. It 's  in  in fact , we looked it up

and it was actually twenty kilohertz sampling.


It 's wide - band , yeah. It 's  in  in fact , we looked it up

and it was actually twenty kilohertz sampling.

Oh , that 's right. I  I did look that up.


and it was actually twenty kilohertz sampling.

Oh , that 's right. I  I did look that up.


and it was actually twenty kilohertz sampling.

Oh , that 's right. I  I did look that up.

I couldn't remember whether that was TI - digits or one of the other digit tasks.


Oh , that 's right. I  I did look that up.

I couldn't remember whether that was TI - digits or one of the other digit tasks.


I couldn't remember whether that was TI - digits or one of the other digit tasks.

Right. But  but , I would  Yeah. It 's  it 's easy enough to try , just run it on


Right. But  but , I would  Yeah. It 's  it 's easy enough to try , just run it on


So , Morgan , you 're getting a little breath noise.


So , Morgan , you 're getting a little breath noise.

You might wanna move the mike down a little bit.


So , Morgan , you 're getting a little breath noise.

You might wanna move the mike down a little bit.

one  one issue  one issue with  with that is that  um , the system has this , uh , notion of a speaker to  which is used in adaptation , variance norm uh , you know , both in , uh , mean and variance normalization and also in the VTL  estimation.


You might wanna move the mike down a little bit.

one  one issue  one issue with  with that is that  um , the system has this , uh , notion of a speaker to  which is used in adaptation , variance norm uh , you know , both in , uh , mean and variance normalization and also in the VTL  estimation.


one  one issue  one issue with  with that is that  um , the system has this , uh , notion of a speaker to  which is used in adaptation , variance norm uh , you know , both in , uh , mean and variance normalization and also in the VTL  estimation.

Yeah , I noticed the script that extracted it.


Yeah , I noticed the script that extracted it.

Do y ? Is  ? So does  so th so does  does , um ,  the TI - digits database have speakers that are known ?


Yeah , I noticed the script that extracted it.

Do y ? Is  ? So does  so th so does  does , um ,  the TI - digits database have speakers that are known ?

And is there  is there enough data or a comparable  comparable amount of data to  to what we have in our recordings here ?


Do y ? Is  ? So does  so th so does  does , um ,  the TI - digits database have speakers that are known ?

And is there  is there enough data or a comparable  comparable amount of data to  to what we have in our recordings here ?

That I don't know. I don't know. I don't know how many speakers there are ,


And is there  is there enough data or a comparable  comparable amount of data to  to what we have in our recordings here ?

That I don't know. I don't know. I don't know how many speakers there are ,


And is there  is there enough data or a comparable  comparable amount of data to  to what we have in our recordings here ?

That I don't know. I don't know. I don't know how many speakers there are ,

and  and how many speakers per utterance.


That I don't know. I don't know. I don't know how many speakers there are ,

and  and how many speakers per utterance.


and  and how many speakers per utterance.

Well , the other thing would be to do it without the adaptation and compare to these numbers without the adaptation. That would


and  and how many speakers per utterance.

Well , the other thing would be to do it without the adaptation and compare to these numbers without the adaptation. That would

Right. Uh , but I 'm not so much worried about the adaptation , actually , than  than the , um ,  um  the , uh , VTL estimation.


Well , the other thing would be to do it without the adaptation and compare to these numbers without the adaptation. That would

Right. Uh , but I 'm not so much worried about the adaptation , actually , than  than the , um ,  um  the , uh , VTL estimation.


Well , the other thing would be to do it without the adaptation and compare to these numbers without the adaptation. That would

Right. Uh , but I 'm not so much worried about the adaptation , actually , than  than the , um ,  um  the , uh , VTL estimation.

If you have only one utterance per speaker you might actually screw up on estimating the  the warping , uh , factor. So , um


Right. Uh , but I 'm not so much worried about the adaptation , actually , than  than the , um ,  um  the , uh , VTL estimation.

If you have only one utterance per speaker you might actually screw up on estimating the  the warping , uh , factor. So , um

I strongly suspect that they have more speakers than we do. So , uh


If you have only one utterance per speaker you might actually screw up on estimating the  the warping , uh , factor. So , um

I strongly suspect that they have more speakers than we do. So , uh

Right. But it 's not the amount of speakers , it 's the num it 's the amount of data per speaker.


If you have only one utterance per speaker you might actually screw up on estimating the  the warping , uh , factor. So , um

I strongly suspect that they have more speakers than we do. So , uh

Right. But it 's not the amount of speakers , it 's the num it 's the amount of data per speaker.

Right. So we  we could probably do an extraction that was roughly equivalent.


I strongly suspect that they have more speakers than we do. So , uh

Right. But it 's not the amount of speakers , it 's the num it 's the amount of data per speaker.

Right. So we  we could probably do an extraction that was roughly equivalent.


Right. But it 's not the amount of speakers , it 's the num it 's the amount of data per speaker.

Right. So we  we could probably do an extraction that was roughly equivalent.


Right. So we  we could probably do an extraction that was roughly equivalent.


So , although I  I sort of know how to run it , there are a little  a f few details here and there that I 'll have to  dig out.


So , although I  I sort of know how to run it , there are a little  a f few details here and there that I 'll have to  dig out.

OK. The key  So th the system actually extracts the speaker ID from the waveform names.


So , although I  I sort of know how to run it , there are a little  a f few details here and there that I 'll have to  dig out.

OK. The key  So th the system actually extracts the speaker ID from the waveform names.

And there 's a  there 's a script  and that is actually all in one script. So there 's this one script that parses waveform names and extracts things like the , um , speaker , uh , ID or something that can stand in as a speaker ID. So , we might have to modify that script to recognize the , um , speakers ,  um , in the  in the , uh , um ,  TI - digits  database.


OK. The key  So th the system actually extracts the speaker ID from the waveform names.

And there 's a  there 's a script  and that is actually all in one script. So there 's this one script that parses waveform names and extracts things like the , um , speaker , uh , ID or something that can stand in as a speaker ID. So , we might have to modify that script to recognize the , um , speakers ,  um , in the  in the , uh , um ,  TI - digits  database.


And there 's a  there 's a script  and that is actually all in one script. So there 's this one script that parses waveform names and extracts things like the , um , speaker , uh , ID or something that can stand in as a speaker ID. So , we might have to modify that script to recognize the , um , speakers ,  um , in the  in the , uh , um ,  TI - digits  database.

Or you can fake  you can fake  names for these waveforms that resemble the names that we use here for the  for the meetings.


Or you can fake  you can fake  names for these waveforms that resemble the names that we use here for the  for the meetings.

That would be the , sort of  probably the safest way to do


Or you can fake  you can fake  names for these waveforms that resemble the names that we use here for the  for the meetings.

That would be the , sort of  probably the safest way to do

I might have to do that anyway to  to do  because we may have to do an extract to get the  amount of data per speaker about right.


That would be the , sort of  probably the safest way to do

I might have to do that anyway to  to do  because we may have to do an extract to get the  amount of data per speaker about right.


That would be the , sort of  probably the safest way to do

I might have to do that anyway to  to do  because we may have to do an extract to get the  amount of data per speaker about right.

The other thing is , isn't TI - digits isolated digits ?


I might have to do that anyway to  to do  because we may have to do an extract to get the  amount of data per speaker about right.

The other thing is , isn't TI - digits isolated digits ?


The other thing is , isn't TI - digits isolated digits ?

Or is that another one ? I 'm  I looked through a bunch of the digits t corp corpora , and now they 're all blurring.


Or is that another one ? I 'm  I looked through a bunch of the digits t corp corpora , and now they 're all blurring.

Cuz one of them was literally people reading a single digit. And then others were connected digits.


Or is that another one ? I 'm  I looked through a bunch of the digits t corp corpora , and now they 're all blurring.

Cuz one of them was literally people reading a single digit. And then others were connected digits.

Yeah. Most of TI - digits is connected digits , I think.


Cuz one of them was literally people reading a single digit. And then others were connected digits.

Yeah. Most of TI - digits is connected digits , I think.


Cuz one of them was literally people reading a single digit. And then others were connected digits.

Yeah. Most of TI - digits is connected digits , I think.

The  I mean , we had a Bellcore corpus that we were using. It was   that 's  that was isolated digits.


Yeah. Most of TI - digits is connected digits , I think.

The  I mean , we had a Bellcore corpus that we were using. It was   that 's  that was isolated digits.

Maybe it 's the Bell Gram. Bell Digits. Alright.


The  I mean , we had a Bellcore corpus that we were using. It was   that 's  that was isolated digits.

Maybe it 's the Bell Gram. Bell Digits. Alright.


The  I mean , we had a Bellcore corpus that we were using. It was   that 's  that was isolated digits.

Maybe it 's the Bell Gram. Bell Digits. Alright.

By the way , I think we can improve these numbers if we care to compr improve them  by , um ,  not starting with the Switchboard models but by taking the Switchboard models and doing supervised adaptation on a small amount of digit data collected in this setting.


Maybe it 's the Bell Gram. Bell Digits. Alright.

By the way , I think we can improve these numbers if we care to compr improve them  by , um ,  not starting with the Switchboard models but by taking the Switchboard models and doing supervised adaptation on a small amount of digit data collected in this setting.


By the way , I think we can improve these numbers if we care to compr improve them  by , um ,  not starting with the Switchboard models but by taking the Switchboard models and doing supervised adaptation on a small amount of digit data collected in this setting.

Because that would adapt your models to the room acoustics and f for the far - field microphones , you know , to the noise. And that should really improve things , um , further. And then you use those adapted models , which are not speaker adapted but sort of acous you know , channel adapted


Because that would adapt your models to the room acoustics and f for the far - field microphones , you know , to the noise. And that should really improve things , um , further. And then you use those adapted models , which are not speaker adapted but sort of acous you know , channel adapted

use that as the starting models for your speaker adaptation.


Because that would adapt your models to the room acoustics and f for the far - field microphones , you know , to the noise. And that should really improve things , um , further. And then you use those adapted models , which are not speaker adapted but sort of acous you know , channel adapted

use that as the starting models for your speaker adaptation.

Yeah.  But the thing is , uh  I mean , w when you  it depends whether you 're ju were just using this as a   a starter task for  you know , to get things going for conversational or if we 're really interested i in connected digits. And I  I think the answer is both. And for  for connected digits over the telephone you don't actually want to put a whole lot of effort into adaptation


use that as the starting models for your speaker adaptation.

Yeah.  But the thing is , uh  I mean , w when you  it depends whether you 're ju were just using this as a   a starter task for  you know , to get things going for conversational or if we 're really interested i in connected digits. And I  I think the answer is both. And for  for connected digits over the telephone you don't actually want to put a whole lot of effort into adaptation


use that as the starting models for your speaker adaptation.

Yeah.  But the thing is , uh  I mean , w when you  it depends whether you 're ju were just using this as a   a starter task for  you know , to get things going for conversational or if we 're really interested i in connected digits. And I  I think the answer is both. And for  for connected digits over the telephone you don't actually want to put a whole lot of effort into adaptation

because  somebody  gets on the phone and says a number and then you just want it. You don't  don't , uh


Yeah.  But the thing is , uh  I mean , w when you  it depends whether you 're ju were just using this as a   a starter task for  you know , to get things going for conversational or if we 're really interested i in connected digits. And I  I think the answer is both. And for  for connected digits over the telephone you don't actually want to put a whole lot of effort into adaptation

because  somebody  gets on the phone and says a number and then you just want it. You don't  don't , uh


because  somebody  gets on the phone and says a number and then you just want it. You don't  don't , uh


Um , but , you know , I  uh , my impression was that you were actually interested in the far - field microphone , uh , problem , I mean. So , you want to  you want to  That 's the obvious thing to try.


Um , but , you know , I  uh , my impression was that you were actually interested in the far - field microphone , uh , problem , I mean. So , you want to  you want to  That 's the obvious thing to try.

Right ? Then , eh  because you  you don't have any


Right ? Then , eh  because you  you don't have any


Right ? Then , eh  because you  you don't have any

That 's where the most m acoustic mismatch is between the currently used models and the  the r the set up here.


That 's where the most m acoustic mismatch is between the currently used models and the  the r the set up here.


That 's where the most m acoustic mismatch is between the currently used models and the  the r the set up here.

Yeah. So that 'd be anoth another interesting data point.


Yeah. So that 'd be anoth another interesting data point.


Yeah. So that 'd be anoth another interesting data point.

I mean , I  I guess I 'm saying I don't know if we 'd want to do that as the  as


I mean , I  I guess I 'm saying I don't know if we 'd want to do that as the  as


If you have a strong fe if you have a strong preference , you could use this.


If you have a strong fe if you have a strong preference , you could use this.

It 's just we  we think it has some spikes. So , uh , we  we didn't use that one.


It 's just we  we think it has some spikes. So , uh , we  we didn't use that one.


I don't know. And Andre - Andreas , your  your microphone 's a little bit low.


I don't know. And Andre - Andreas , your  your microphone 's a little bit low.

I don't know if we wanna use that as the


I don't know if we wanna use that as the


I  I already adjusted this a number of times.


I  I already adjusted this a number of times.

Yeah , I think these mikes are not working as well as I would like.


Yeah , I think these mikes are not working as well as I would like.

can't quite seem to  Yeah , I think this contraption around your head is not  working so well.


Yeah , I think these mikes are not working as well as I would like.

can't quite seem to  Yeah , I think this contraption around your head is not  working so well.

Too many adju too many adjustments. Yeah. Anyway , what I was saying is that I  I think I probably wouldn't want to see that as sort of like the norm , that we compared all things to.


can't quite seem to  Yeah , I think this contraption around your head is not  working so well.

Too many adju too many adjustments. Yeah. Anyway , what I was saying is that I  I think I probably wouldn't want to see that as sort of like the norm , that we compared all things to.

To , uh , the  to have  have all this ad all this , uh , adaptation. But I think it 's an important data point , if you 're  if  Yeah.


Too many adju too many adjustments. Yeah. Anyway , what I was saying is that I  I think I probably wouldn't want to see that as sort of like the norm , that we compared all things to.

To , uh , the  to have  have all this ad all this , uh , adaptation. But I think it 's an important data point , if you 're  if  Yeah.


To , uh , the  to have  have all this ad all this , uh , adaptation. But I think it 's an important data point , if you 're  if  Yeah.

Um. The other thing that  that , uh  of course , what Barry was looking at was  was just that , the near versus far. And , yeah , the adaptation would get  th some of that.


Um. The other thing that  that , uh  of course , what Barry was looking at was  was just that , the near versus far. And , yeah , the adaptation would get  th some of that.

But , I think even  even if there was , uh , only a factor of two or something , like I was saying in the email , I think that 's   that 's a big factor. So


But , I think even  even if there was , uh , only a factor of two or something , like I was saying in the email , I think that 's   that 's a big factor. So

Liz , you could also just use the other mike if you 're having problems with that one.


Liz , you could also just use the other mike if you 're having problems with that one.


Liz , you could also just use the other mike if you 're having problems with that one.

Yeah. This would be OK. We  we  we think that this has spikes on it ,


Yeah. This would be OK. We  we  we think that this has spikes on it ,

It 's this thing 's  This is too big for my head.


It 's this thing 's  This is too big for my head.


I mean , mine are too. E th everybody 's ears are too big for these things.


I mean , mine are too. E th everybody 's ears are too big for these things.

No , my  my  But this is too big for my head. So , I mean ,   it doesn't  you know , it 's sit


I mean , mine are too. E th everybody 's ears are too big for these things.

No , my  my  But this is too big for my head. So , I mean ,   it doesn't  you know , it 's sit

Well , if you 'd rather have this one then it 's


No , my  my  But this is too big for my head. So , I mean ,   it doesn't  you know , it 's sit

Well , if you 'd rather have this one then it 's


Well , if you 'd rather have this one then it 's


So the  To get that , uh , pivoted this way , it pivots like this.


So the  To get that , uh , pivoted this way , it pivots like this.

And there 's a screw that you can tighten.


And there 's a screw that you can tighten.


So if it doesn't bounce around too much , that 's actually good placement.


So if it doesn't bounce around too much , that 's actually good placement.

But it looks like it 's gonna bounce a lot.


But it looks like it 's gonna bounce a lot.


Uh , adaptation , non - adaptation , um , factor of two , um  Oh , yeah. I know what I was go w


Uh , adaptation , non - adaptation , um , factor of two , um  Oh , yeah. I know what I was go w

What k u By the way , wh what factor of two did you  ?


What k u By the way , wh what factor of two did you  ?

It 's tha that  that we were saying , you know , well is  how much worse is far than near , you know.


It 's tha that  that we were saying , you know , well is  how much worse is far than near , you know.


It 's tha that  that we were saying , you know , well is  how much worse is far than near , you know.

And I mean it depends on which one you 're looking at ,


And I mean it depends on which one you 're looking at ,

but for the everybody , it 's  little under a factor or two.


but for the everybody , it 's  little under a factor or two.

Yeah. I  I know what I was thinking was that maybe , uh , i i we could actually t t try at least looking at , uh , some of the  the large vocabulary speech from a far microphone , at least from the good one.


Yeah. I  I know what I was thinking was that maybe , uh , i i we could actually t t try at least looking at , uh , some of the  the large vocabulary speech from a far microphone , at least from the good one.

I mean , before I thought we 'd get , you know , a hundred and fifty percent error or something , but if   if , uh  if we 're getting thirty - five , forty percent or something ,  u um


I mean , before I thought we 'd get , you know , a hundred and fifty percent error or something , but if   if , uh  if we 're getting thirty - five , forty percent or something ,  u um

Actually if you run , though , on a close - talking mike over the whole meeting , during all those silences , you get , like , four hundred percent word error.


I mean , before I thought we 'd get , you know , a hundred and fifty percent error or something , but if   if , uh  if we 're getting thirty - five , forty percent or something ,  u um

Actually if you run , though , on a close - talking mike over the whole meeting , during all those silences , you get , like , four hundred percent word error.

Mm - hmm. Right. I understand. But doing the same kind of limited thing


Actually if you run , though , on a close - talking mike over the whole meeting , during all those silences , you get , like , four hundred percent word error.

Mm - hmm. Right. I understand. But doing the same kind of limited thing


Actually if you run , though , on a close - talking mike over the whole meeting , during all those silences , you get , like , four hundred percent word error.

Mm - hmm. Right. I understand. But doing the same kind of limited thing

Yeah , sure. Get all these insertions. But I 'm saying if you do the same kind of limited thing  as people have done in Switchboard evaluations or as  a


Mm - hmm. Right. I understand. But doing the same kind of limited thing

Yeah , sure. Get all these insertions. But I 'm saying if you do the same kind of limited thing  as people have done in Switchboard evaluations or as  a

Yeah. Where you know who the speaker is and there 's no overlap ? And you do just the far - field for those regions ?


Yeah , sure. Get all these insertions. But I 'm saying if you do the same kind of limited thing  as people have done in Switchboard evaluations or as  a

Yeah. Where you know who the speaker is and there 's no overlap ? And you do just the far - field for those regions ?

Yeah. Yeah. The same sort of numbers that we got those graphs from. Right ?


Yeah , sure. Get all these insertions. But I 'm saying if you do the same kind of limited thing  as people have done in Switchboard evaluations or as  a

Yeah. Where you know who the speaker is and there 's no overlap ? And you do just the far - field for those regions ?

Yeah. Yeah. The same sort of numbers that we got those graphs from. Right ?

Could we do exactly the same thing that we 're doing now , but do it with a far - field mike ?


Yeah. Where you know who the speaker is and there 's no overlap ? And you do just the far - field for those regions ?

Yeah. Yeah. The same sort of numbers that we got those graphs from. Right ?

Could we do exactly the same thing that we 're doing now , but do it with a far - field mike ?


Yeah. Yeah. The same sort of numbers that we got those graphs from. Right ?

Could we do exactly the same thing that we 're doing now , but do it with a far - field mike ?

Cuz we extract the times from the near - field mike , but you use the acoustics from the far - field mike.


Could we do exactly the same thing that we 're doing now , but do it with a far - field mike ?

Cuz we extract the times from the near - field mike , but you use the acoustics from the far - field mike.

Right. I understand that. I just meant that  so you have  three choices. There 's , um  You can use times where that person is talking only from the transcripts but the segmentations were  were synchronized. Or you can do a forced alignment on the close - talking to determine that , the you know , within this segment , these really were the times that this person was talking and elsewhere in the segment other people are overlapping and just front - end those pieces. Or you can run it on the whole data , which is  which is , you know , a


Cuz we extract the times from the near - field mike , but you use the acoustics from the far - field mike.

Right. I understand that. I just meant that  so you have  three choices. There 's , um  You can use times where that person is talking only from the transcripts but the segmentations were  were synchronized. Or you can do a forced alignment on the close - talking to determine that , the you know , within this segment , these really were the times that this person was talking and elsewhere in the segment other people are overlapping and just front - end those pieces. Or you can run it on the whole data , which is  which is , you know , a

But  but  but how did we get the  how did we determine the links , uh , that we 're testing on in the stuff we reported ?


Cuz we extract the times from the near - field mike , but you use the acoustics from the far - field mike.

Right. I understand that. I just meant that  so you have  three choices. There 's , um  You can use times where that person is talking only from the transcripts but the segmentations were  were synchronized. Or you can do a forced alignment on the close - talking to determine that , the you know , within this segment , these really were the times that this person was talking and elsewhere in the segment other people are overlapping and just front - end those pieces. Or you can run it on the whole data , which is  which is , you know , a

But  but  but how did we get the  how did we determine the links , uh , that we 're testing on in the stuff we reported ?

In the H L T paper we took  segments that are channel  time - aligned , which is now h being changed in the transcription process , which is good , and we took cases where the transcribers said there was only one person talking here , because no one else had time  any words in that segment and called that " non - overlap ".


Right. I understand that. I just meant that  so you have  three choices. There 's , um  You can use times where that person is talking only from the transcripts but the segmentations were  were synchronized. Or you can do a forced alignment on the close - talking to determine that , the you know , within this segment , these really were the times that this person was talking and elsewhere in the segment other people are overlapping and just front - end those pieces. Or you can run it on the whole data , which is  which is , you know , a

But  but  but how did we get the  how did we determine the links , uh , that we 're testing on in the stuff we reported ?

In the H L T paper we took  segments that are channel  time - aligned , which is now h being changed in the transcription process , which is good , and we took cases where the transcribers said there was only one person talking here , because no one else had time  any words in that segment and called that " non - overlap ".

And tha And that 's what we were getting those numbers from.


But  but  but how did we get the  how did we determine the links , uh , that we 're testing on in the stuff we reported ?

In the H L T paper we took  segments that are channel  time - aligned , which is now h being changed in the transcription process , which is good , and we took cases where the transcribers said there was only one person talking here , because no one else had time  any words in that segment and called that " non - overlap ".

And tha And that 's what we were getting those numbers from.


In the H L T paper we took  segments that are channel  time - aligned , which is now h being changed in the transcription process , which is good , and we took cases where the transcribers said there was only one person talking here , because no one else had time  any words in that segment and called that " non - overlap ".

And tha And that 's what we were getting those numbers from.


And tha And that 's what we were getting those numbers from.

The bad numbers were from  the segments where there was overlap.


The bad numbers were from  the segments where there was overlap.

Well , we could start with the good ones.


The bad numbers were from  the segments where there was overlap.

Well , we could start with the good ones.

But anyway  so I think that we should try it once with  the same conditions that were used to create those , and in those same segments just use one of the P Z


Well , we could start with the good ones.

But anyway  so I think that we should try it once with  the same conditions that were used to create those , and in those same segments just use one of the P Z


But anyway  so I think that we should try it once with  the same conditions that were used to create those , and in those same segments just use one of the P Z

And then , you know , I mean , the thing is if we were getting , uh  what , thirty - five , forty percent , something like that on  on that particular set , uh , does it go to seventy or eighty ?


And then , you know , I mean , the thing is if we were getting , uh  what , thirty - five , forty percent , something like that on  on that particular set , uh , does it go to seventy or eighty ?

Or , does it use up so much memory we can't decode it ?


And then , you know , I mean , the thing is if we were getting , uh  what , thirty - five , forty percent , something like that on  on that particular set , uh , does it go to seventy or eighty ?

Or , does it use up so much memory we can't decode it ?

It might also depend on which speaker th it is and how close they are to the PZM ?


Or , does it use up so much memory we can't decode it ?

It might also depend on which speaker th it is and how close they are to the PZM ?


Or , does it use up so much memory we can't decode it ?

It might also depend on which speaker th it is and how close they are to the PZM ?

I don't know how different they are from each other.


It might also depend on which speaker th it is and how close they are to the PZM ?

I don't know how different they are from each other.

You want to probably choose the PZM channel that is closest to the speaker.


I don't know how different they are from each other.

You want to probably choose the PZM channel that is closest to the speaker.


You want to probably choose the PZM channel that is closest to the speaker.

For this particular digit ones , I just picked that one.


For this particular digit ones , I just picked that one.


For this particular digit ones , I just picked that one.

OK. So we would then use that one , too ,


OK. So we would then use that one , too ,


You know , it 's  so i but I would  I 'd pick that one. It 'll be less good for some people than for other , but I  I 'd like to see it on the same  exact same data set that  that we did the other thing on.


You know , it 's  so i but I would  I 'd pick that one. It 'll be less good for some people than for other , but I  I 'd like to see it on the same  exact same data set that  that we did the other thing on.

Actually  I sh actually should 've picked a different one ,


You know , it 's  so i but I would  I 'd pick that one. It 'll be less good for some people than for other , but I  I 'd like to see it on the same  exact same data set that  that we did the other thing on.

Actually  I sh actually should 've picked a different one ,

because  that could be why the PDA is worse. Because it 's further away from most of the people reading digits.


Actually  I sh actually should 've picked a different one ,

because  that could be why the PDA is worse. Because it 's further away from most of the people reading digits.


because  that could be why the PDA is worse. Because it 's further away from most of the people reading digits.


Well , yeah. You could look at , I guess , that PZM or something.


Well , yeah. You could look at , I guess , that PZM or something.

But the other is , it 's very , uh  I mean , even though there 's  I 'm sure the f f the  the SRI , uh , front - end has some kind of pre - emphasis , it 's  it 's , uh   still , th it 's picking up lots of low - frequency energy.


But the other is , it 's very , uh  I mean , even though there 's  I 'm sure the f f the  the SRI , uh , front - end has some kind of pre - emphasis , it 's  it 's , uh   still , th it 's picking up lots of low - frequency energy.

So , even discriminating against it , I 'm sure some of it 's getting through. Um. But , yeah , you 're right. Prob - a part of it is just the distance.


But the other is , it 's very , uh  I mean , even though there 's  I 'm sure the f f the  the SRI , uh , front - end has some kind of pre - emphasis , it 's  it 's , uh   still , th it 's picking up lots of low - frequency energy.

So , even discriminating against it , I 'm sure some of it 's getting through. Um. But , yeah , you 're right. Prob - a part of it is just the distance.

And aren't these pretty bad microphones ?


So , even discriminating against it , I 'm sure some of it 's getting through. Um. But , yeah , you 're right. Prob - a part of it is just the distance.

And aren't these pretty bad microphones ?


And aren't these pretty bad microphones ?

Well , they 're bad. But , I mean , if you listen to it , it sounds OK. You know ? u Yeah.


Well , they 're bad. But , I mean , if you listen to it , it sounds OK. You know ? u Yeah.

Yeah. When you listen to it , uh , the PZM and the PDA  Yeah , th the PDA has higher sound floor but not by a lot. It 's really pretty  uh , pretty much the same.


Well , they 're bad. But , I mean , if you listen to it , it sounds OK. You know ? u Yeah.

Yeah. When you listen to it , uh , the PZM and the PDA  Yeah , th the PDA has higher sound floor but not by a lot. It 's really pretty  uh , pretty much the same.

I just remember you saying you got them to be cheap on purpose. Cheap in terms of their quality. So.


Well , they 're bad. But , I mean , if you listen to it , it sounds OK. You know ? u Yeah.

Yeah. When you listen to it , uh , the PZM and the PDA  Yeah , th the PDA has higher sound floor but not by a lot. It 's really pretty  uh , pretty much the same.

I just remember you saying you got them to be cheap on purpose. Cheap in terms of their quality. So.

Well , they 're  twenty - five cents or so.


Yeah. When you listen to it , uh , the PZM and the PDA  Yeah , th the PDA has higher sound floor but not by a lot. It 's really pretty  uh , pretty much the same.

I just remember you saying you got them to be cheap on purpose. Cheap in terms of their quality. So.

Well , they 're  twenty - five cents or so.

Th - we wanted them to be  to be typical of what would be in a PDA.


I just remember you saying you got them to be cheap on purpose. Cheap in terms of their quality. So.

Well , they 're  twenty - five cents or so.

Th - we wanted them to be  to be typical of what would be in a PDA.


Well , they 're  twenty - five cents or so.

Th - we wanted them to be  to be typical of what would be in a PDA.


Th - we wanted them to be  to be typical of what would be in a PDA.

So they are  they 're not the PZM three hundred dollar type. They 're the twenty - five cent ,


So they are  they 're not the PZM three hundred dollar type. They 're the twenty - five cent ,


But , I mean , the thing is people use those little mikes for everything because they 're really not bad.


But , I mean , the thing is people use those little mikes for everything because they 're really not bad.

I mean , if you 're not  doing something ridiculous like feeding it to a speech recognizer , they  they   they  you know , you can hear the sou hear the sounds just fine.


I mean , if you 're not  doing something ridiculous like feeding it to a speech recognizer , they  they   they  you know , you can hear the sou hear the sounds just fine.


I mean , if you 're not  doing something ridiculous like feeding it to a speech recognizer , they  they   they  you know , you can hear the sou hear the sounds just fine.

You know , it 's  They  I mean , i it 's more or less the same principles as these other mikes are built under , it 's just that  there 's less quality control. They just , you know , churn them out and don't check them. Um. So. So that was  Yeah. So that was i interesting result. So like I said , the front - end guys are very much interested in  in this is as  as well and


I mean , if you 're not  doing something ridiculous like feeding it to a speech recognizer , they  they   they  you know , you can hear the sou hear the sounds just fine.

You know , it 's  They  I mean , i it 's more or less the same principles as these other mikes are built under , it 's just that  there 's less quality control. They just , you know , churn them out and don't check them. Um. So. So that was  Yeah. So that was i interesting result. So like I said , the front - end guys are very much interested in  in this is as  as well and

So  so , but where is this now ? I mean , what 's  where do we go from here ?


You know , it 's  They  I mean , i it 's more or less the same principles as these other mikes are built under , it 's just that  there 's less quality control. They just , you know , churn them out and don't check them. Um. So. So that was  Yeah. So that was i interesting result. So like I said , the front - end guys are very much interested in  in this is as  as well and

So  so , but where is this now ? I mean , what 's  where do we go from here ?


You know , it 's  They  I mean , i it 's more or less the same principles as these other mikes are built under , it 's just that  there 's less quality control. They just , you know , churn them out and don't check them. Um. So. So that was  Yeah. So that was i interesting result. So like I said , the front - end guys are very much interested in  in this is as  as well and

So  so , but where is this now ? I mean , what 's  where do we go from here ?

I mean , we  so we have a  we have a  a system that works pretty well but it 's not , you know , the system that people here are used to using  to working with.


So  so , but where is this now ? I mean , what 's  where do we go from here ?

I mean , we  so we have a  we have a  a system that works pretty well but it 's not , you know , the system that people here are used to using  to working with.

Well , I think what we wanna do is we want to  eh ,


I mean , we  so we have a  we have a  a system that works pretty well but it 's not , you know , the system that people here are used to using  to working with.

Well , I think what we wanna do is we want to  eh ,


I mean , we  so we have a  we have a  a system that works pretty well but it 's not , you know , the system that people here are used to using  to working with.

Well , I think what we wanna do is we want to  eh ,

and we 've talked about this in other  contexts  we want to  have the ability to feed it different features.


Well , I think what we wanna do is we want to  eh ,

and we 've talked about this in other  contexts  we want to  have the ability to feed it different features.


and we 've talked about this in other  contexts  we want to  have the ability to feed it different features.

And then , um ,  from the point of view of the front - end research , it would be s uh , substituting for HTK.


And then , um ,  from the point of view of the front - end research , it would be s uh , substituting for HTK.

I think that 's the key thing. And then if we can feed it different features , then we can try all the different things that we 're trying there.


I think that 's the key thing. And then if we can feed it different features , then we can try all the different things that we 're trying there.

And then , um , uh , also Dave is  is thinking about using the data in different ways , uh , to  um , uh , explicitly work on reverberation


And then , um , uh , also Dave is  is thinking about using the data in different ways , uh , to  um , uh , explicitly work on reverberation

starting with some techniques that some other people have  found somewhat useful , and  Yeah.


And then , um , uh , also Dave is  is thinking about using the data in different ways , uh , to  um , uh , explicitly work on reverberation

starting with some techniques that some other people have  found somewhat useful , and  Yeah.

OK. So  so the key  thing that 's missing here is basically the ability to feed , you know , other features  i into the recognizer


starting with some techniques that some other people have  found somewhat useful , and  Yeah.

OK. So  so the key  thing that 's missing here is basically the ability to feed , you know , other features  i into the recognizer


OK. So  so the key  thing that 's missing here is basically the ability to feed , you know , other features  i into the recognizer


OK. And , uh , es I don't know when Chuck will be back but that 's exactly what he  he 's gonna


OK. And , uh , es I don't know when Chuck will be back but that 's exactly what he  he 's gonna

H h He 's  he 's sort of back , but he drove for fourteen hours an and wasn't gonna make it in today.


OK. And , uh , es I don't know when Chuck will be back but that 's exactly what he  he 's gonna

H h He 's  he 's sort of back , but he drove for fourteen hours an and wasn't gonna make it in today.

Oh , OK. So , I think that 's one of the things that he said he would be working on. Um.


H h He 's  he 's sort of back , but he drove for fourteen hours an and wasn't gonna make it in today.

Oh , OK. So , I think that 's one of the things that he said he would be working on. Um.

Just sort of t to make sure that  we can do that


Oh , OK. So , I think that 's one of the things that he said he would be working on. Um.

Just sort of t to make sure that  we can do that


Just sort of t to make sure that  we can do that


It 's  uh , I mean , the  the front - end is f i tha that 's in the SRI recognizer is very nice in that it does a lot of things on the fly but it unfortunately  is not  designed and , um   like the , uh , ICSI system is , where you can feed it from a pipeline of  of the command. So , the  what that means probably for the foreseeable future is that you have to , uh , dump out , um  you know , if you want to use some new features , you have to dump them into individual files and  give those files to the recognizer.


It 's  uh , I mean , the  the front - end is f i tha that 's in the SRI recognizer is very nice in that it does a lot of things on the fly but it unfortunately  is not  designed and , um   like the , uh , ICSI system is , where you can feed it from a pipeline of  of the command. So , the  what that means probably for the foreseeable future is that you have to , uh , dump out , um  you know , if you want to use some new features , you have to dump them into individual files and  give those files to the recognizer.

Oh. So , although you  you can pipe it as well , we tend to do it that way because that way you can concentrate on one block and not keep re - doing it over and over.


Oh. So , although you  you can pipe it as well , we tend to do it that way because that way you can concentrate on one block and not keep re - doing it over and over.


So tha that 's exactly what the P - file  is for.


So tha that 's exactly what the P - file  is for.

Yeah , the  the  the cumbersome thing is  is , um  is that you actually have to dump out little  little files.


Yeah , the  the  the cumbersome thing is  is , um  is that you actually have to dump out little  little files.

So for each segment that you want to recognize  you have to  dump out  a separate file.


So for each segment that you want to recognize  you have to  dump out  a separate file.

Just like i th like th as if there were these waveform segments , but instead you have sort of feature file segments. But , you know  So.


So for each segment that you want to recognize  you have to  dump out  a separate file.

Just like i th like th as if there were these waveform segments , but instead you have sort of feature file segments. But , you know  So.

Cool. OK. So the s the  the next thing we had on the agenda was something about alignments ?


Just like i th like th as if there were these waveform segments , but instead you have sort of feature file segments. But , you know  So.

Cool. OK. So the s the  the next thing we had on the agenda was something about alignments ?

Oh. Yes , we have  I don't know , did you wanna talk about it , or  ? I can give a  I was just telling this to Jane and  and  W we  we were able to get some definite improvement on the forced alignments by looking at them first and then realizing the kinds of errors  that were occurring and um , some of the errors occurring very frequently are just things like the first word being moved to as early as possible in the recognition , which is a um , I think was both a  a pruning  problem and possibly a problem with needing constraints on word locations. And so we tried both of these st things. We tried saying  I don't know , I got this  whacky idea that  just from looking at the data , that when people talk  their words are usually chunked together. It 's not that they say one word and then there 's a bunch of words together. They 're  might say one word and then another word far away if they were doing just backchannels ? But in general , if there 's , like , five or six words and one word 's far away from it , that 's probably wrong on average. So , um  And then also , ca the pruning , of course , was too  too severe.


Just like i th like th as if there were these waveform segments , but instead you have sort of feature file segments. But , you know  So.

Cool. OK. So the s the  the next thing we had on the agenda was something about alignments ?

Oh. Yes , we have  I don't know , did you wanna talk about it , or  ? I can give a  I was just telling this to Jane and  and  W we  we were able to get some definite improvement on the forced alignments by looking at them first and then realizing the kinds of errors  that were occurring and um , some of the errors occurring very frequently are just things like the first word being moved to as early as possible in the recognition , which is a um , I think was both a  a pruning  problem and possibly a problem with needing constraints on word locations. And so we tried both of these st things. We tried saying  I don't know , I got this  whacky idea that  just from looking at the data , that when people talk  their words are usually chunked together. It 's not that they say one word and then there 's a bunch of words together. They 're  might say one word and then another word far away if they were doing just backchannels ? But in general , if there 's , like , five or six words and one word 's far away from it , that 's probably wrong on average. So , um  And then also , ca the pruning , of course , was too  too severe.

So that 's actually interesting. The pruning was the same value that we used for recognition. And we had lowered that  we had used tighter pruning after Liz ran some experiments showing that , you know , it runs slower and there 's no real difference in


Cool. OK. So the s the  the next thing we had on the agenda was something about alignments ?

Oh. Yes , we have  I don't know , did you wanna talk about it , or  ? I can give a  I was just telling this to Jane and  and  W we  we were able to get some definite improvement on the forced alignments by looking at them first and then realizing the kinds of errors  that were occurring and um , some of the errors occurring very frequently are just things like the first word being moved to as early as possible in the recognition , which is a um , I think was both a  a pruning  problem and possibly a problem with needing constraints on word locations. And so we tried both of these st things. We tried saying  I don't know , I got this  whacky idea that  just from looking at the data , that when people talk  their words are usually chunked together. It 's not that they say one word and then there 's a bunch of words together. They 're  might say one word and then another word far away if they were doing just backchannels ? But in general , if there 's , like , five or six words and one word 's far away from it , that 's probably wrong on average. So , um  And then also , ca the pruning , of course , was too  too severe.

So that 's actually interesting. The pruning was the same value that we used for recognition. And we had lowered that  we had used tighter pruning after Liz ran some experiments showing that , you know , it runs slower and there 's no real difference in

Actually it was better with  slightly better or about th


Oh. Yes , we have  I don't know , did you wanna talk about it , or  ? I can give a  I was just telling this to Jane and  and  W we  we were able to get some definite improvement on the forced alignments by looking at them first and then realizing the kinds of errors  that were occurring and um , some of the errors occurring very frequently are just things like the first word being moved to as early as possible in the recognition , which is a um , I think was both a  a pruning  problem and possibly a problem with needing constraints on word locations. And so we tried both of these st things. We tried saying  I don't know , I got this  whacky idea that  just from looking at the data , that when people talk  their words are usually chunked together. It 's not that they say one word and then there 's a bunch of words together. They 're  might say one word and then another word far away if they were doing just backchannels ? But in general , if there 's , like , five or six words and one word 's far away from it , that 's probably wrong on average. So , um  And then also , ca the pruning , of course , was too  too severe.

So that 's actually interesting. The pruning was the same value that we used for recognition. And we had lowered that  we had used tighter pruning after Liz ran some experiments showing that , you know , it runs slower and there 's no real difference in

Actually it was better with  slightly better or about th


So that 's actually interesting. The pruning was the same value that we used for recognition. And we had lowered that  we had used tighter pruning after Liz ran some experiments showing that , you know , it runs slower and there 's no real difference in

Actually it was better with  slightly better or about th


Actually it was better with  slightly better or about th

Right. So for free recognition , this  the lower pruning value is better.


Right. So for free recognition , this  the lower pruning value is better.

It 's probably cuz the recognition 's just bad en at a point where it 's bad enough that  that you don't lose anything.


Right. So for free recognition , this  the lower pruning value is better.

It 's probably cuz the recognition 's just bad en at a point where it 's bad enough that  that you don't lose anything.

You  Correct. Right. Um , but it turned out for  for  to get accurate alignments it was really important to open up the pruning significantly.


It 's probably cuz the recognition 's just bad en at a point where it 's bad enough that  that you don't lose anything.

You  Correct. Right. Um , but it turned out for  for  to get accurate alignments it was really important to open up the pruning significantly.


You  Correct. Right. Um , but it turned out for  for  to get accurate alignments it was really important to open up the pruning significantly.

Um  because otherwise it would sort of do greedy alignment , um , in regions where there was no real speech yet from the foreground speaker.


Um  because otherwise it would sort of do greedy alignment , um , in regions where there was no real speech yet from the foreground speaker.


Um  because otherwise it would sort of do greedy alignment , um , in regions where there was no real speech yet from the foreground speaker.

Um ,  so that was one big factor that helped improve things and then the other thing was that , you know , as Liz said the  we f enforce the fact that , uh , the foreground speech has to be continuous. It cannot be  you cannot have a background speech hypothesis in the middle of the foreground speech. You can only have background speech at the beginning and the end.


Um  because otherwise it would sort of do greedy alignment , um , in regions where there was no real speech yet from the foreground speaker.

Um ,  so that was one big factor that helped improve things and then the other thing was that , you know , as Liz said the  we f enforce the fact that , uh , the foreground speech has to be continuous. It cannot be  you cannot have a background speech hypothesis in the middle of the foreground speech. You can only have background speech at the beginning and the end.

Yeah. I mean , yeah , it isn't always true , and I think what we really want is some clever way to do this , where , um , you know , from the data or from maybe some hand - corrected alignments from transcribers that things like words that do occur just by themselves  a alone , like backchannels or something that we did allow to have background speech around it


Um ,  so that was one big factor that helped improve things and then the other thing was that , you know , as Liz said the  we f enforce the fact that , uh , the foreground speech has to be continuous. It cannot be  you cannot have a background speech hypothesis in the middle of the foreground speech. You can only have background speech at the beginning and the end.

Yeah. I mean , yeah , it isn't always true , and I think what we really want is some clever way to do this , where , um , you know , from the data or from maybe some hand - corrected alignments from transcribers that things like words that do occur just by themselves  a alone , like backchannels or something that we did allow to have background speech around it


Yeah. I mean , yeah , it isn't always true , and I think what we really want is some clever way to do this , where , um , you know , from the data or from maybe some hand - corrected alignments from transcribers that things like words that do occur just by themselves  a alone , like backchannels or something that we did allow to have background speech around it


but the rest would be constrained. So , I think we have a version that 's pretty good for the native speakers. I don't know yet about the non - native speakers. And , um , we basically also made noise models for the different  sort of grouped some of the  mouth noises together. Um , so , and then there 's a background speech model. And we also  There was some neat  or , interesting cases , like there 's one meeting where ,  um , Jose 's giving a presentation and he 's talking about , um , the word " mixed  signal " and someone didn't understand , uh , that you were saying " mixed "  I think , Morgan. And so your speech - ch was s saying something about mixed signal.


but the rest would be constrained. So , I think we have a version that 's pretty good for the native speakers. I don't know yet about the non - native speakers. And , um , we basically also made noise models for the different  sort of grouped some of the  mouth noises together. Um , so , and then there 's a background speech model. And we also  There was some neat  or , interesting cases , like there 's one meeting where ,  um , Jose 's giving a presentation and he 's talking about , um , the word " mixed  signal " and someone didn't understand , uh , that you were saying " mixed "  I think , Morgan. And so your speech - ch was s saying something about mixed signal.

And the next turn was a lot of people saying " mixed " , like " he means mixed signal " or " I think it 's mixed ". And the word " mixed " in this segment occurs , like , a bunch of times.


And the next turn was a lot of people saying " mixed " , like " he means mixed signal " or " I think it 's mixed ". And the word " mixed " in this segment occurs , like , a bunch of times.

And Chuck 's on the lapel here , and he also says " mixed " but it 's at the last one , and of course the aligner th aligns it everywhere else to everybody else 's " mixed " ,


And Chuck 's on the lapel here , and he also says " mixed " but it 's at the last one , and of course the aligner th aligns it everywhere else to everybody else 's " mixed " ,

cuz there 's no adaptation yet. So there 's   I think there 's some issues about  u We probably want to adapt at least the foreground speaker. But , I guess Andreas tried adapting both the foreground and a background generic speaker , and that 's actually a little bit of a f funky model. Like , it gives you some weird alignments , just because often the background speakers match better to the foreground than the foreground speaker.


cuz there 's no adaptation yet. So there 's   I think there 's some issues about  u We probably want to adapt at least the foreground speaker. But , I guess Andreas tried adapting both the foreground and a background generic speaker , and that 's actually a little bit of a f funky model. Like , it gives you some weird alignments , just because often the background speakers match better to the foreground than the foreground speaker.


especially when you get lots of the same words , uh , occurring in the


especially when you get lots of the same words , uh , occurring in the

Well , the  I  I think you can do better by  uh , cloning  so we have a reject phone. And you  and what we wanted to try with  you know , once we have this paper written and have a little more time ,  uh , t cloning that reject model and then one copy of it would be adapted to the foreground speaker to capture the rejects in the foreground , like fragments and stuff , and the other copy would be adapted to the background speaker.


Well , the  I  I think you can do better by  uh , cloning  so we have a reject phone. And you  and what we wanted to try with  you know , once we have this paper written and have a little more time ,  uh , t cloning that reject model and then one copy of it would be adapted to the foreground speaker to capture the rejects in the foreground , like fragments and stuff , and the other copy would be adapted to the background speaker.

Right now the words like  partial words are  reject models and you normally allow those to match to any word.


Right now the words like  partial words are  reject models and you normally allow those to match to any word.


Right now the words like  partial words are  reject models and you normally allow those to match to any word.

But then the background speech was also a reject model , and so this constraint of not allowing rejects in between  you know , it needs to differentiate between the two. So just sort of working through a bunch of debugging kinds of issues.


But then the background speech was also a reject model , and so this constraint of not allowing rejects in between  you know , it needs to differentiate between the two. So just sort of working through a bunch of debugging kinds of issues.

And another one is turns , like people starting with  " well I think " and someone else is  " well how about ". So the word " well " is in this  in this  segment multiple times , and as soon as it occurs usually the aligner will try to align it to the first person who says it. But then that constraint of sort of  uh , proximity constraint will push it over to the person who really said it in general.


But then the background speech was also a reject model , and so this constraint of not allowing rejects in between  you know , it needs to differentiate between the two. So just sort of working through a bunch of debugging kinds of issues.

And another one is turns , like people starting with  " well I think " and someone else is  " well how about ". So the word " well " is in this  in this  segment multiple times , and as soon as it occurs usually the aligner will try to align it to the first person who says it. But then that constraint of sort of  uh , proximity constraint will push it over to the person who really said it in general.

Is the proximity constraint a hard constraint , or did you do some sort of probabilistic weighting distance , or  ?


And another one is turns , like people starting with  " well I think " and someone else is  " well how about ". So the word " well " is in this  in this  segment multiple times , and as soon as it occurs usually the aligner will try to align it to the first person who says it. But then that constraint of sort of  uh , proximity constraint will push it over to the person who really said it in general.

Is the proximity constraint a hard constraint , or did you do some sort of probabilistic weighting distance , or  ?


Is the proximity constraint a hard constraint , or did you do some sort of probabilistic weighting distance , or  ?

No. We  w OK. We  it 's straightforward to actually just have a  a penalty that doesn't completely disallows it but discourages it. But , um , we just didn't have time to play with , you know , tuning yet another  yet another parameter.


No. We  w OK. We  it 's straightforward to actually just have a  a penalty that doesn't completely disallows it but discourages it. But , um , we just didn't have time to play with , you know , tuning yet another  yet another parameter.


No. We  w OK. We  it 's straightforward to actually just have a  a penalty that doesn't completely disallows it but discourages it. But , um , we just didn't have time to play with , you know , tuning yet another  yet another parameter.

And really the reason we can't do it is just that we don't have a  we don't have ground truth for these. So ,  we would need a hand - marked , um ,  word - level alignments or at least sort of the boundaries of the speech betw you know , between the speakers. Um , and then use that as a reference and tune the parameters of the  of the model , uh , to op to get the best  performance.


And really the reason we can't do it is just that we don't have a  we don't have ground truth for these. So ,  we would need a hand - marked , um ,  word - level alignments or at least sort of the boundaries of the speech betw you know , between the speakers. Um , and then use that as a reference and tune the parameters of the  of the model , uh , to op to get the best  performance.


And really the reason we can't do it is just that we don't have a  we don't have ground truth for these. So ,  we would need a hand - marked , um ,  word - level alignments or at least sort of the boundaries of the speech betw you know , between the speakers. Um , and then use that as a reference and tune the parameters of the  of the model , uh , to op to get the best  performance.

G given  I  I mean , I wa I wa I was gonna ask you anyway , uh , how you assessed that things were better.


G given  I  I mean , I wa I wa I was gonna ask you anyway , uh , how you assessed that things were better.

I looked at them. I spent two days  um , in Waves


I looked at them. I spent two days  um , in Waves

Oh , it was painful because  the thing is , you know the alignments share a lot in common , so  And you 're  yo you 're looking at these segments where there 's a lot of speech. I mean , a lot of them have a lot of words. Not by every speaker


Oh , it was painful because  the thing is , you know the alignments share a lot in common , so  And you 're  yo you 're looking at these segments where there 's a lot of speech. I mean , a lot of them have a lot of words. Not by every speaker

but by some speaker there 's a lot of words. No , not


but by some speaker there 's a lot of words. No , not

I mean that if you look at the individual segments from just one person you don't see a lot of words ,


I mean that if you look at the individual segments from just one person you don't see a lot of words ,


I mean that if you look at the individual segments from just one person you don't see a lot of words ,

but altogether you 'll see a lot of words up there.


but altogether you 'll see a lot of words up there.


And so the reject is also mapping and pauses  So I looked at them all in Waves and just lined up all the alignments , and , at first it sort of looked like a mess and then the more I looked at it , I thought " OK , well it 's moving these words leftward and  " You know , it wasn't that bad. It was just doing certain things wrong. So  But , I don't , you know , have time to l  to look at all of them and it would be really useful to have , like , a  a transcriber who could use Waves , um , just mark , like , the beginning and end of the foreground speaker 's real words  like , the beginning of the first word , the end of the last word  and then we could , you know , do some adjustments.


And so the reject is also mapping and pauses  So I looked at them all in Waves and just lined up all the alignments , and , at first it sort of looked like a mess and then the more I looked at it , I thought " OK , well it 's moving these words leftward and  " You know , it wasn't that bad. It was just doing certain things wrong. So  But , I don't , you know , have time to l  to look at all of them and it would be really useful to have , like , a  a transcriber who could use Waves , um , just mark , like , the beginning and end of the foreground speaker 's real words  like , the beginning of the first word , the end of the last word  and then we could , you know , do some adjustments.

Yeah. I  OK. I have to ask you something , is i does it have to be Waves ? Because if we could benefit from what you did , incorporate that into the present transcripts ,  that would help.


And so the reject is also mapping and pauses  So I looked at them all in Waves and just lined up all the alignments , and , at first it sort of looked like a mess and then the more I looked at it , I thought " OK , well it 's moving these words leftward and  " You know , it wasn't that bad. It was just doing certain things wrong. So  But , I don't , you know , have time to l  to look at all of them and it would be really useful to have , like , a  a transcriber who could use Waves , um , just mark , like , the beginning and end of the foreground speaker 's real words  like , the beginning of the first word , the end of the last word  and then we could , you know , do some adjustments.

Yeah. I  OK. I have to ask you something , is i does it have to be Waves ? Because if we could benefit from what you did , incorporate that into the present transcripts ,  that would help.

And then , um , the other thing is , I believe that I did hand So. One of these transcripts was gone over by a transcriber and then I hand - marked it myself so that we do have , uh , the beginning and ending of individual utterances. Um , I didn't do it word level ,


Yeah. I  OK. I have to ask you something , is i does it have to be Waves ? Because if we could benefit from what you did , incorporate that into the present transcripts ,  that would help.

And then , um , the other thing is , I believe that I did hand So. One of these transcripts was gone over by a transcriber and then I hand - marked it myself so that we do have , uh , the beginning and ending of individual utterances. Um , I didn't do it word level ,


And then , um , the other thing is , I believe that I did hand So. One of these transcripts was gone over by a transcriber and then I hand - marked it myself so that we do have , uh , the beginning and ending of individual utterances. Um , I didn't do it word level ,


So I  so for  for one of the N S A groups. And also I went back to the original one that I first transcribed and  and did it w uh , w uh , utterance by utterance for that particular one. So I think you do have  if that 's a sufficient unit , I think that you do have hand - marking for that. But it 'd be wonderful to be able to  benefit from your Waves stuff.


So I  so for  for one of the N S A groups. And also I went back to the original one that I first transcribed and  and did it w uh , w uh , utterance by utterance for that particular one. So I think you do have  if that 's a sufficient unit , I think that you do have hand - marking for that. But it 'd be wonderful to be able to  benefit from your Waves stuff.

Yeah. I mean , if  if you can , um  if you wanna


Yeah. I mean , if  if you can , um  if you wanna


well , Jane and I were  just in terms of the tool , talking about this. I guess Sue had had some  reactions. You know , interface - wise if you 're looking at speech , you wanna be able to know really where the words are. And so ,  we can give you some examples of sort of what this output looks like ,


well , Jane and I were  just in terms of the tool , talking about this. I guess Sue had had some  reactions. You know , interface - wise if you 're looking at speech , you wanna be able to know really where the words are. And so ,  we can give you some examples of sort of what this output looks like ,

Yeah , that 's right. Middle of the word , or


well , Jane and I were  just in terms of the tool , talking about this. I guess Sue had had some  reactions. You know , interface - wise if you 're looking at speech , you wanna be able to know really where the words are. And so ,  we can give you some examples of sort of what this output looks like ,

Yeah , that 's right. Middle of the word , or

um , and see if you can in maybe incorporate it into the Transcriber tool some way , or


well , Jane and I were  just in terms of the tool , talking about this. I guess Sue had had some  reactions. You know , interface - wise if you 're looking at speech , you wanna be able to know really where the words are. And so ,  we can give you some examples of sort of what this output looks like ,

Yeah , that 's right. Middle of the word , or

um , and see if you can in maybe incorporate it into the Transcriber tool some way , or

Well , I th I 'm thinking just ch e e incorporating it into the representation.


Yeah , that 's right. Middle of the word , or

um , and see if you can in maybe incorporate it into the Transcriber tool some way , or

Well , I th I 'm thinking just ch e e incorporating it into the representation.


um , and see if you can in maybe incorporate it into the Transcriber tool some way , or

Well , I th I 'm thinking just ch e e incorporating it into the representation.


Well , I th I 'm thinking just ch e e incorporating it into the representation.

You mean like  Yeah , word start insights.


You mean like  Yeah , word start insights.

if you have start points , if you have , like , time tags ,


You mean like  Yeah , word start insights.

if you have start points , if you have , like , time tags ,

which is what I assume. Isn't that what  what you  ? Well , see , Adam would be


if you have start points , if you have , like , time tags ,

which is what I assume. Isn't that what  what you  ? Well , see , Adam would be


which is what I assume. Isn't that what  what you  ? Well , see , Adam would be


which is what I assume. Isn't that what  what you  ? Well , see , Adam would be

I mean , we convert it to this format that the , um , NIST scoring tool unders uh , CTM. Conversation Time - Marked file. And  and then that 's the  that 's what the


I mean , we convert it to this format that the , um , NIST scoring tool unders uh , CTM. Conversation Time - Marked file. And  and then that 's the  that 's what the


So you would know this more than I would.


So you would know this more than I would.

It seems like she  if she 's g if she 's moving time marks around ,


It seems like she  if she 's g if she 's moving time marks around ,


It seems like she  if she 's g if she 's moving time marks around ,

since our representation in Transcriber uses time marks , it seems like there should be some way of  of using that  benefitting from that.


since our representation in Transcriber uses time marks , it seems like there should be some way of  of using that  benefitting from that.

Yeah , it wou the advantage would just be that when you brought up a bin you would be able  if you were zoomed in enough in Transcriber to see all the words ,


Yeah , it wou the advantage would just be that when you brought up a bin you would be able  if you were zoomed in enough in Transcriber to see all the words ,

you would be able to , like , have the words sort of located in time , if you wanted to do that.


Yeah , it wou the advantage would just be that when you brought up a bin you would be able  if you were zoomed in enough in Transcriber to see all the words ,

you would be able to , like , have the words sort of located in time , if you wanted to do that.

So  so if we e e even just had a  a  It sounds like w we   we almost do.


you would be able to , like , have the words sort of located in time , if you wanted to do that.

So  so if we e e even just had a  a  It sounds like w we   we almost do.


So  so if we e e even just had a  a  It sounds like w we   we almost do.


Yeah. Just ha uh , trying out  the alignment  procedure that you have on that


Yeah. Just ha uh , trying out  the alignment  procedure that you have on that

you could actually get something , um  uh , uh , get an objective measure. Uh


you could actually get something , um  uh , uh , get an objective measure. Uh

You mean on  on the hand - marked , um  So we  we only r hav I only looked at actually alignments from one meeting that we chose ,


You mean on  on the hand - marked , um  So we  we only r hav I only looked at actually alignments from one meeting that we chose ,

I think MR four , just randomly , um  And


I think MR four , just randomly , um  And


I think MR four , just randomly , um  And

We knew  we knew that it had these insertion errors from


We knew  we knew that it had these insertion errors from

It had sort of  average recognition performance in a bunch of speakers


We knew  we knew that it had these insertion errors from

It had sort of  average recognition performance in a bunch of speakers

and it was a Meeting Recorder meeting. Um. But , yeah , we should try to use what you have. I did re - run recognition on your new version of MR one.


It had sort of  average recognition performance in a bunch of speakers

and it was a Meeting Recorder meeting. Um. But , yeah , we should try to use what you have. I did re - run recognition on your new version of MR one.


and it was a Meeting Recorder meeting. Um. But , yeah , we should try to use what you have. I did re - run recognition on your new version of MR one.

I  I mean the  the one with Dan  Ellis in it  and Eric.


and it was a Meeting Recorder meeting. Um. But , yeah , we should try to use what you have. I did re - run recognition on your new version of MR one.

I  I mean the  the one with Dan  Ellis in it  and Eric.

Good ! Uh - huh. Yeah , exactly. Yeah. Yeah.


I  I mean the  the one with Dan  Ellis in it  and Eric.

Good ! Uh - huh. Yeah , exactly. Yeah. Yeah.


I  I mean the  the one with Dan  Ellis in it  and Eric.

Good ! Uh - huh. Yeah , exactly. Yeah. Yeah.

Um  That  Yeah , actually it wasn't the new new , it was the medium new.


Good ! Uh - huh. Yeah , exactly. Yeah. Yeah.

Um  That  Yeah , actually it wasn't the new new , it was the medium new.


Um  That  Yeah , actually it wasn't the new new , it was the medium new.

But  but we would  we should do the  the latest version.


But  but we would  we should do the  the latest version.


You  did you adjust the  the utterance times , um , for each channel ?


You  did you adjust the  the utterance times , um , for each channel ?

Yes. Yes , I did. And furthermore , I found that there were a certain number where   not  not a lot , but several times I actually  moved an utterance from  Adam 's channel to Dan 's or from Dan 's to Adam 's. So there was some speaker identif And the reason was because  I transcribed that at a point before   uh , before we had the multiple audio available f so I couldn't switch between the audio. I  I transcribed it off of the mixed channel entirely , which meant in overlaps , I was at a  at a terrific disadvantage.


You  did you adjust the  the utterance times , um , for each channel ?

Yes. Yes , I did. And furthermore , I found that there were a certain number where   not  not a lot , but several times I actually  moved an utterance from  Adam 's channel to Dan 's or from Dan 's to Adam 's. So there was some speaker identif And the reason was because  I transcribed that at a point before   uh , before we had the multiple audio available f so I couldn't switch between the audio. I  I transcribed it off of the mixed channel entirely , which meant in overlaps , I was at a  at a terrific disadvantage.

In addition it was before the channelized , uh , possibility was there. And finally I did it using the speakers of my , um   of  you know , off the CPU on my  on my machine cuz I didn't have a headphone.


Yes. Yes , I did. And furthermore , I found that there were a certain number where   not  not a lot , but several times I actually  moved an utterance from  Adam 's channel to Dan 's or from Dan 's to Adam 's. So there was some speaker identif And the reason was because  I transcribed that at a point before   uh , before we had the multiple audio available f so I couldn't switch between the audio. I  I transcribed it off of the mixed channel entirely , which meant in overlaps , I was at a  at a terrific disadvantage.

In addition it was before the channelized , uh , possibility was there. And finally I did it using the speakers of my , um   of  you know , off the CPU on my  on my machine cuz I didn't have a headphone.


In addition it was before the channelized , uh , possibility was there. And finally I did it using the speakers of my , um   of  you know , off the CPU on my  on my machine cuz I didn't have a headphone.

So it @ @ , like , I mean  Yeah , I  I mean , i in retrospect  it would 've been good to ha  have got I should 've gotten a headphone. But in any case , um , thi this is  this was transcribed in a  in a ,  uh , less optimal way than  than the ones that came after it , and I was able to  you know , an and this meant that there were some speaker identif identifications which were changes.


In addition it was before the channelized , uh , possibility was there. And finally I did it using the speakers of my , um   of  you know , off the CPU on my  on my machine cuz I didn't have a headphone.

So it @ @ , like , I mean  Yeah , I  I mean , i in retrospect  it would 've been good to ha  have got I should 've gotten a headphone. But in any case , um , thi this is  this was transcribed in a  in a ,  uh , less optimal way than  than the ones that came after it , and I was able to  you know , an and this meant that there were some speaker identif identifications which were changes.

Well , I know there were some speaker labelling problems , um , after interruptions.


So it @ @ , like , I mean  Yeah , I  I mean , i in retrospect  it would 've been good to ha  have got I should 've gotten a headphone. But in any case , um , thi this is  this was transcribed in a  in a ,  uh , less optimal way than  than the ones that came after it , and I was able to  you know , an and this meant that there were some speaker identif identifications which were changes.

Well , I know there were some speaker labelling problems , um , after interruptions.


So it @ @ , like , I mean  Yeah , I  I mean , i in retrospect  it would 've been good to ha  have got I should 've gotten a headphone. But in any case , um , thi this is  this was transcribed in a  in a ,  uh , less optimal way than  than the ones that came after it , and I was able to  you know , an and this meant that there were some speaker identif identifications which were changes.

Well , I know there were some speaker labelling problems , um , after interruptions.

Is that what you 're referring to ? I mean , cuz there 's this one instance when , for example , you 're running down the stairs.


Well , I know there were some speaker labelling problems , um , after interruptions.

Is that what you 're referring to ? I mean , cuz there 's this one instance when , for example , you 're running down the stairs.


Is that what you 're referring to ? I mean , cuz there 's this one instance when , for example , you 're running down the stairs.


Don  Don has had   He knows  he can just read it like a play.


Don  Don has had   He knows  he can just read it like a play.

Right. It 's a  Yeah , I 've  I 've  I 'm very well acquainted with this meeting.


Right. It 's a  Yeah , I 've  I 've  I 'm very well acquainted with this meeting.

" And then she said , and then he said. "


" And then she said , and then he said. "

Yeah , I know it by heart. So , um ,  there 's one point when you 're running down the stairs.


" And then she said , and then he said. "

Yeah , I know it by heart. So , um ,  there 's one point when you 're running down the stairs.

Right ? And , like , there 's an interruption. You interrupt somebody , but then there 's no line after that. For example , there 's no speaker identification after that line.


Yeah , I know it by heart. So , um ,  there 's one point when you 're running down the stairs.

Right ? And , like , there 's an interruption. You interrupt somebody , but then there 's no line after that. For example , there 's no speaker identification after that line.


Right ? And , like , there 's an interruption. You interrupt somebody , but then there 's no line after that. For example , there 's no speaker identification after that line.

Is that what you 're talking about ? Or were there mislabellings as far as , like , the a Adam was  ?


Right ? And , like , there 's an interruption. You interrupt somebody , but then there 's no line after that. For example , there 's no speaker identification after that line.

Is that what you 're talking about ? Or were there mislabellings as far as , like , the a Adam was  ?

That was fixed , um , before  i i i I think I I think I understood that pretty


Is that what you 're talking about ? Or were there mislabellings as far as , like , the a Adam was  ?

That was fixed , um , before  i i i I think I I think I understood that pretty

Yeah. Cuz I thought I let you know about that.


Is that what you 're talking about ? Or were there mislabellings as far as , like , the a Adam was  ?

That was fixed , um , before  i i i I think I I think I understood that pretty

Yeah. Cuz I thought I let you know about that.

Thank you for mentioning. Yeah , no , tha that  That I think went away a couple of versions ago ,


That was fixed , um , before  i i i I think I I think I understood that pretty

Yeah. Cuz I thought I let you know about that.

Thank you for mentioning. Yeah , no , tha that  That I think went away a couple of versions ago ,


Yeah. Cuz I thought I let you know about that.

Thank you for mentioning. Yeah , no , tha that  That I think went away a couple of versions ago ,


Thank you for mentioning. Yeah , no , tha that  That I think went away a couple of versions ago ,

But you 're actually saying that certain , uh , speakers were mis mis - identified.


But you 're actually saying that certain , uh , speakers were mis mis - identified.

Yeah. So , with  under  um , uh , listening to the mixed channel , there were times when , as surprising as that is , I got Adam 's voice confused with Dan 's and vice versa


Yeah. So , with  under  um , uh , listening to the mixed channel , there were times when , as surprising as that is , I got Adam 's voice confused with Dan 's and vice versa


and embedde embedded in overlaps. The other thing that was w interesting to me was that I picked up a lot of , um , backchannels which were hidden in the mixed signal ,


and embedde embedded in overlaps. The other thing that was w interesting to me was that I picked up a lot of , um , backchannels which were hidden in the mixed signal ,

which , you know , I mean , you c not  not too surprising. But the other thing that  I  I hadn't thought about this , but I thou I wanted to raise this when you were  uh , with respect to also a strategy which might help with the alignments potentially , but that 's  When I was looking at these backchannels , they were turning up usually   very often in  w well , I won't say " usually "  but anyway , very often , I picked them up in a channel  w which was the person who had asked a question. S so , like , someone says " an and have you done the so - and - so ? " And then there would be backchannels , but it would be the person who asked the question. Other people weren't really doing much backchannelling. And , you know , sometimes you have the  Yeah , uh - huh.