I mean , if it 's two  if  if it 's , uh  if it 's two - fifty , then we could keep the delta where it is if we shaved off twenty. If it 's two hundred , if we shaved off twenty , we could  we could , uh , meet it by moving the delta back.

So , how do you know that what you have is too much if they 're still deciding ?

Uh , we don't , but it 's just  I mean , the main thing is that since that we got burned last time , and  you know , by not worrying about it very much , we 're just staying conscious of it.


So , how do you know that what you have is too much if they 're still deciding ?

Uh , we don't , but it 's just  I mean , the main thing is that since that we got burned last time , and  you know , by not worrying about it very much , we 're just staying conscious of it.

And so , th I mean , if  if  if a week before we have to be done someone says , " Well , you have to have fifty milliseconds less than you have now " , it would be pretty frantic around here. So


Uh , we don't , but it 's just  I mean , the main thing is that since that we got burned last time , and  you know , by not worrying about it very much , we 're just staying conscious of it.

And so , th I mean , if  if  if a week before we have to be done someone says , " Well , you have to have fifty milliseconds less than you have now " , it would be pretty frantic around here. So


And so , th I mean , if  if  if a week before we have to be done someone says , " Well , you have to have fifty milliseconds less than you have now " , it would be pretty frantic around here. So


And so , th I mean , if  if  if a week before we have to be done someone says , " Well , you have to have fifty milliseconds less than you have now " , it would be pretty frantic around here. So

But still , that 's  that 's a pretty big , uh , win. And it doesn't seem like you 're  in terms of your delay , you 're , uh , that


But still , that 's  that 's a pretty big , uh , win. And it doesn't seem like you 're  in terms of your delay , you 're , uh , that

He added a bit on , I guess , because before we were  we were  had  were able to have the noise , uh , stuff , uh , and the LVA be in parallel.


But still , that 's  that 's a pretty big , uh , win. And it doesn't seem like you 're  in terms of your delay , you 're , uh , that

He added a bit on , I guess , because before we were  we were  had  were able to have the noise , uh , stuff , uh , and the LVA be in parallel.

And now he 's  he 's requiring it to be done first.


He added a bit on , I guess , because before we were  we were  had  were able to have the noise , uh , stuff , uh , and the LVA be in parallel.

And now he 's  he 's requiring it to be done first.

Well , but I think the main thing , maybe , is the cleaning of the speech , which takes forty milliseconds or so.


And now he 's  he 's requiring it to be done first.

Well , but I think the main thing , maybe , is the cleaning of the speech , which takes forty milliseconds or so.

Right. Well , so you say  let 's say ten milliseconds  seconds for the LDA.


And now he 's  he 's requiring it to be done first.

Well , but I think the main thing , maybe , is the cleaning of the speech , which takes forty milliseconds or so.

Right. Well , so you say  let 's say ten milliseconds  seconds for the LDA.

And  and  but  the LDA is , well , pretty short right now.


Well , but I think the main thing , maybe , is the cleaning of the speech , which takes forty milliseconds or so.

Right. Well , so you say  let 's say ten milliseconds  seconds for the LDA.

And  and  but  the LDA is , well , pretty short right now.

Well , ten. And then forty for the other.


Right. Well , so you say  let 's say ten milliseconds  seconds for the LDA.

And  and  but  the LDA is , well , pretty short right now.

Well , ten. And then forty for the other.


And  and  but  the LDA is , well , pretty short right now.

Well , ten. And then forty for the other.

Yeah , the LDA  LDA  we don't know , is , like  is it very crucial for the features , right ?


Well , ten. And then forty for the other.

Yeah , the LDA  LDA  we don't know , is , like  is it very crucial for the features , right ?


Yeah , the LDA  LDA  we don't know , is , like  is it very crucial for the features , right ?


I mean , I  maybe the LDA 's not very useful then.


I mean , you have twenty for delta computation which y now you 're sort of doing twice , right ? But yo w were you doing that before ?


I mean , you have twenty for delta computation which y now you 're sort of doing twice , right ? But yo w were you doing that before ?

Mmm. Well , in the proposal , um , the input of the VAD network were just three frames , I think.


I mean , you have twenty for delta computation which y now you 're sort of doing twice , right ? But yo w were you doing that before ?

Mmm. Well , in the proposal , um , the input of the VAD network were just three frames , I think.

On the  in the  Mm - hmm. Just  Yeah , just the static , no delta.


Mmm. Well , in the proposal , um , the input of the VAD network were just three frames , I think.

On the  in the  Mm - hmm. Just  Yeah , just the static , no delta.


On the  in the  Mm - hmm. Just  Yeah , just the static , no delta.

So , what you have now is fort uh , forty for the  the noise , twenty for the delta , and ten for the LDA. That 's seventy milliseconds of stuff which was formerly in parallel ,


So , what you have now is fort uh , forty for the  the noise , twenty for the delta , and ten for the LDA. That 's seventy milliseconds of stuff which was formerly in parallel ,


So , what you have now is fort uh , forty for the  the noise , twenty for the delta , and ten for the LDA. That 's seventy milliseconds of stuff which was formerly in parallel ,

you know , that 's  that 's the difference as far as the timing , right ?


you know , that 's  that 's the difference as far as the timing , right ?


you know , that 's  that 's the difference as far as the timing , right ?

Um , and you could experiment with cutting various pieces of these back a bit , but  I mean , we 're s we 're not  we 're not in terrible shape.


you know , that 's  that 's the difference as far as the timing , right ?

Um , and you could experiment with cutting various pieces of these back a bit , but  I mean , we 're s we 're not  we 're not in terrible shape.

Yeah , that 's what it seems like to me. It 's pretty good.


Um , and you could experiment with cutting various pieces of these back a bit , but  I mean , we 're s we 're not  we 're not in terrible shape.

Yeah , that 's what it seems like to me. It 's pretty good.


Yeah , that 's what it seems like to me. It 's pretty good.

It 's  it 's not like it 's adding up to four hundred milliseconds or something.


It 's  it 's not like it 's adding up to four hundred milliseconds or something.

Where  where is this  where is this fifty - seven point O two in  in comparison to the last evaluation ?


It 's  it 's not like it 's adding up to four hundred milliseconds or something.

Where  where is this  where is this fifty - seven point O two in  in comparison to the last evaluation ?

Well , it 's  I think it 's better than anything , uh , anybody got.


Where  where is this  where is this fifty - seven point O two in  in comparison to the last evaluation ?

Well , it 's  I think it 's better than anything , uh , anybody got.

Yeah. The best was fifty - four point five.


Well , it 's  I think it 's better than anything , uh , anybody got.

Yeah. The best was fifty - four point five.


Yeah. The best was fifty - four point five.


And our system was forty - nine , but with the neural network.


And our system was forty - nine , but with the neural network.

With the f with the neural net. Yeah , and r and


With the f with the neural net. Yeah , and r and

Yeah , so this is  this is like the first proposal. The proposal - one. It was forty - four , actually.


With the f with the neural net. Yeah , and r and

Yeah , so this is  this is like the first proposal. The proposal - one. It was forty - four , actually.

Yeah. Yeah. And we still don't have the neural net in. So  so it 's


Yeah , so this is  this is like the first proposal. The proposal - one. It was forty - four , actually.

Yeah. Yeah. And we still don't have the neural net in. So  so it 's


Yeah , so this is  this is like the first proposal. The proposal - one. It was forty - four , actually.

Yeah. Yeah. And we still don't have the neural net in. So  so it 's

You know. So it 's  We 're  we 're doing better.


Yeah. Yeah. And we still don't have the neural net in. So  so it 's

You know. So it 's  We 're  we 're doing better.


You know. So it 's  We 're  we 're doing better.

I mean , we 're getting better recognition. I mean , I 'm sure other people working on this are not sitting still either , but


I mean , we 're getting better recognition. I mean , I 'm sure other people working on this are not sitting still either , but

but  but , uh  Uh , I mean , the important thing is that we learn how to do this better , and , you know. So. Um , Yeah. So , our , um  Yeah , you can see the kind of  kind of numbers that we 're having , say , on SpeechDat - Car which is a hard task , cuz it 's really , um  I think it 's just sort of  sort of reasonable numbers , starting to be. I mean , it 's still terri


I mean , we 're getting better recognition. I mean , I 'm sure other people working on this are not sitting still either , but

but  but , uh  Uh , I mean , the important thing is that we learn how to do this better , and , you know. So. Um , Yeah. So , our , um  Yeah , you can see the kind of  kind of numbers that we 're having , say , on SpeechDat - Car which is a hard task , cuz it 's really , um  I think it 's just sort of  sort of reasonable numbers , starting to be. I mean , it 's still terri

Mm - hmm. Yeah , even for a well - matched case it 's sixty percent error rate reduction ,


but  but , uh  Uh , I mean , the important thing is that we learn how to do this better , and , you know. So. Um , Yeah. So , our , um  Yeah , you can see the kind of  kind of numbers that we 're having , say , on SpeechDat - Car which is a hard task , cuz it 's really , um  I think it 's just sort of  sort of reasonable numbers , starting to be. I mean , it 's still terri

Mm - hmm. Yeah , even for a well - matched case it 's sixty percent error rate reduction ,


Mm - hmm. Yeah , even for a well - matched case it 's sixty percent error rate reduction ,


Um , Yeah. So actually , this is in between  what we had with the previous VAD and what Sunil did with an IDL VAD. Which gave sixty - two percent improvement , right ?


Um , Yeah. So actually , this is in between  what we had with the previous VAD and what Sunil did with an IDL VAD. Which gave sixty - two percent improvement , right ?

It 's almost an average somewhere around


It 's almost an average somewhere around


It 's almost an average somewhere around

What was that ? Say that last part again ?


What was that ? Say that last part again ?

So , if you use , like , an IDL VAD , uh , for dropping the frames ,


What was that ? Say that last part again ?

So , if you use , like , an IDL VAD , uh , for dropping the frames ,

the best that we can get  i That means that we estimate the silence probability on the clean version of the utterances. Then you can go up to sixty - two percent error rate reduction , globally.


So , if you use , like , an IDL VAD , uh , for dropping the frames ,

the best that we can get  i That means that we estimate the silence probability on the clean version of the utterances. Then you can go up to sixty - two percent error rate reduction , globally.


the best that we can get  i That means that we estimate the silence probability on the clean version of the utterances. Then you can go up to sixty - two percent error rate reduction , globally.


the best that we can get  i That means that we estimate the silence probability on the clean version of the utterances. Then you can go up to sixty - two percent error rate reduction , globally.

So that would be even  That wouldn't change this number down here to sixty - two ?


So that would be even  That wouldn't change this number down here to sixty - two ?


So that would be even  That wouldn't change this number down here to sixty - two ?

If you add a g good v very good VAD , that works as well as a VAD working on clean speech ,


If you add a g good v very good VAD , that works as well as a VAD working on clean speech ,


If you add a g good v very good VAD , that works as well as a VAD working on clean speech ,

So that 's sort of the best you could hope for.


So that 's sort of the best you could hope for.


So that 's sort of the best you could hope for.

Probably. Yeah. So fi si fifty - three is what you were getting with the old VAD.


Probably. Yeah. So fi si fifty - three is what you were getting with the old VAD.


Probably. Yeah. So fi si fifty - three is what you were getting with the old VAD.

And , uh  and sixty - two with the  the , you know , quote , unquote , cheating VAD. And fifty - seven is what you got with the real VAD.


And , uh  and sixty - two with the  the , you know , quote , unquote , cheating VAD. And fifty - seven is what you got with the real VAD.


And , uh  and sixty - two with the  the , you know , quote , unquote , cheating VAD. And fifty - seven is what you got with the real VAD.

Uh , yeah , the next thing is , I started to play  Well , I don't want to worry too much about the delay , no. Maybe it 's better to wait


Uh , yeah , the next thing is , I started to play  Well , I don't want to worry too much about the delay , no. Maybe it 's better to wait


from the committee. Uh , but I started to play with the , um ,   uh , tandem neural network. Mmm I just did the configuration that 's very similar to what we did for the February proposal. And  Um. So. There is a f a first feature stream that use uh straight MFCC features.


from the committee. Uh , but I started to play with the , um ,   uh , tandem neural network. Mmm I just did the configuration that 's very similar to what we did for the February proposal. And  Um. So. There is a f a first feature stream that use uh straight MFCC features.

Well , these features actually. And the other stream is the output of a neural network , using as input , also , these , um , cleaned MFCC. Um


from the committee. Uh , but I started to play with the , um ,   uh , tandem neural network. Mmm I just did the configuration that 's very similar to what we did for the February proposal. And  Um. So. There is a f a first feature stream that use uh straight MFCC features.

Well , these features actually. And the other stream is the output of a neural network , using as input , also , these , um , cleaned MFCC. Um

Those are th those are th what is going into the tandem net ?


Well , these features actually. And the other stream is the output of a neural network , using as input , also , these , um , cleaned MFCC. Um

Those are th those are th what is going into the tandem net ?


Those are th those are th what is going into the tandem net ?

So there is just this feature stream ,  the fifteen MFCC plus delta and double - delta.


So there is just this feature stream ,  the fifteen MFCC plus delta and double - delta.


So there is just this feature stream ,  the fifteen MFCC plus delta and double - delta.

Um , so it 's  makes forty - five features  that are used as input to the HTK. And then , there is  there are more inputs that comes from the tandem MLP.


Um , so it 's  makes forty - five features  that are used as input to the HTK. And then , there is  there are more inputs that comes from the tandem MLP.


cuz then it has one part that 's discriminative ,


So , um , uh , yeah. Right now it seems that  i I just tested on SpeechDat - Car while the experiment are running on your  on TI - digits. Well , it improves on the well - matched and the mismatched conditions , but it get worse on the highly mismatched. Um ,


So , um , uh , yeah. Right now it seems that  i I just tested on SpeechDat - Car while the experiment are running on your  on TI - digits. Well , it improves on the well - matched and the mismatched conditions , but it get worse on the highly mismatched. Um ,

like , on the well - match and medium mismatch , the gain is around five percent relative , but it goes down a lot more , like fifteen percent on the HM case.


like , on the well - match and medium mismatch , the gain is around five percent relative , but it goes down a lot more , like fifteen percent on the HM case.

You 're just using the full ninety features ?


You 're just using the full ninety features ?

i I have , um  From the networks , it 's twenty - eight. So


i I have , um  From the networks , it 's twenty - eight. So

And from the other side it 's forty - five.


i I have , um  From the networks , it 's twenty - eight. So

And from the other side it 's forty - five.

So it 's  you have seventy - three features ,


And from the other side it 's forty - five.

So it 's  you have seventy - three features ,


So it 's  you have seventy - three features ,

and you 're just feeding them like that.


and you 're just feeding them like that.


and you 're just feeding them like that.

Mm - hmm. There 's a KLT after the neural network , as  as before.


Mm - hmm. There 's a KLT after the neural network , as  as before.

That 's how you get down to twenty - eight ?


That 's how you get down to twenty - eight ?


Uh. It 's  i i i It 's because it 's what we did for the first proposal. We tested , uh , trying to go down


I wanted to do something very similar to the proposal as a first  first try.


But we have to  for sure , we have to go down , because the limit is now sixty features.


But we have to  for sure , we have to go down , because the limit is now sixty features.

So , uh , we have to find a way to decrease the number of features. Um


But we have to  for sure , we have to go down , because the limit is now sixty features.

So , uh , we have to find a way to decrease the number of features. Um

So , it seems funny that  I don't know , maybe I don't u quite understand everything ,  but that adding features  I guess  I guess if you 're keeping the back - end fixed. Maybe that 's it. Because it seems like just adding information shouldn't give worse results. But I guess if you 're keeping the number of Gaussians fixed in the recognizer , then


So , uh , we have to find a way to decrease the number of features. Um

So , it seems funny that  I don't know , maybe I don't u quite understand everything ,  but that adding features  I guess  I guess if you 're keeping the back - end fixed. Maybe that 's it. Because it seems like just adding information shouldn't give worse results. But I guess if you 're keeping the number of Gaussians fixed in the recognizer , then


So , it seems funny that  I don't know , maybe I don't u quite understand everything ,  but that adding features  I guess  I guess if you 're keeping the back - end fixed. Maybe that 's it. Because it seems like just adding information shouldn't give worse results. But I guess if you 're keeping the number of Gaussians fixed in the recognizer , then

But , I mean , just in general , adding information  Suppose the information you added , well , was a really terrible feature and all it brought in was noise.


But , I mean , just in general , adding information  Suppose the information you added , well , was a really terrible feature and all it brought in was noise.


But , I mean , just in general , adding information  Suppose the information you added , well , was a really terrible feature and all it brought in was noise.

Right ? So  so , um  Or  or suppose it wasn't completely terrible , but it was completely equivalent to another one feature that you had , except it was noisier.


Right ? So  so , um  Or  or suppose it wasn't completely terrible , but it was completely equivalent to another one feature that you had , except it was noisier.

Right ? In that case you wouldn't necessarily expect it to be better at all.


Right ? So  so , um  Or  or suppose it wasn't completely terrible , but it was completely equivalent to another one feature that you had , except it was noisier.

Right ? In that case you wouldn't necessarily expect it to be better at all.

Oh , yeah , I wasn't necessarily saying it should be better. I 'm just surprised that you 're getting fifteen percent relative worse on the wel


Right ? In that case you wouldn't necessarily expect it to be better at all.

Oh , yeah , I wasn't necessarily saying it should be better. I 'm just surprised that you 're getting fifteen percent relative worse on the wel


Oh , yeah , I wasn't necessarily saying it should be better. I 'm just surprised that you 're getting fifteen percent relative worse on the wel


So , " highly mismatched condition " means that in fact your training is a bad estimate of your test.


So , " highly mismatched condition " means that in fact your training is a bad estimate of your test.

So having  having , uh , a g a l a greater number of features , if they aren't maybe the right features that you use , certainly can e can easily , uh , make things worse. I mean , you 're right. If you have  if you have , uh , lots and lots of data , and you have  and your  your  your training is representative of your test , then getting more sources of information should just help. But  but it 's  It doesn't necessarily work that way.


So having  having , uh , a g a l a greater number of features , if they aren't maybe the right features that you use , certainly can e can easily , uh , make things worse. I mean , you 're right. If you have  if you have , uh , lots and lots of data , and you have  and your  your  your training is representative of your test , then getting more sources of information should just help. But  but it 's  It doesn't necessarily work that way.


So having  having , uh , a g a l a greater number of features , if they aren't maybe the right features that you use , certainly can e can easily , uh , make things worse. I mean , you 're right. If you have  if you have , uh , lots and lots of data , and you have  and your  your  your training is representative of your test , then getting more sources of information should just help. But  but it 's  It doesn't necessarily work that way.

So I wonder , um , Well , what 's your  what 's your thought about what to do next with it ?


So I wonder , um , Well , what 's your  what 's your thought about what to do next with it ?

Um , I don't know. I 'm surprised , because I expected the neural net to help more when there is more mismatch , as it was the case for the


So I wonder , um , Well , what 's your  what 's your thought about what to do next with it ?

Um , I don't know. I 'm surprised , because I expected the neural net to help more when there is more mismatch , as it was the case for the

So , was the training set same as the p the February proposal ? OK.


Um , I don't know. I 'm surprised , because I expected the neural net to help more when there is more mismatch , as it was the case for the

So , was the training set same as the p the February proposal ? OK.

Yeah , it 's the same training set , so it 's TIMIT with the TI - digits ' , uh , noises , uh , added.


So , was the training set same as the p the February proposal ? OK.

Yeah , it 's the same training set , so it 's TIMIT with the TI - digits ' , uh , noises , uh , added.


Yeah , it 's the same training set , so it 's TIMIT with the TI - digits ' , uh , noises , uh , added.

Well , we might  uh , we might have to experiment with , uh better training sets. Again. But ,


Well , we might  uh , we might have to experiment with , uh better training sets. Again. But ,


Well , we might  uh , we might have to experiment with , uh better training sets. Again. But ,

I  The other thing is , I mean , before you found that was the best configuration , but you might have to retest those things now that we have different  The rest of it is different , right ? So , um , uh , For instance , what 's the effect of just putting the neural net on without the o other  other path ?


I  The other thing is , I mean , before you found that was the best configuration , but you might have to retest those things now that we have different  The rest of it is different , right ? So , um , uh , For instance , what 's the effect of just putting the neural net on without the o other  other path ?

I mean , you know what the straight features do.


I mean , you know what the straight features do.

That gives you this. You know what it does in combination.


That gives you this. You know what it does in combination.


That gives you this. You know what it does in combination.

What if you did the  Would it make sense to do the KLT on the full set of combined features ? Instead of just on the


What if you did the  Would it make sense to do the KLT on the full set of combined features ? Instead of just on the

Yeah. I g I guess. Um. The reason I did it this ways is that in February , it  we  we tested different things like that , so , having two KLT , having just a KLT for a network , or having a global KLT.


Yeah. I g I guess. Um. The reason I did it this ways is that in February , it  we  we tested different things like that , so , having two KLT , having just a KLT for a network , or having a global KLT.


The differences between these configurations were not huge , but  it was marginally better with this configuration.


The differences between these configurations were not huge , but  it was marginally better with this configuration.

But , yeah , that 's obviously another thing to try ,


But , yeah , that 's obviously another thing to try ,


And I guess if the  These are all  so all of these seventy - three features are going into , um , the , uh  the HMM.


And I guess if the  These are all  so all of these seventy - three features are going into , um , the , uh  the HMM.

And is  are  i i are  are any deltas being computed of tha of them ?


And is  are  i i are  are any deltas being computed of tha of them ?


And is  are  i i are  are any deltas being computed of tha of them ?

So. But n th the , um , tandem features are u used as they are.


So. But n th the , um , tandem features are u used as they are.


So. But n th the , um , tandem features are u used as they are.

So , yeah , maybe we can add some context from these features also as  Dan did in  in his last work.


So. But n th the , um , tandem features are u used as they are.

So , yeah , maybe we can add some context from these features also as  Dan did in  in his last work.

Could. i Yeah , but the other thing I was thinking was , um  Uh , now I lost track of what I was thinking. But.


So , yeah , maybe we can add some context from these features also as  Dan did in  in his last work.

Could. i Yeah , but the other thing I was thinking was , um  Uh , now I lost track of what I was thinking. But.

What is the  You said there was a limit of sixty features or something ?


Could. i Yeah , but the other thing I was thinking was , um  Uh , now I lost track of what I was thinking. But.

What is the  You said there was a limit of sixty features or something ?

What 's the relation between that limit and the , um , forty - eight  uh , forty eight hundred bits per second ?


What is the  You said there was a limit of sixty features or something ?

What 's the relation between that limit and the , um , forty - eight  uh , forty eight hundred bits per second ?


What 's the relation between that limit and the , um , forty - eight  uh , forty eight hundred bits per second ?


The f the forty - eight hundred bits is for transmission of some features.


The f the forty - eight hundred bits is for transmission of some features.

because i I mean , if you 're only using h


The f the forty - eight hundred bits is for transmission of some features.

because i I mean , if you 're only using h

And generally , i it  s allows you to transmit like , fifteen , uh , cepstrum.


The f the forty - eight hundred bits is for transmission of some features.

because i I mean , if you 're only using h

And generally , i it  s allows you to transmit like , fifteen , uh , cepstrum.

The issue was that , um , this is supposed to be a standard that 's then gonna be fed to somebody 's recognizer somewhere which might be , you know , it  it might be a concern how many parameters are use  u used and so forth. And so , uh , they felt they wanted to set a limit. So they chose sixty. Some people wanted to use hundreds of parameters and  and that bothered some other people.


because i I mean , if you 're only using h

And generally , i it  s allows you to transmit like , fifteen , uh , cepstrum.

The issue was that , um , this is supposed to be a standard that 's then gonna be fed to somebody 's recognizer somewhere which might be , you know , it  it might be a concern how many parameters are use  u used and so forth. And so , uh , they felt they wanted to set a limit. So they chose sixty. Some people wanted to use hundreds of parameters and  and that bothered some other people.


And generally , i it  s allows you to transmit like , fifteen , uh , cepstrum.

The issue was that , um , this is supposed to be a standard that 's then gonna be fed to somebody 's recognizer somewhere which might be , you know , it  it might be a concern how many parameters are use  u used and so forth. And so , uh , they felt they wanted to set a limit. So they chose sixty. Some people wanted to use hundreds of parameters and  and that bothered some other people.

u And so they just chose that. I  I  I think it 's kind of r arbitrary too. But  but that 's  that 's kind of what was chosen. I  I remembered what I was going to say. What I was going to say is that , um , maybe   maybe with the noise removal , uh , these things are now more correlated. So you have two sets of things that are kind of uncorrelated , uh , within themselves , but they 're pretty correlated with one another.


The issue was that , um , this is supposed to be a standard that 's then gonna be fed to somebody 's recognizer somewhere which might be , you know , it  it might be a concern how many parameters are use  u used and so forth. And so , uh , they felt they wanted to set a limit. So they chose sixty. Some people wanted to use hundreds of parameters and  and that bothered some other people.

u And so they just chose that. I  I  I think it 's kind of r arbitrary too. But  but that 's  that 's kind of what was chosen. I  I remembered what I was going to say. What I was going to say is that , um , maybe   maybe with the noise removal , uh , these things are now more correlated. So you have two sets of things that are kind of uncorrelated , uh , within themselves , but they 're pretty correlated with one another.


u And so they just chose that. I  I  I think it 's kind of r arbitrary too. But  but that 's  that 's kind of what was chosen. I  I remembered what I was going to say. What I was going to say is that , um , maybe   maybe with the noise removal , uh , these things are now more correlated. So you have two sets of things that are kind of uncorrelated , uh , within themselves , but they 're pretty correlated with one another.

And , um , they 're being fed into these , uh , variants , only Gaussians and so forth , and  and , uh ,


And , um , they 're being fed into these , uh , variants , only Gaussians and so forth , and  and , uh ,

so maybe it would be a better idea now than it was before to , uh , have , uh , one KLT over everything , to de - correlate it.


so maybe it would be a better idea now than it was before to , uh , have , uh , one KLT over everything , to de - correlate it.


so maybe it would be a better idea now than it was before to , uh , have , uh , one KLT over everything , to de - correlate it.

What are the S N Rs in the training set , TIMIT ?


What are the S N Rs in the training set , TIMIT ?

It 's , uh , ranging from zero to clean ? Yeah. From zero to clean.


What are the S N Rs in the training set , TIMIT ?

It 's , uh , ranging from zero to clean ? Yeah. From zero to clean.

Yeah. So we found this  this , uh  this Macrophone data , and so forth , that we were using for these other experiments , to be pretty good.


It 's , uh , ranging from zero to clean ? Yeah. From zero to clean.

Yeah. So we found this  this , uh  this Macrophone data , and so forth , that we were using for these other experiments , to be pretty good.


Yeah. So we found this  this , uh  this Macrophone data , and so forth , that we were using for these other experiments , to be pretty good.

So that 's  i after you explore these other alternatives , that might be another way to start looking , is  is just improving the training set.


So that 's  i after you explore these other alternatives , that might be another way to start looking , is  is just improving the training set.

I mean , we were getting , uh , lots better recognition using that , than  Of course , you do have the problem that , um , u i  we are not able to increase the number of Gaussians , uh , or anything to , uh , uh , to match anything. So we 're only improving the training of our feature set , but that 's still probably something.


So that 's  i after you explore these other alternatives , that might be another way to start looking , is  is just improving the training set.

I mean , we were getting , uh , lots better recognition using that , than  Of course , you do have the problem that , um , u i  we are not able to increase the number of Gaussians , uh , or anything to , uh , uh , to match anything. So we 're only improving the training of our feature set , but that 's still probably something.

So you 're saying , add the Macrophone data to the training of the neural net ? The tandem net ?


I mean , we were getting , uh , lots better recognition using that , than  Of course , you do have the problem that , um , u i  we are not able to increase the number of Gaussians , uh , or anything to , uh , uh , to match anything. So we 're only improving the training of our feature set , but that 's still probably something.

So you 're saying , add the Macrophone data to the training of the neural net ? The tandem net ?

Yeah , that 's the only place that we can train.


So you 're saying , add the Macrophone data to the training of the neural net ? The tandem net ?

Yeah , that 's the only place that we can train.

We can't train the other stuff with anything other than the standard amount ,


Yeah , that 's the only place that we can train.

We can't train the other stuff with anything other than the standard amount ,


We can't train the other stuff with anything other than the standard amount ,


We can't train the other stuff with anything other than the standard amount ,

What  what was it trained on again ? The one that you used ?


What  what was it trained on again ? The one that you used ?


Um , Uh , it 's , uh , five hundred hidden units. And


Um , Uh , it 's , uh , five hundred hidden units. And

And again , you did experiments back then where you made it bigger and it  and that was  that was sort of the threshold point. Much less than that , it was worse ,


And again , you did experiments back then where you made it bigger and it  and that was  that was sort of the threshold point. Much less than that , it was worse ,


much more than that , it wasn't much better. Hmm.


much more than that , it wasn't much better. Hmm.

So is it  is it though the performance , big relation in the high ma high mismatch has something to do with the , uh , cleaning up that you  that is done on the TIMIT after adding noise ?


much more than that , it wasn't much better. Hmm.

So is it  is it though the performance , big relation in the high ma high mismatch has something to do with the , uh , cleaning up that you  that is done on the TIMIT after adding noise ?

So  it 's  i All the noises are from the TI - digits ,


So is it  is it though the performance , big relation in the high ma high mismatch has something to do with the , uh , cleaning up that you  that is done on the TIMIT after adding noise ?

So  it 's  i All the noises are from the TI - digits ,


So  it 's  i All the noises are from the TI - digits ,


Well , it it 's like the high mismatch of the SpeechDat - Car after cleaning up , maybe having more noise than the  the training set of TIMIT after clean  s after you do the noise clean - up.


Well , it it 's like the high mismatch of the SpeechDat - Car after cleaning up , maybe having more noise than the  the training set of TIMIT after clean  s after you do the noise clean - up.

I mean , earlier you never had any compensation , you just trained it straight away.


I mean , earlier you never had any compensation , you just trained it straight away.

So it had like all these different conditions of S N Rs , actually in their training set of neural net.


So it had like all these different conditions of S N Rs , actually in their training set of neural net.

But after cleaning up you have now a different set of S N Rs , right ?


But after cleaning up you have now a different set of S N Rs , right ?


And  is it something to do with the mismatch that  that 's created after the cleaning up , like the high mismatch


And  is it something to do with the mismatch that  that 's created after the cleaning up , like the high mismatch

You mean the  the most noisy occurrences on SpeechDat - Car might be a lot more noisy than


And  is it something to do with the mismatch that  that 's created after the cleaning up , like the high mismatch

You mean the  the most noisy occurrences on SpeechDat - Car might be a lot more noisy than

Mm - hmm. Of  that  I mean , the SNR after the noise compensation of the SpeechDat - Car.


And  is it something to do with the mismatch that  that 's created after the cleaning up , like the high mismatch

You mean the  the most noisy occurrences on SpeechDat - Car might be a lot more noisy than

Mm - hmm. Of  that  I mean , the SNR after the noise compensation of the SpeechDat - Car.

Oh , so  Right. So the training  the  the neural net is being trained with noise compensated stuff.


You mean the  the most noisy occurrences on SpeechDat - Car might be a lot more noisy than

Mm - hmm. Of  that  I mean , the SNR after the noise compensation of the SpeechDat - Car.

Oh , so  Right. So the training  the  the neural net is being trained with noise compensated stuff.


Mm - hmm. Of  that  I mean , the SNR after the noise compensation of the SpeechDat - Car.

Oh , so  Right. So the training  the  the neural net is being trained with noise compensated stuff.


Oh , so  Right. So the training  the  the neural net is being trained with noise compensated stuff.


but , uh , you 're saying  Yeah , the noisier ones are still going to be , even after our noise compensation , are still gonna be pretty noisy.


but , uh , you 're saying  Yeah , the noisier ones are still going to be , even after our noise compensation , are still gonna be pretty noisy.

Yeah , so now the after - noise compensation the neural net is seeing a different set of S N Rs than that was originally there in the training set. Of TIMIT. Because in the TIMIT it was zero to some clean.


Yeah , so now the after - noise compensation the neural net is seeing a different set of S N Rs than that was originally there in the training set. Of TIMIT. Because in the TIMIT it was zero to some clean.


Yeah , so now the after - noise compensation the neural net is seeing a different set of S N Rs than that was originally there in the training set. Of TIMIT. Because in the TIMIT it was zero to some clean.

So the net saw all the SNR @ @ conditions.


So the net saw all the SNR @ @ conditions.

Now after cleaning up it 's a different set of SNR.


Now after cleaning up it 's a different set of SNR.

And that SNR may not be , like , com covering the whole set of S N Rs that you 're getting in the SpeechDat - Car.


Now after cleaning up it 's a different set of SNR.

And that SNR may not be , like , com covering the whole set of S N Rs that you 're getting in the SpeechDat - Car.

Right , but the SpeechDat - Car data that you 're seeing is also reduced in noise by the noise compensation.


And that SNR may not be , like , com covering the whole set of S N Rs that you 're getting in the SpeechDat - Car.

Right , but the SpeechDat - Car data that you 're seeing is also reduced in noise by the noise compensation.


And that SNR may not be , like , com covering the whole set of S N Rs that you 're getting in the SpeechDat - Car.

Right , but the SpeechDat - Car data that you 're seeing is also reduced in noise by the noise compensation.

Yeah , yeah , yeah , yeah , it is. But , I 'm saying , there could be some  some issues of


Right , but the SpeechDat - Car data that you 're seeing is also reduced in noise by the noise compensation.

Yeah , yeah , yeah , yeah , it is. But , I 'm saying , there could be some  some issues of


Yeah , yeah , yeah , yeah , it is. But , I 'm saying , there could be some  some issues of


Well , if the initial range of SNR is different , we  the problem was already there before. And


Well , if the initial range of SNR is different , we  the problem was already there before. And

Yeah , I mean , it depends on whether you believe that the noise compensation is equally reducing the noise on the test set and the training set.


Yeah , I mean , it depends on whether you believe that the noise compensation is equally reducing the noise on the test set and the training set.


Right ? I mean , you 're saying there 's a mismatch in noise that wasn't there before ,


Right ? I mean , you 're saying there 's a mismatch in noise that wasn't there before ,

but if they were both the same before , then if they were both reduic reduced equally , then , there would not be a mismatch.


but if they were both the same before , then if they were both reduic reduced equally , then , there would not be a mismatch.

So , I mean , this may be  Heaven forbid , this noise compensation process may be imperfect , but. Uh , so maybe it 's treating some things differently.


So , I mean , this may be  Heaven forbid , this noise compensation process may be imperfect , but. Uh , so maybe it 's treating some things differently.

Well , I  I don't know. I  I just  that could be seen from the TI - digits , uh , testing condition because , um , the noises are from the TI - digits , right ? Noise


Well , I  I don't know. I  I just  that could be seen from the TI - digits , uh , testing condition because , um , the noises are from the TI - digits , right ? Noise

So cleaning up the TI - digits and if the performance goes down in the TI - digits mismatch  high mismatch like this


So cleaning up the TI - digits and if the performance goes down in the TI - digits mismatch  high mismatch like this

on a clean training , or zero DB testing.


on a clean training , or zero DB testing.


I mean , the Macrophone data , um , I think , you know , it was recorded over many different telephones.


I mean , the Macrophone data , um , I think , you know , it was recorded over many different telephones.

And , um , so , there 's lots of different kinds of acoustic conditions. I mean , it 's not artificially added noise or anything. So it 's not the same. I don't think there 's anybody recording over a car from a car , but  I think it 's  it 's varied enough that if  if doing this adjustments , uh , and playing around with it doesn't , uh , make it better , the most  uh , it seems like the most obvious thing to do is to improve the training set. Um  I mean , what we were  uh  the condition  It  it gave us an enormous amount of improvement in what we were doing with Meeting Recorder digits , even though there , again , these m Macrophone digits were very , very different from , uh , what we were going on here. I mean , we weren't talking over a telephone here. But it was just  I think just having a  a nice variation in acoustic conditions was just a good thing.


And , um , so , there 's lots of different kinds of acoustic conditions. I mean , it 's not artificially added noise or anything. So it 's not the same. I don't think there 's anybody recording over a car from a car , but  I think it 's  it 's varied enough that if  if doing this adjustments , uh , and playing around with it doesn't , uh , make it better , the most  uh , it seems like the most obvious thing to do is to improve the training set. Um  I mean , what we were  uh  the condition  It  it gave us an enormous amount of improvement in what we were doing with Meeting Recorder digits , even though there , again , these m Macrophone digits were very , very different from , uh , what we were going on here. I mean , we weren't talking over a telephone here. But it was just  I think just having a  a nice variation in acoustic conditions was just a good thing.


And , um , so , there 's lots of different kinds of acoustic conditions. I mean , it 's not artificially added noise or anything. So it 's not the same. I don't think there 's anybody recording over a car from a car , but  I think it 's  it 's varied enough that if  if doing this adjustments , uh , and playing around with it doesn't , uh , make it better , the most  uh , it seems like the most obvious thing to do is to improve the training set. Um  I mean , what we were  uh  the condition  It  it gave us an enormous amount of improvement in what we were doing with Meeting Recorder digits , even though there , again , these m Macrophone digits were very , very different from , uh , what we were going on here. I mean , we weren't talking over a telephone here. But it was just  I think just having a  a nice variation in acoustic conditions was just a good thing.

Yeah , actually  to s eh , what I observed in the HM case is that the number of deletion dramatically increases. It  it doubles.


Yeah , actually  to s eh , what I observed in the HM case is that the number of deletion dramatically increases. It  it doubles.


Yeah , actually  to s eh , what I observed in the HM case is that the number of deletion dramatically increases. It  it doubles.

When I added the num the neural network it doubles the number of deletions. Yeah , so I don't you know  how to interpret that , but , mmm


When I added the num the neural network it doubles the number of deletions. Yeah , so I don't you know  how to interpret that , but , mmm

And  and did  an other numbers stay the same ? Insertion substitutions stay the same ?


And  and did  an other numbers stay the same ? Insertion substitutions stay the same ?


And  and did  an other numbers stay the same ? Insertion substitutions stay the same ?

they  maybe they are a little bit uh , lower.


they  maybe they are a little bit uh , lower.


they  maybe they are a little bit uh , lower.

Did they increase the number of deletions even for the cases that got better ?


Did they increase the number of deletions even for the cases that got better ?


And it  Remind me again , the " highly mismatched " means that the


And it  Remind me again , the " highly mismatched " means that the

It 's clean training  Well , close microphone training and distant microphone , um , high speed , I think.


It 's clean training  Well , close microphone training and distant microphone , um , high speed , I think.


It 's clean training  Well , close microphone training and distant microphone , um , high speed , I think.

Well  The most noisy cases are the distant microphone for testing.


It 's clean training  Well , close microphone training and distant microphone , um , high speed , I think.

Well  The most noisy cases are the distant microphone for testing.

Right. So  Well , maybe the noise subtraction is subtracting off speech.


Well  The most noisy cases are the distant microphone for testing.

Right. So  Well , maybe the noise subtraction is subtracting off speech.


Right. So  Well , maybe the noise subtraction is subtracting off speech.

But  Yeah. I mean , but without the neural network it 's  well , it 's better. It 's just when we add the neural networks.


But  Yeah. I mean , but without the neural network it 's  well , it 's better. It 's just when we add the neural networks.


Well that  that says that , you know , the , um  the models in  in , uh , the recognizer are really paying attention to the neural net features.


But , yeah , actually   the TIMIT noises  are sort of a range of noises and they 're not so much the stationary driving kind of noises , right ? It 's  it 's pretty different. Isn't it ?


But , yeah , actually   the TIMIT noises  are sort of a range of noises and they 're not so much the stationary driving kind of noises , right ? It 's  it 's pretty different. Isn't it ?

Uh , there is a car noise. So there are f just four noises. Um , uh , " Car " , I think , " Babble " ,


Uh , there is a car noise. So there are f just four noises. Um , uh , " Car " , I think , " Babble " ,


So  it 's mostly  Well , " Car " is stationary ,


So  it 's mostly  Well , " Car " is stationary ,

" Babble " , it 's a stationary background plus some voices ,


" Babble " , it 's a stationary background plus some voices ,

some speech over it. And the other two are rather stationary also.


" Babble " , it 's a stationary background plus some voices ,

some speech over it. And the other two are rather stationary also.

Well , I  I think that if you run it  Actually , you  maybe you remember this. When you  in  in the old experiments when you ran with the neural net only , and didn't have this side path , um , uh , with the  the pure features as well , did it make things better to have the neural net ?


some speech over it. And the other two are rather stationary also.

Well , I  I think that if you run it  Actually , you  maybe you remember this. When you  in  in the old experiments when you ran with the neural net only , and didn't have this side path , um , uh , with the  the pure features as well , did it make things better to have the neural net ?


Well , I  I think that if you run it  Actually , you  maybe you remember this. When you  in  in the old experiments when you ran with the neural net only , and didn't have this side path , um , uh , with the  the pure features as well , did it make things better to have the neural net ?


So , until you put the second path in with the pure features , the neural net wasn't helping at all.


So , until you put the second path in with the pure features , the neural net wasn't helping at all.

It was helping , uh , if the features are b were bad ,


It was helping , uh , if the features are b were bad ,


C Cs. as soon as we added LDA on - line normalization , and  all these things , then


C Cs. as soon as we added LDA on - line normalization , and  all these things , then

They were doing similar enough things. Well , I still think it would be k sort of interesting to see what would happen if you just had the neural net without the side thing.


C Cs. as soon as we added LDA on - line normalization , and  all these things , then

They were doing similar enough things. Well , I still think it would be k sort of interesting to see what would happen if you just had the neural net without the side thing.

And  and the thing I  I have in mind is , uh , maybe you 'll see that the results are not just a little bit worse.


They were doing similar enough things. Well , I still think it would be k sort of interesting to see what would happen if you just had the neural net without the side thing.

And  and the thing I  I have in mind is , uh , maybe you 'll see that the results are not just a little bit worse.


And  and the thing I  I have in mind is , uh , maybe you 'll see that the results are not just a little bit worse.

Maybe that they 're a lot worse. You know ? And , um  But if on the ha other hand , uh , it 's , say , somewhere in between what you 're seeing now and  and  and , uh , what you 'd have with just the pure features , then maybe there is some problem of a  of a , uh , combination of these things , or correlation between them somehow.


Maybe that they 're a lot worse. You know ? And , um  But if on the ha other hand , uh , it 's , say , somewhere in between what you 're seeing now and  and  and , uh , what you 'd have with just the pure features , then maybe there is some problem of a  of a , uh , combination of these things , or correlation between them somehow.

If it really is that the net is hurting you at the moment , then I think the issue is to focus on  on , uh , improving the  the net.


If it really is that the net is hurting you at the moment , then I think the issue is to focus on  on , uh , improving the  the net.


So what 's the overall effe I mean , you haven't done all the experiments but you said it was i somewhat better , say , five percent better , for the first two conditions , and fifteen percent worse for the other one ? But it 's  but of course that one 's weighted lower ,


So what 's the overall effe I mean , you haven't done all the experiments but you said it was i somewhat better , say , five percent better , for the first two conditions , and fifteen percent worse for the other one ? But it 's  but of course that one 's weighted lower ,

I d I  I think it 's  it was one or two percent. That 's not that bad , but it was l like two percent relative worse on SpeechDat - Car. I have to  to check that. Well , I have  I will.


I d I  I think it 's  it was one or two percent. That 's not that bad , but it was l like two percent relative worse on SpeechDat - Car. I have to  to check that. Well , I have  I will.

Well , it will  overall it will be still better even if it is fifteen percent worse , because the fifteen percent worse is given like f w twenty - five  point two five eight.


Well , it will  overall it will be still better even if it is fifteen percent worse , because the fifteen percent worse is given like f w twenty - five  point two five eight.

Right. So the  so the worst it could be , if the others were exactly the same , is four ,


Right. So the  so the worst it could be , if the others were exactly the same , is four ,


Right. So the  so the worst it could be , if the others were exactly the same , is four ,

and  and , uh , in fact since the others are somewhat better


Right. So the  so the worst it could be , if the others were exactly the same , is four ,

and  and , uh , in fact since the others are somewhat better

Yeah , so it 's four. Is i So either it 'll get cancelled out , or you 'll get , like , almost the same.


and  and , uh , in fact since the others are somewhat better

Yeah , so it 's four. Is i So either it 'll get cancelled out , or you 'll get , like , almost the same.


Yeah , so it 's four. Is i So either it 'll get cancelled out , or you 'll get , like , almost the same.


Yeah , it should be pretty close to cancelled out.


Yeah , it should be pretty close to cancelled out.

You know , I 've been wondering about something.


You know , I 've been wondering about something.

In the , um  a lot of the , um  the Hub - five systems , um , recently have been using LDA. and  and they , um  They run LDA on the features right before they train the models. So there 's the  the LDA is  is right there before the H M


In the , um  a lot of the , um  the Hub - five systems , um , recently have been using LDA. and  and they , um  They run LDA on the features right before they train the models. So there 's the  the LDA is  is right there before the H M

So , you guys are using LDA but it seems like it 's pretty far back in the process.


In the , um  a lot of the , um  the Hub - five systems , um , recently have been using LDA. and  and they , um  They run LDA on the features right before they train the models. So there 's the  the LDA is  is right there before the H M

So , you guys are using LDA but it seems like it 's pretty far back in the process.

Uh , this LDA is different from the LDA that you are talking about. The LDA that you  saying is , like , you take a block of features , like nine frames or something ,  and then do an LDA on it ,


So , you guys are using LDA but it seems like it 's pretty far back in the process.

Uh , this LDA is different from the LDA that you are talking about. The LDA that you  saying is , like , you take a block of features , like nine frames or something ,  and then do an LDA on it ,


So , you guys are using LDA but it seems like it 's pretty far back in the process.

Uh , this LDA is different from the LDA that you are talking about. The LDA that you  saying is , like , you take a block of features , like nine frames or something ,  and then do an LDA on it ,

and then reduce the dimensionality to something like twenty - four or something like that.


Uh , this LDA is different from the LDA that you are talking about. The LDA that you  saying is , like , you take a block of features , like nine frames or something ,  and then do an LDA on it ,

and then reduce the dimensionality to something like twenty - four or something like that.


and then reduce the dimensionality to something like twenty - four or something like that.


and then reduce the dimensionality to something like twenty - four or something like that.

I mean , it 's  you know , you 're just basically i


I mean , it 's  you know , you 're just basically i

Yeah , so this is like a two d two dimensional tile.


I mean , it 's  you know , you 're just basically i

Yeah , so this is like a two d two dimensional tile.

You 're shifting the feature space. Yeah.


I mean , it 's  you know , you 're just basically i

Yeah , so this is like a two d two dimensional tile.

You 're shifting the feature space. Yeah.

So this is a two dimensional tile. And the LDA that we are f applying is only in time , not in frequency  high cost frequency. So it 's like  more like a filtering in time , rather than doing a r


Yeah , so this is like a two d two dimensional tile.

You 're shifting the feature space. Yeah.

So this is a two dimensional tile. And the LDA that we are f applying is only in time , not in frequency  high cost frequency. So it 's like  more like a filtering in time , rather than doing a r

Ah. OK. So what i what about , um  i u what i w I mean , I don't know if this is a good idea or not , but what if you put  ran the other kind of LDA , uh , on your features right before they go into the HMM ?


You 're shifting the feature space. Yeah.

So this is a two dimensional tile. And the LDA that we are f applying is only in time , not in frequency  high cost frequency. So it 's like  more like a filtering in time , rather than doing a r

Ah. OK. So what i what about , um  i u what i w I mean , I don't know if this is a good idea or not , but what if you put  ran the other kind of LDA , uh , on your features right before they go into the HMM ?


So this is a two dimensional tile. And the LDA that we are f applying is only in time , not in frequency  high cost frequency. So it 's like  more like a filtering in time , rather than doing a r

Ah. OK. So what i what about , um  i u what i w I mean , I don't know if this is a good idea or not , but what if you put  ran the other kind of LDA , uh , on your features right before they go into the HMM ?


Ah. OK. So what i what about , um  i u what i w I mean , I don't know if this is a good idea or not , but what if you put  ran the other kind of LDA , uh , on your features right before they go into the HMM ?

Well. What do we do with the ANN is  is something like that except that it 's not linear. But it 's  it 's like a nonlinear discriminant analysis.


Well. What do we do with the ANN is  is something like that except that it 's not linear. But it 's  it 's like a nonlinear discriminant analysis.

Yeah. Right , it 's the  It 's  Right. The  So  Yeah , so it 's sort of like


Well. What do we do with the ANN is  is something like that except that it 's not linear. But it 's  it 's like a nonlinear discriminant analysis.

Yeah. Right , it 's the  It 's  Right. The  So  Yeah , so it 's sort of like

The tandem stuff is kind of like i nonlinear LDA.


Yeah. Right , it 's the  It 's  Right. The  So  Yeah , so it 's sort of like

The tandem stuff is kind of like i nonlinear LDA.


The tandem stuff is kind of like i nonlinear LDA.


But I mean , w but the other features that you have , um , th the non - tandem ones ,


But I mean , w but the other features that you have , um , th the non - tandem ones ,

Uh. Mm - hmm. Yeah , I know. That  that  Yeah. Well , in the proposal , they were transformed u using PCA , but


But I mean , w but the other features that you have , um , th the non - tandem ones ,

Uh. Mm - hmm. Yeah , I know. That  that  Yeah. Well , in the proposal , they were transformed u using PCA , but

Yeah , it might be that LDA could be better.


Uh. Mm - hmm. Yeah , I know. That  that  Yeah. Well , in the proposal , they were transformed u using PCA , but

Yeah , it might be that LDA could be better.

The a the argument i is kind of i in  and it 's not like we really know , but the argument anyway is that , um , uh , we always have the prob I mean , discriminative things are good. LDA , neural nets , they 're good.


Yeah , it might be that LDA could be better.

The a the argument i is kind of i in  and it 's not like we really know , but the argument anyway is that , um , uh , we always have the prob I mean , discriminative things are good. LDA , neural nets , they 're good.


Yeah , it might be that LDA could be better.

The a the argument i is kind of i in  and it 's not like we really know , but the argument anyway is that , um , uh , we always have the prob I mean , discriminative things are good. LDA , neural nets , they 're good.

Uh , they 're good because you  you  you learn to distinguish between these categories that you want to be good at distinguishing between. And PCA doesn't do that. It  PAC - PCA  low - order PCA throws away pieces that are uh , maybe not  not gonna be helpful just because they 're small , basically.


The a the argument i is kind of i in  and it 's not like we really know , but the argument anyway is that , um , uh , we always have the prob I mean , discriminative things are good. LDA , neural nets , they 're good.

Uh , they 're good because you  you  you learn to distinguish between these categories that you want to be good at distinguishing between. And PCA doesn't do that. It  PAC - PCA  low - order PCA throws away pieces that are uh , maybe not  not gonna be helpful just because they 're small , basically.


Uh , they 're good because you  you  you learn to distinguish between these categories that you want to be good at distinguishing between. And PCA doesn't do that. It  PAC - PCA  low - order PCA throws away pieces that are uh , maybe not  not gonna be helpful just because they 're small , basically.

But , uh , the problem is , training sets aren't perfect and testing sets are different. So you f you  you face the potential problem with discriminative stuff , be it LDA or neural nets , that you are training to discriminate between categories in one space but what you 're really gonna be g getting is  is something else.


But , uh , the problem is , training sets aren't perfect and testing sets are different. So you f you  you face the potential problem with discriminative stuff , be it LDA or neural nets , that you are training to discriminate between categories in one space but what you 're really gonna be g getting is  is something else.

And so , uh , Stephane 's idea was , uh , let 's feed , uh , both this discriminatively trained thing and something that 's not. So you have a good set of features that everybody 's worked really hard to make ,


And so , uh , Stephane 's idea was , uh , let 's feed , uh , both this discriminatively trained thing and something that 's not. So you have a good set of features that everybody 's worked really hard to make ,

and then , uh , you  you discriminately train it , but you also take the path that  that doesn't have that ,


and then , uh , you  you discriminately train it , but you also take the path that  that doesn't have that ,

and putting those in together. And that  that seem So it 's kind of like a combination of the  uh , what , uh , Dan has been calling , you know , a feature  uh , you know , a feature combination versus posterior combination or something. It 's  it 's , you know , you have the posterior combination but then you get the features from that and use them as a feature combination with these  these other things. And that seemed , at least in the last one , as he was just saying , he  he  when he only did discriminative stuff , i it actually was  was  it didn't help at all in this particular case.


and putting those in together. And that  that seem So it 's kind of like a combination of the  uh , what , uh , Dan has been calling , you know , a feature  uh , you know , a feature combination versus posterior combination or something. It 's  it 's , you know , you have the posterior combination but then you get the features from that and use them as a feature combination with these  these other things. And that seemed , at least in the last one , as he was just saying , he  he  when he only did discriminative stuff , i it actually was  was  it didn't help at all in this particular case.

There was enough of a difference , I guess , between the testing and training. But by having them both there  The fact is some of the time , the discriminative stuff is gonna help you.


There was enough of a difference , I guess , between the testing and training. But by having them both there  The fact is some of the time , the discriminative stuff is gonna help you.

And some of the time it 's going to hurt you ,


And some of the time it 's going to hurt you ,

and by combining two information sources if , you know  if  if


And some of the time it 's going to hurt you ,

and by combining two information sources if , you know  if  if

So you wouldn't necessarily then want to do LDA on the non - tandem features because now you 're doing something to them that


and by combining two information sources if , you know  if  if

So you wouldn't necessarily then want to do LDA on the non - tandem features because now you 're doing something to them that

That i i I think that 's counter to that idea.


So you wouldn't necessarily then want to do LDA on the non - tandem features because now you 're doing something to them that

That i i I think that 's counter to that idea.

Now , again , it 's  we 're just trying these different things. We don't really know what 's gonna work best. But if that 's the hypothesis , at least it would be counter to that hypothesis to do that.


That i i I think that 's counter to that idea.

Now , again , it 's  we 're just trying these different things. We don't really know what 's gonna work best. But if that 's the hypothesis , at least it would be counter to that hypothesis to do that.


Now , again , it 's  we 're just trying these different things. We don't really know what 's gonna work best. But if that 's the hypothesis , at least it would be counter to that hypothesis to do that.

Um , and in principle you would think that the neural net would do better at the discriminant part than LDA.


Um , and in principle you would think that the neural net would do better at the discriminant part than LDA.


Um , and in principle you would think that the neural net would do better at the discriminant part than LDA.

Yeah. Exactly. I mean , we , uh  we were getting ready to do the tandem , uh , stuff for the Hub - five system , and , um , Andreas and I talked about it , and the idea w the thought was , " Well , uh , yeah , that i you know  th the neural net should be better , but we should at least have uh , a number , you know , to show that we did try the LDA in place of the neural net , so that we can you know , show a clear path.


Yeah. Exactly. I mean , we , uh  we were getting ready to do the tandem , uh , stuff for the Hub - five system , and , um , Andreas and I talked about it , and the idea w the thought was , " Well , uh , yeah , that i you know  th the neural net should be better , but we should at least have uh , a number , you know , to show that we did try the LDA in place of the neural net , so that we can you know , show a clear path.


Yeah. Exactly. I mean , we , uh  we were getting ready to do the tandem , uh , stuff for the Hub - five system , and , um , Andreas and I talked about it , and the idea w the thought was , " Well , uh , yeah , that i you know  th the neural net should be better , but we should at least have uh , a number , you know , to show that we did try the LDA in place of the neural net , so that we can you know , show a clear path.

You know , that you have it without it , then you have the LDA , then you have the neural net , and you can see , theoretically. So. I was just wondering  I  I


You know , that you have it without it , then you have the LDA , then you have the neural net , and you can see , theoretically. So. I was just wondering  I  I


That 's what  that 's what we 're gonna do next as soon as I finish this other thing. So.


That 's what  that 's what we 're gonna do next as soon as I finish this other thing. So.

Yeah. Yeah. No , well , that 's a good idea. I  I


Yeah. Yeah. No , well , that 's a good idea. I  I


No , no , but it might not  not even be true.


No , no , but it might not  not even be true.

I mean , it 's  it 's  it 's  it 's  it 's a great idea. I mean , one of the things that always disturbed me , uh , in the  the resurgence of neural nets that happened in the eighties was that , um , a lot of people  Because neural nets were pretty easy to  to use  a lot of people were just using them for all sorts of things without , uh , looking at all into the linear , uh  uh , versions of them.


I mean , it 's  it 's  it 's  it 's  it 's a great idea. I mean , one of the things that always disturbed me , uh , in the  the resurgence of neural nets that happened in the eighties was that , um , a lot of people  Because neural nets were pretty easy to  to use  a lot of people were just using them for all sorts of things without , uh , looking at all into the linear , uh  uh , versions of them.

And , uh , people were doing recurrent nets but not looking at IIR filters , and  You know , I mean , uh , so I think , yeah , it 's definitely a good idea to try it.


I mean , it 's  it 's  it 's  it 's  it 's a great idea. I mean , one of the things that always disturbed me , uh , in the  the resurgence of neural nets that happened in the eighties was that , um , a lot of people  Because neural nets were pretty easy to  to use  a lot of people were just using them for all sorts of things without , uh , looking at all into the linear , uh  uh , versions of them.

And , uh , people were doing recurrent nets but not looking at IIR filters , and  You know , I mean , uh , so I think , yeah , it 's definitely a good idea to try it.

Yeah , and everybody 's putting that on their  systems now , and so , I that 's what made me wonder about this ,


And , uh , people were doing recurrent nets but not looking at IIR filters , and  You know , I mean , uh , so I think , yeah , it 's definitely a good idea to try it.

Yeah , and everybody 's putting that on their  systems now , and so , I that 's what made me wonder about this ,

Well , they 've been putting them in their systems off and on for ten years ,


Yeah , and everybody 's putting that on their  systems now , and so , I that 's what made me wonder about this ,

Well , they 've been putting them in their systems off and on for ten years ,


Well , they 've been putting them in their systems off and on for ten years ,

Yeah , what I mean is it 's  it 's like in the Hub - five evaluations , you know , and you read the system descriptions and everybody 's got ,  you know , LDA on their features.


Yeah , what I mean is it 's  it 's like in the Hub - five evaluations , you know , and you read the system descriptions and everybody 's got ,  you know , LDA on their features.


It 's the transformation they 're estimating on  Well , they are trained on the same data as the final HMM are.


It 's the transformation they 're estimating on  Well , they are trained on the same data as the final HMM are.

Yeah , so it 's different. Yeah , exactly. Cuz they don't have these , you know , mismatches that  that you guys have.


It 's the transformation they 're estimating on  Well , they are trained on the same data as the final HMM are.

Yeah , so it 's different. Yeah , exactly. Cuz they don't have these , you know , mismatches that  that you guys have.

So that 's why I was wondering if maybe it 's not even a good idea.


Yeah , so it 's different. Yeah , exactly. Cuz they don't have these , you know , mismatches that  that you guys have.

So that 's why I was wondering if maybe it 's not even a good idea.


So that 's why I was wondering if maybe it 's not even a good idea.

I don't know. I  I don't know enough about it ,


I don't know. I  I don't know enough about it ,


I don't know. I  I don't know enough about it ,

I mean , part of why  I  I think part of why you were getting into the KLT  Y you were describing to me at one point that you wanted to see if , uh , you know , getting good orthogonal features was  and combining the  the different temporal ranges  was the key thing that was happening or whether it was this discriminant thing , right ? So you were just trying  I think you r I mean , this is  it doesn't have the LDA aspect but th as far as the orthogonalizing transformation , you were trying that at one point , right ?


I mean , part of why  I  I think part of why you were getting into the KLT  Y you were describing to me at one point that you wanted to see if , uh , you know , getting good orthogonal features was  and combining the  the different temporal ranges  was the key thing that was happening or whether it was this discriminant thing , right ? So you were just trying  I think you r I mean , this is  it doesn't have the LDA aspect but th as far as the orthogonalizing transformation , you were trying that at one point , right ?


Does something. It doesn't work as well. Yeah. Yeah.


Does something. It doesn't work as well. Yeah. Yeah.

So , yeah , I 've been exploring a parallel VAD without neural network with , like , less latency using SNR and energy , um , after the cleaning up. So what I 'd been trying was , um , uh  After the b after the noise compensation , n I was trying t to f find a f feature based on the ratio of the energies , that is , cl after clean and before clean. So that if  if they are , like , pretty c close to one , which means it 's speech. And if it is n if it is close to zero , which is  So it 's like a scale @ @ probability value. So I was trying , uh , with full band and multiple bands , m ps uh  separating them to different frequency bands and deriving separate decisions on each bands , and trying to combine them. Uh , the advantage being like it doesn't have the latency of the neural net if it  if it can


Does something. It doesn't work as well. Yeah. Yeah.

So , yeah , I 've been exploring a parallel VAD without neural network with , like , less latency using SNR and energy , um , after the cleaning up. So what I 'd been trying was , um , uh  After the b after the noise compensation , n I was trying t to f find a f feature based on the ratio of the energies , that is , cl after clean and before clean. So that if  if they are , like , pretty c close to one , which means it 's speech. And if it is n if it is close to zero , which is  So it 's like a scale @ @ probability value. So I was trying , uh , with full band and multiple bands , m ps uh  separating them to different frequency bands and deriving separate decisions on each bands , and trying to combine them. Uh , the advantage being like it doesn't have the latency of the neural net if it  if it can

g And  it gave me like , uh , one point  One  more than one percent relative improvement. So , from fifty - three point six it went to fifty f four point eight. So it 's , like , only slightly more than a percent improvement ,


So , yeah , I 've been exploring a parallel VAD without neural network with , like , less latency using SNR and energy , um , after the cleaning up. So what I 'd been trying was , um , uh  After the b after the noise compensation , n I was trying t to f find a f feature based on the ratio of the energies , that is , cl after clean and before clean. So that if  if they are , like , pretty c close to one , which means it 's speech. And if it is n if it is close to zero , which is  So it 's like a scale @ @ probability value. So I was trying , uh , with full band and multiple bands , m ps uh  separating them to different frequency bands and deriving separate decisions on each bands , and trying to combine them. Uh , the advantage being like it doesn't have the latency of the neural net if it  if it can

g And  it gave me like , uh , one point  One  more than one percent relative improvement. So , from fifty - three point six it went to fifty f four point eight. So it 's , like , only slightly more than a percent improvement ,


g And  it gave me like , uh , one point  One  more than one percent relative improvement. So , from fifty - three point six it went to fifty f four point eight. So it 's , like , only slightly more than a percent improvement ,

just like  Which means that it 's  it 's doing a slightly better job than the previous VAD ,


just like  Which means that it 's  it 's doing a slightly better job than the previous VAD ,


does it still have the median  filter stuff ?


Yeah , so d with the delay , that 's gone is the input , which is the sixty millisecond. The forty plus  twenty.


Yeah , so d with the delay , that 's gone is the input , which is the sixty millisecond. The forty plus  twenty.

At the input of the neural net you have this , uh , f nine frames of context plus the delta.


At the input of the neural net you have this , uh , f nine frames of context plus the delta.


Uh , so the delay is only the forty millisecond of the noise cleaning , plus the hundred millisecond smoothing at the output.


Uh , so the delay is only the forty millisecond of the noise cleaning , plus the hundred millisecond smoothing at the output.

Um. So. Yeah. So the  the  di the biggest  The problem f for me was to find a consistent threshold that works  well across the different databases , because I t I try to make it work on tr SpeechDat - Car


Um. So. Yeah. So the  the  di the biggest  The problem f for me was to find a consistent threshold that works  well across the different databases , because I t I try to make it work on tr SpeechDat - Car

and it fails on TI - digits , or if I try to make it work on that it 's just the Italian or something , it doesn't work on the Finnish.


and it fails on TI - digits , or if I try to make it work on that it 's just the Italian or something , it doesn't work on the Finnish.

So , um. So there are  there was , like , some problem in balancing the deletions and insertions when I try different thresholds.


So , um. So there are  there was , like , some problem in balancing the deletions and insertions when I try different thresholds.

So  The  I 'm still trying to make it better by using some other features from the  after the p clean up  maybe , some , uh , correlation  auto - correlation or some s additional features of  to mainly the improvement of the VAD. I 've been trying.


So , um. So there are  there was , like , some problem in balancing the deletions and insertions when I try different thresholds.

So  The  I 'm still trying to make it better by using some other features from the  after the p clean up  maybe , some , uh , correlation  auto - correlation or some s additional features of  to mainly the improvement of the VAD. I 've been trying.

Now this  this  this , uh , " before and after clean " , it sounds like you think that 's a good feature. That  that , it  you th think that the , uh  the  i it appears to be a good feature , right ?


So  The  I 'm still trying to make it better by using some other features from the  after the p clean up  maybe , some , uh , correlation  auto - correlation or some s additional features of  to mainly the improvement of the VAD. I 've been trying.

Now this  this  this , uh , " before and after clean " , it sounds like you think that 's a good feature. That  that , it  you th think that the , uh  the  i it appears to be a good feature , right ?


Now this  this  this , uh , " before and after clean " , it sounds like you think that 's a good feature. That  that , it  you th think that the , uh  the  i it appears to be a good feature , right ?


Yeah , so  Yeah , so that 's the  Yeah. So we 've been thinking about putting it into the neural net also.


Yeah , so  Yeah , so that 's the  Yeah. So we 've been thinking about putting it into the neural net also.

Then you don't have to worry about the thresholds and


Then you don't have to worry about the thresholds and


Yeah. So if we  if we can live with the latency or cut the latencies elsewhere , then  then that would be a , uh , good thing.


Yeah. So if we  if we can live with the latency or cut the latencies elsewhere , then  then that would be a , uh , good thing.

Um , anybody  has anybody  you guys or  or Naren , uh , somebody , tried the , uh , um , second th second stream thing ? Uh.


Yeah. So if we  if we can live with the latency or cut the latencies elsewhere , then  then that would be a , uh , good thing.

Um , anybody  has anybody  you guys or  or Naren , uh , somebody , tried the , uh , um , second th second stream thing ? Uh.

Oh , I just  I just h put the second stream in place and , uh ran one experiment , but just like  just to know that everything is fine.


Um , anybody  has anybody  you guys or  or Naren , uh , somebody , tried the , uh , um , second th second stream thing ? Uh.

Oh , I just  I just h put the second stream in place and , uh ran one experiment , but just like  just to know that everything is fine.


Um , anybody  has anybody  you guys or  or Naren , uh , somebody , tried the , uh , um , second th second stream thing ? Uh.

Oh , I just  I just h put the second stream in place and , uh ran one experiment , but just like  just to know that everything is fine.

So it was like , uh , forty - five cepstrum plus twenty - three mel  log mel.


Oh , I just  I just h put the second stream in place and , uh ran one experiment , but just like  just to know that everything is fine.

So it was like , uh , forty - five cepstrum plus twenty - three mel  log mel.


So it was like , uh , forty - five cepstrum plus twenty - three mel  log mel.

And  and , just , like , it gave me the baseline performance of the Aurora , which is like zero improvement.


And  and , just , like , it gave me the baseline performance of the Aurora , which is like zero improvement.

So I just tried it on Italian just to know that everything is  But I  I didn't export anything out of it because it was , like , a weird feature set.


So I just tried it on Italian just to know that everything is  But I  I didn't export anything out of it because it was , like , a weird feature set.


So I just tried it on Italian just to know that everything is  But I  I didn't export anything out of it because it was , like , a weird feature set.

Yeah. Well , what I think , you know , would be more what you 'd want to do is  is  is , uh , put it into another neural net. Right ?


Yeah. Well , what I think , you know , would be more what you 'd want to do is  is  is , uh , put it into another neural net. Right ?


Yeah. Well , what I think , you know , would be more what you 'd want to do is  is  is , uh , put it into another neural net. Right ?

And then  But , yeah , we 're  we 're not quite there yet. So we have to  figure out the neural nets , I guess.


And then  But , yeah , we 're  we 're not quite there yet. So we have to  figure out the neural nets , I guess.


And then  But , yeah , we 're  we 're not quite there yet. So we have to  figure out the neural nets , I guess.

The uh , other thing I was wondering was , um , if the neural net , um , has any  because of the different noise con unseen noise conditions for the neural net , where , like , you train it on those four noise conditions , while you are feeding it with , like , a additional  some four plus some  f few more conditions which it hasn't seen , actually ,


The uh , other thing I was wondering was , um , if the neural net , um , has any  because of the different noise con unseen noise conditions for the neural net , where , like , you train it on those four noise conditions , while you are feeding it with , like , a additional  some four plus some  f few more conditions which it hasn't seen , actually ,


Um  instead of just h having c uh , those cleaned up t cepstrum , sh should we feed some additional information , like  The  the  We have the VAD flag. I mean , should we f feed the VAD flag , also , at the input so that it  it has some additional discriminating information at the input ?


Um  instead of just h having c uh , those cleaned up t cepstrum , sh should we feed some additional information , like  The  the  We have the VAD flag. I mean , should we f feed the VAD flag , also , at the input so that it  it has some additional discriminating information at the input ?

We have the VAD information also available at the back - end.


We have the VAD information also available at the back - end.


We have the VAD information also available at the back - end.

So if it is something the neural net is not able to discriminate the classes


So if it is something the neural net is not able to discriminate the classes

I mean  Because most of it is sil I mean , we have dropped some silence f We have dropped so silence frames ?


I mean  Because most of it is sil I mean , we have dropped some silence f We have dropped so silence frames ?

No , we haven't dropped silence frames still.


No , we haven't dropped silence frames still.


the b b biggest classification would be the speech and silence. So , by having an additional , uh , feature which says " this is speech and this is nonspeech " , I mean , it certainly helps in some unseen noise conditions for the neural net.


the b b biggest classification would be the speech and silence. So , by having an additional , uh , feature which says " this is speech and this is nonspeech " , I mean , it certainly helps in some unseen noise conditions for the neural net.

What  Do y do you have that feature available for the test data ?


the b b biggest classification would be the speech and silence. So , by having an additional , uh , feature which says " this is speech and this is nonspeech " , I mean , it certainly helps in some unseen noise conditions for the neural net.

What  Do y do you have that feature available for the test data ?

Well , I mean , we have  we are transferring the VAD to the back - end  feature to the back - end. Because we are dropping it at the back - end after everything  all the features are computed.


What  Do y do you have that feature available for the test data ?

Well , I mean , we have  we are transferring the VAD to the back - end  feature to the back - end. Because we are dropping it at the back - end after everything  all the features are computed.


Well , I mean , we have  we are transferring the VAD to the back - end  feature to the back - end. Because we are dropping it at the back - end after everything  all the features are computed.


so the neural  so that is coming from a separate neural net or some VAD.


so the neural  so that is coming from a separate neural net or some VAD.

So you 're saying , feed that , also , into  the neural net.


So you 're saying , feed that , also , into  the neural net.

to  Yeah. So it it 's an  additional discriminating information.


to  Yeah. So it it 's an  additional discriminating information.

You could feed it into the neural net. The other thing  you could do is just , um , p modify the , uh , output probabilities of the  of the , uh , uh , um , neural net , tandem neural net ,  based on the fact that you have a silence probability.


You could feed it into the neural net. The other thing  you could do is just , um , p modify the , uh , output probabilities of the  of the , uh , uh , um , neural net , tandem neural net ,  based on the fact that you have a silence probability.


So you have an independent estimator of what the silence probability is , and you could multiply the two things , and renormalize.


So you have an independent estimator of what the silence probability is , and you could multiply the two things , and renormalize.

Uh , I mean , you 'd have to do the nonlinearity part and deal with that. Uh , I mean , go backwards from what the nonlinearity would , you know  would be.


Uh , I mean , you 'd have to do the nonlinearity part and deal with that. Uh , I mean , go backwards from what the nonlinearity would , you know  would be.


But in principle wouldn't it be better to feed it in ? And let the net do that ?


But in principle wouldn't it be better to feed it in ? And let the net do that ?

I mean , let 's put it this way. I mean , y you  you have this complicated system with thousands and thousand parameters


I mean , let 's put it this way. I mean , y you  you have this complicated system with thousands and thousand parameters


I mean , let 's put it this way. I mean , y you  you have this complicated system with thousands and thousand parameters

and you can tell it , uh , " Learn this thing. " Or you can say , " It 's silence ! Go away ! " I mean , I mean , i Doesn't  ? I think  I think the second one sounds a lot more direct.


and you can tell it , uh , " Learn this thing. " Or you can say , " It 's silence ! Go away ! " I mean , I mean , i Doesn't  ? I think  I think the second one sounds a lot more direct.


and you can tell it , uh , " Learn this thing. " Or you can say , " It 's silence ! Go away ! " I mean , I mean , i Doesn't  ? I think  I think the second one sounds a lot more direct.

Right. So , what if you then , uh  since you know this , what if you only use the neural net on the speech portions ?


Right. So , what if you then , uh  since you know this , what if you only use the neural net on the speech portions ?


Right. So , what if you then , uh  since you know this , what if you only use the neural net on the speech portions ?

Well , I guess that 's the same. Uh , that 's similar.


Well , I guess that 's the same. Uh , that 's similar.

Yeah , I mean , y you 'd have to actually run it continuously ,


Well , I guess that 's the same. Uh , that 's similar.

Yeah , I mean , y you 'd have to actually run it continuously ,

But I mean  I mean , train the net only on


Well , I guess that 's the same. Uh , that 's similar.

Yeah , I mean , y you 'd have to actually run it continuously ,

But I mean  I mean , train the net only on

but it 's  @ @  Well , no , you want to train on  on the nonspeech also , because that 's part of what you 're learning in it , to  to  to generate , that it 's  it has to distinguish between.


Yeah , I mean , y you 'd have to actually run it continuously ,

But I mean  I mean , train the net only on

but it 's  @ @  Well , no , you want to train on  on the nonspeech also , because that 's part of what you 're learning in it , to  to  to generate , that it 's  it has to distinguish between.


But I mean  I mean , train the net only on

but it 's  @ @  Well , no , you want to train on  on the nonspeech also , because that 's part of what you 're learning in it , to  to  to generate , that it 's  it has to distinguish between.

But I mean , if you 're gonna  if you 're going to multiply the output of the net by this other decision , uh , would  then you don't care about whether the net makes that distinction , right ?


but it 's  @ @  Well , no , you want to train on  on the nonspeech also , because that 's part of what you 're learning in it , to  to  to generate , that it 's  it has to distinguish between.

But I mean , if you 're gonna  if you 're going to multiply the output of the net by this other decision , uh , would  then you don't care about whether the net makes that distinction , right ?

Well , yeah. But this other thing isn't perfect.


But I mean , if you 're gonna  if you 're going to multiply the output of the net by this other decision , uh , would  then you don't care about whether the net makes that distinction , right ?

Well , yeah. But this other thing isn't perfect.


But I mean , if you 're gonna  if you 're going to multiply the output of the net by this other decision , uh , would  then you don't care about whether the net makes that distinction , right ?

Well , yeah. But this other thing isn't perfect.

So that you bring in some information from the net itself.


Well , yeah. But this other thing isn't perfect.

So that you bring in some information from the net itself.


So that you bring in some information from the net itself.

Yeah. Now the only thing that  that bothers me about all this is that I  I  I  The  the fact  i i It 's sort of bothersome that you 're getting more deletions.


So that you bring in some information from the net itself.

Yeah. Now the only thing that  that bothers me about all this is that I  I  I  The  the fact  i i It 's sort of bothersome that you 're getting more deletions.

Yeah. But  So I might maybe look at , is it due to the fact that um , the probability of the silence at the output of the network , is , uh ,


Yeah. Now the only thing that  that bothers me about all this is that I  I  I  The  the fact  i i It 's sort of bothersome that you 're getting more deletions.

Yeah. But  So I might maybe look at , is it due to the fact that um , the probability of the silence at the output of the network , is , uh ,


Yeah. But  So I might maybe look at , is it due to the fact that um , the probability of the silence at the output of the network , is , uh ,


If it 's the case , then multiplying it again by  i by something ?


Yeah , it  it may be too  it 's too high in a sense , like , everything is more like a , um , flat probability.


Yeah , it  it may be too  it 's too high in a sense , like , everything is more like a , um , flat probability.

So , like , it 's not really doing any distinction between speech and nonspeech


So , like , it 's not really doing any distinction between speech and nonspeech


Be interesting to look at the  Yeah , for the  I wonder if you could do this. But if you look at the , um , highly mism high mismat the output of the net on the high mismatch case and just look at , you know , the distribution versus the  the other ones , do you  do you see more peaks or something ?


Be interesting to look at the  Yeah , for the  I wonder if you could do this. But if you look at the , um , highly mism high mismat the output of the net on the high mismatch case and just look at , you know , the distribution versus the  the other ones , do you  do you see more peaks or something ?

Yeah. Yeah , like the entropy of the  the output ,


Yeah. Yeah , like the entropy of the  the output ,


It  it seems that the VAD network doesn't  Well , it doesn't drop , uh , too many frames because the dele the number of deletion is reasonable. But it 's just when we add the tandem , the final MLP , and then


It  it seems that the VAD network doesn't  Well , it doesn't drop , uh , too many frames because the dele the number of deletion is reasonable. But it 's just when we add the tandem , the final MLP , and then

Yeah. Now the only problem is you don't want to ta I guess wait for the output of the VAD before you can put something into the other system ,


It  it seems that the VAD network doesn't  Well , it doesn't drop , uh , too many frames because the dele the number of deletion is reasonable. But it 's just when we add the tandem , the final MLP , and then

Yeah. Now the only problem is you don't want to ta I guess wait for the output of the VAD before you can put something into the other system ,

cuz that 'll shoot up the latency a lot , right ? Am I missing something here ?


Yeah. Now the only problem is you don't want to ta I guess wait for the output of the VAD before you can put something into the other system ,

cuz that 'll shoot up the latency a lot , right ? Am I missing something here ?


cuz that 'll shoot up the latency a lot , right ? Am I missing something here ?


Yeah. So that 's maybe a problem with what I was just saying. But  but  I I guess


Yeah. So that 's maybe a problem with what I was just saying. But  but  I I guess

But if you were gonna put it in as a feature it means you already have it by the time you get to the tandem net , right ?


Yeah. So that 's maybe a problem with what I was just saying. But  but  I I guess

But if you were gonna put it in as a feature it means you already have it by the time you get to the tandem net , right ?

Um , well. We  w we don't have it , actually ,


But if you were gonna put it in as a feature it means you already have it by the time you get to the tandem net , right ?

Um , well. We  w we don't have it , actually ,

because it 's  it has a high rate energy


Um , well. We  w we don't have it , actually ,

because it 's  it has a high rate energy


because it 's  it has a high rate energy


It 's kind of done in  I mean , some of the things are , not in parallel , but certainly , it would be in parallel with the  with a tandem net.


It 's kind of done in  I mean , some of the things are , not in parallel , but certainly , it would be in parallel with the  with a tandem net.

In time. So maybe , if that doesn't work , um  But it would be interesting to see if that was the problem , anyway. And  and  and then I guess another alternative would be to take the feature that you 're feeding into the VAD , and feeding it into the other one as well.


In time. So maybe , if that doesn't work , um  But it would be interesting to see if that was the problem , anyway. And  and  and then I guess another alternative would be to take the feature that you 're feeding into the VAD , and feeding it into the other one as well.

And then maybe it would just learn  learn it better.


And then maybe it would just learn  learn it better.

Um  But that 's  Yeah , that 's an interesting thing to try to see , if what 's going on is that in the highly mismatched condition , it 's , um , causing deletions by having this silence probability up  up too high ,


Um  But that 's  Yeah , that 's an interesting thing to try to see , if what 's going on is that in the highly mismatched condition , it 's , um , causing deletions by having this silence probability up  up too high ,

at some point where the VAD is saying it 's actually speech.


at some point where the VAD is saying it 's actually speech.


Cuz  Well , the V A if the VAD said  since the VAD is  is  is right a lot , uh


Yeah. Well , we just started working with it. But these are  these are some good ideas I think.