Yeah  so  so this  this is like  So this makes the delay like zero for LDA because it 's completely causal.

So  So I got actually just the results for the Italian for that and that 's like  So the fifty - one point O nine has become forty - eight point O six , which is like three percent relative degradation. So I have like the fifty - one point O nine


So  So I got actually just the results for the Italian for that and that 's like  So the fifty - one point O nine has become forty - eight point O six , which is like three percent relative degradation. So I have like the fifty - one point O nine

and  So. I don't know it f fares for the other conditions. So it 's just like  it 's like a three percent relative degradation , with the


So  So I got actually just the results for the Italian for that and that 's like  So the fifty - one point O nine has become forty - eight point O six , which is like three percent relative degradation. So I have like the fifty - one point O nine

and  So. I don't know it f fares for the other conditions. So it 's just like  it 's like a three percent relative degradation , with the

But  but is there  is there a problem with the one hundred eighty milliseconds ? Or  ?


and  So. I don't know it f fares for the other conditions. So it 's just like  it 's like a three percent relative degradation , with the

But  but is there  is there a problem with the one hundred eighty milliseconds ? Or  ?


But  but is there  is there a problem with the one hundred eighty milliseconds ? Or  ?

Yeah , I mean , I talked to  to  uh , I ta Uh , I talked , uh , about it with  with Hynek. I mean , there is


Yeah , I mean , I talked to  to  uh , I ta Uh , I talked , uh , about it with  with Hynek. I mean , there is

This is  So  So , basically our  our position is  that , um , we shouldn't be unduly constraining the latency at this point because we 're all still experimenting with trying to make the performance better in the presence of noise. Uh , there is a minority in that group who is a arguing  who are arguing for  um , uh , having a further constraining of the latency. So we 're s just continuing to keep aware of what the trade - offs are and , you know , what  what do we gain from having longer or shorter latencies ?


Yeah , I mean , I talked to  to  uh , I ta Uh , I talked , uh , about it with  with Hynek. I mean , there is

This is  So  So , basically our  our position is  that , um , we shouldn't be unduly constraining the latency at this point because we 're all still experimenting with trying to make the performance better in the presence of noise. Uh , there is a minority in that group who is a arguing  who are arguing for  um , uh , having a further constraining of the latency. So we 're s just continuing to keep aware of what the trade - offs are and , you know , what  what do we gain from having longer or shorter latencies ?

But since we always seem to at least get something out of longer latencies not being so constrained , we 're tending to go with that if we 're not told we can't do it.


This is  So  So , basically our  our position is  that , um , we shouldn't be unduly constraining the latency at this point because we 're all still experimenting with trying to make the performance better in the presence of noise. Uh , there is a minority in that group who is a arguing  who are arguing for  um , uh , having a further constraining of the latency. So we 're s just continuing to keep aware of what the trade - offs are and , you know , what  what do we gain from having longer or shorter latencies ?

But since we always seem to at least get something out of longer latencies not being so constrained , we 're tending to go with that if we 're not told we can't do it.

What  where was the , um  the smallest latency of all the systems last time ?


But since we always seem to at least get something out of longer latencies not being so constrained , we 're tending to go with that if we 're not told we can't do it.

What  where was the , um  the smallest latency of all the systems last time ?


What  where was the , um  the smallest latency of all the systems last time ?

Well , France Telecom was  was  was very short latency


Well , France Telecom was  was  was very short latency


It was in the order of thirty milliseconds


Yeah , so it 's possible to get very short latency.


Yeah , so it 's possible to get very short latency.

But , again , we 're  the  the approaches that we 're using are ones that  take advantage of


Yeah , so it 's possible to get very short latency.

But , again , we 're  the  the approaches that we 're using are ones that  take advantage of

Yeah. I was just curious about where we are compared to , you know , the shortest that people have done.


Yeah , so it 's possible to get very short latency.

But , again , we 're  the  the approaches that we 're using are ones that  take advantage of

Yeah. I was just curious about where we are compared to , you know , the shortest that people have done.

But  but I think this thirty milliseconds  they  they did  it did not include the  the delta calculation.


But , again , we 're  the  the approaches that we 're using are ones that  take advantage of

Yeah. I was just curious about where we are compared to , you know , the shortest that people have done.

But  but I think this thirty milliseconds  they  they did  it did not include the  the delta calculation.


Yeah. I was just curious about where we are compared to , you know , the shortest that people have done.

But  but I think this thirty milliseconds  they  they did  it did not include the  the delta calculation.


But  but I think this thirty milliseconds  they  they did  it did not include the  the delta calculation.


So if they include the delta , it will be an additional forty millisecond.


So if they include the delta , it will be an additional forty millisecond.

Yeah. I  I don't remember the  i th They were not using the HTK delta ?


Yeah. I  I don't remember the  i th They were not using the HTK delta ?

No , they 're using a nine - point window , which is like a four on either side ,


No , they 're using a nine - point window , which is like a four on either side ,


Where does the comprish compression in decoding delay comes from ?


Where does the comprish compression in decoding delay comes from ?

That 's the way the  the  the frames are packed , like you have to wait for one more frame to pack. Because it 's  the CRC is computed for two frames always.


Where does the comprish compression in decoding delay comes from ?

That 's the way the  the  the frames are packed , like you have to wait for one more frame to pack. Because it 's  the CRC is computed for two frames always.

Well , that  the they would need that forty milliseconds also.


That 's the way the  the  the frames are packed , like you have to wait for one more frame to pack. Because it 's  the CRC is computed for two frames always.

Well , that  the they would need that forty milliseconds also.


That 's the way the  the  the frames are packed , like you have to wait for one more frame to pack. Because it 's  the CRC is computed for two frames always.

Well , that  the they would need that forty milliseconds also.

No. They actually changed the compression scheme altogether.


Well , that  the they would need that forty milliseconds also.

No. They actually changed the compression scheme altogether.


No. They actually changed the compression scheme altogether.


No. They actually changed the compression scheme altogether.

So they have their own compression and decoding scheme and they  I don't know what they have.


So they have their own compression and decoding scheme and they  I don't know what they have.


So they have their own compression and decoding scheme and they  I don't know what they have.

But they have coded zero delay for that. Because they ch I know they changed it , their compression. They have their own CRC , their  their own  error correction mechanism.


But they have coded zero delay for that. Because they ch I know they changed it , their compression. They have their own CRC , their  their own  error correction mechanism.

So they don't have to wait more than one more frame to know whether the current frame is in error.


So they don't have to wait more than one more frame to know whether the current frame is in error.

So they changed the whole thing so that there 's no delay for that compression and  part also.


So they changed the whole thing so that there 's no delay for that compression and  part also.


So they changed the whole thing so that there 's no delay for that compression and  part also.

Even you have reported actually zero delay for the  compression. I thought maybe you also have some different


Even you have reported actually zero delay for the  compression. I thought maybe you also have some different

Mmm. Mmm. No , I think I  I used this scheme as it was before.


Even you have reported actually zero delay for the  compression. I thought maybe you also have some different

Mmm. Mmm. No , I think I  I used this scheme as it was before.

OK , we 've got twenty minutes so we should  probably try to move along. Uh , did you wanna go next , Stephane ?


Mmm. Mmm. No , I think I  I used this scheme as it was before.

OK , we 've got twenty minutes so we should  probably try to move along. Uh , did you wanna go next , Stephane ?


OK , we 've got twenty minutes so we should  probably try to move along. Uh , did you wanna go next , Stephane ?


So you have w w one sheet ? This one is  you don't need it , alright.


So you have w w one sheet ? This one is  you don't need it , alright.

So you have to take the whole  the five. There should be five sheets.


So you have to take the whole  the five. There should be five sheets.

I have four now because I left one with Dave because I thought I was dropping one off and passing the others on. So , no , we 're not. OK.


I have four now because I left one with Dave because I thought I was dropping one off and passing the others on. So , no , we 're not. OK.


OK , maybe there 's not enough for everybody.


So , yeah , there are two figures showing actually the , mmm , um , performance of the current VAD. So it 's a n neural network based on PLP parameters , uh , which estimate silence probabilities , and then I just put a median filtering on this to smooth the probabilities , right ? Um  I didn't use the  the scheme that 's currently in the proposal because  I don't want to  In the proposal  Well , in  in the system we want to add like speech frame before every word and a little bit of  of , uh , s a couple of frames after also. Uh , but to estimate the performance of the VAD , we don't want to do that , because it would artificially increase the um  the false alarm rate of speech detection. Right ? Um , so , there is u normally a figure for the Finnish and one for Italian. And maybe someone has two for the Italian because I 'm missing one figure here.


So , yeah , there are two figures showing actually the , mmm , um , performance of the current VAD. So it 's a n neural network based on PLP parameters , uh , which estimate silence probabilities , and then I just put a median filtering on this to smooth the probabilities , right ? Um  I didn't use the  the scheme that 's currently in the proposal because  I don't want to  In the proposal  Well , in  in the system we want to add like speech frame before every word and a little bit of  of , uh , s a couple of frames after also. Uh , but to estimate the performance of the VAD , we don't want to do that , because it would artificially increase the um  the false alarm rate of speech detection. Right ? Um , so , there is u normally a figure for the Finnish and one for Italian. And maybe someone has two for the Italian because I 'm missing one figure here.

Well  Well , whatever. Uh  Yeah , so one surprising thing that we can notice first is that apparently the speech miss rate is uh , higher than the false alarm rate. So. It means


So , yeah , there are two figures showing actually the , mmm , um , performance of the current VAD. So it 's a n neural network based on PLP parameters , uh , which estimate silence probabilities , and then I just put a median filtering on this to smooth the probabilities , right ? Um  I didn't use the  the scheme that 's currently in the proposal because  I don't want to  In the proposal  Well , in  in the system we want to add like speech frame before every word and a little bit of  of , uh , s a couple of frames after also. Uh , but to estimate the performance of the VAD , we don't want to do that , because it would artificially increase the um  the false alarm rate of speech detection. Right ? Um , so , there is u normally a figure for the Finnish and one for Italian. And maybe someone has two for the Italian because I 'm missing one figure here.

Well  Well , whatever. Uh  Yeah , so one surprising thing that we can notice first is that apparently the speech miss rate is uh , higher than the false alarm rate. So. It means

So  so what is the lower curve and the upper curve ?


Well  Well , whatever. Uh  Yeah , so one surprising thing that we can notice first is that apparently the speech miss rate is uh , higher than the false alarm rate. So. It means

So  so what is the lower curve and the upper curve ?

Mm - hmm. Yeah , there are two curves. One curve 's for the close - talking microphone , which is the lower curve.


So  so what is the lower curve and the upper curve ?

Mm - hmm. Yeah , there are two curves. One curve 's for the close - talking microphone , which is the lower curve.

And the other one is for the distant microphone


Mm - hmm. Yeah , there are two curves. One curve 's for the close - talking microphone , which is the lower curve.

And the other one is for the distant microphone


And the other one is for the distant microphone

which has more noise so , it 's logical that  it performs worse. So as I was saying , the miss rate is quite important uh , which means that we tend to label speech as  as a silence. And , uh , I didn't analyze further yet , but  I think it 's  it may be due to the fricative sounds which may be  in noisy condition maybe label  labelled as silence. And it may also be due to the alignment because  well , the reference alignment. Because right now I just use an alignment obtained from  from a system trained on channel zero. And I checked it a little bit but there might be alignment errors. Um , yeah , e like the fact that   the  the models tend to align their first state on silence and their last state o on silence also. So the reference  reference alignment would label as speech some silence frame before speech and after speech. This is something that we already noticed before when  mmm , So this cus this could also explain , uh , the high miss rate maybe. Uh


And the other one is for the distant microphone

which has more noise so , it 's logical that  it performs worse. So as I was saying , the miss rate is quite important uh , which means that we tend to label speech as  as a silence. And , uh , I didn't analyze further yet , but  I think it 's  it may be due to the fricative sounds which may be  in noisy condition maybe label  labelled as silence. And it may also be due to the alignment because  well , the reference alignment. Because right now I just use an alignment obtained from  from a system trained on channel zero. And I checked it a little bit but there might be alignment errors. Um , yeah , e like the fact that   the  the models tend to align their first state on silence and their last state o on silence also. So the reference  reference alignment would label as speech some silence frame before speech and after speech. This is something that we already noticed before when  mmm , So this cus this could also explain , uh , the high miss rate maybe. Uh

And  and this  this curves are the average over the whole database , so.


which has more noise so , it 's logical that  it performs worse. So as I was saying , the miss rate is quite important uh , which means that we tend to label speech as  as a silence. And , uh , I didn't analyze further yet , but  I think it 's  it may be due to the fricative sounds which may be  in noisy condition maybe label  labelled as silence. And it may also be due to the alignment because  well , the reference alignment. Because right now I just use an alignment obtained from  from a system trained on channel zero. And I checked it a little bit but there might be alignment errors. Um , yeah , e like the fact that   the  the models tend to align their first state on silence and their last state o on silence also. So the reference  reference alignment would label as speech some silence frame before speech and after speech. This is something that we already noticed before when  mmm , So this cus this could also explain , uh , the high miss rate maybe. Uh

And  and this  this curves are the average over the whole database , so.


And  and this  this curves are the average over the whole database , so.

Um  Yeah , and the different points of the curves are for five uh , thresholds on the probability  uh from point three to point seven.


Um  Yeah , and the different points of the curves are for five uh , thresholds on the probability  uh from point three to point seven.


Um  Yeah , and the different points of the curves are for five uh , thresholds on the probability  uh from point three to point seven.

OK. S OK  so d the detection threshold is very


OK. S OK  so d the detection threshold is very


OK. S OK  so d the detection threshold is very

The VAD ? Yeah. There first , a threshold on the probability  @ @  That puts all the values to zero or one.


The VAD ? Yeah. There first , a threshold on the probability  @ @  That puts all the values to zero or one.


The VAD ? Yeah. There first , a threshold on the probability  @ @  That puts all the values to zero or one.

Yeah , so the median filtering is fixed. You just change the threshold ?


Yeah , so the median filtering is fixed. You just change the threshold ?


Yeah , so the median filtering is fixed. You just change the threshold ?

yeah. Mm - hmm. So , going from channel zero to channel one , uh , almost double the error rate. Um , Yeah. Well , so it 's a reference performance that we can  you know , if we want to  to work on the VAD ,  we can work on this basis


yeah. Mm - hmm. So , going from channel zero to channel one , uh , almost double the error rate. Um , Yeah. Well , so it 's a reference performance that we can  you know , if we want to  to work on the VAD ,  we can work on this basis


So three  three hundred and fifty inputs ,


So three  three hundred and fifty inputs ,

uh , six thousand hidden nodes and two outputs. t t


uh , six thousand hidden nodes and two outputs. t t


Yeah. Uh , ppp. I don't know , you have questions about that , or suggestions ?


Yeah. Uh , ppp. I don't know , you have questions about that , or suggestions ?

It seems  the performance seems worse in Finnish , which


It seems  the performance seems worse in Finnish , which


I mean , the MLP 's not trained on Finnish.


I mean , the MLP 's not trained on Finnish.

Oh  oh. Sorry. Uh , it 's Italian TI - digits.


Oh  oh. Sorry. Uh , it 's Italian TI - digits.


And also there are like funny noises on Finnish more than on Italian. I mean , like music


And also there are like funny noises on Finnish more than on Italian. I mean , like music

and  um  So , yeah , we were looking at this. But for most of the noises , noises are  um , I don't know if we want to talk about that. But , well , the  the " Car " noises are below like five hundred hertz. And we were looking at the " Music " utterances and in this case the noise is more about two thousand hertz.


and  um  So , yeah , we were looking at this. But for most of the noises , noises are  um , I don't know if we want to talk about that. But , well , the  the " Car " noises are below like five hundred hertz. And we were looking at the " Music " utterances and in this case the noise is more about two thousand hertz.


and  um  So , yeah , we were looking at this. But for most of the noises , noises are  um , I don't know if we want to talk about that. But , well , the  the " Car " noises are below like five hundred hertz. And we were looking at the " Music " utterances and in this case the noise is more about two thousand hertz.

Well , the music energy 's very low apparently. Uh , uh , from zero to two  two thousand hertz. So maybe just looking at this frequency range for  from five hundred to two thousand would improve somewhat the VAD


Well , the music energy 's very low apparently. Uh , uh , from zero to two  two thousand hertz. So maybe just looking at this frequency range for  from five hundred to two thousand would improve somewhat the VAD


So there are like some  some s some parameters you wanted to use or something ?


So there are like some  some s some parameters you wanted to use or something ?

Mm - hmm. Uh , the next , um  Oh , it 's there.


Mm - hmm. Uh , the next , um  Oh , it 's there.

So is the  is the  is the training  is the training based on these labels files which you take as reference here ?


Mm - hmm. Uh , the next , um  Oh , it 's there.

So is the  is the  is the training  is the training based on these labels files which you take as reference here ?

Wh - when you train the neural net y y you


So is the  is the  is the training  is the training based on these labels files which you take as reference here ?

Wh - when you train the neural net y y you

No. It 's not. It 's  it was trained on some alignment obtained um , uh  For the Italian data , I think we trained the neural network on  with embedded training. So re - estimation of the alignment using the neural network , I guess. That 's right ?


Wh - when you train the neural net y y you

No. It 's not. It 's  it was trained on some alignment obtained um , uh  For the Italian data , I think we trained the neural network on  with embedded training. So re - estimation of the alignment using the neural network , I guess. That 's right ?

Yeah. We actually trained , uh , the  on the Italian training part.


No. It 's not. It 's  it was trained on some alignment obtained um , uh  For the Italian data , I think we trained the neural network on  with embedded training. So re - estimation of the alignment using the neural network , I guess. That 's right ?

Yeah. We actually trained , uh , the  on the Italian training part.


Yeah. We actually trained , uh , the  on the Italian training part.

So it was a f f a phonetic classification system for the Italian Aurora data.


So it was a f f a phonetic classification system for the Italian Aurora data.


So it was a f f a phonetic classification system for the Italian Aurora data.

For the Aurora data that it was trained on , it was different. Like , for TI - digits you used a  a previous system that you had , I guess.


So it was a f f a phonetic classification system for the Italian Aurora data.

For the Aurora data that it was trained on , it was different. Like , for TI - digits you used a  a previous system that you had , I guess.

What   No it  Yeah , yeah. That 's true.


For the Aurora data that it was trained on , it was different. Like , for TI - digits you used a  a previous system that you had , I guess.

What   No it  Yeah , yeah. That 's true.

So the alignments from the different database that are used for training came from different system.


What   No it  Yeah , yeah. That 's true.

So the alignments from the different database that are used for training came from different system.

Then we put them tog together. Well , you put them together and trained the VAD on them.


So the alignments from the different database that are used for training came from different system.

Then we put them tog together. Well , you put them together and trained the VAD on them.


Then we put them tog together. Well , you put them together and trained the VAD on them.


Uh , But did you use channel  did you align channel one also ? Or


Uh , But did you use channel  did you align channel one also ? Or

I just took their entire Italian training part.


Uh , But did you use channel  did you align channel one also ? Or

I just took their entire Italian training part.

So it was both channel zero plus channel one.


I just took their entire Italian training part.

So it was both channel zero plus channel one.

So di Yeah. So the alignments might be wrong then on channel one , right ?


So it was both channel zero plus channel one.

So di Yeah. So the alignments might be wrong then on channel one , right ?


So di Yeah. So the alignments might be wrong then on channel one , right ?


at least want to retrain on these alignments , which should be better because they come from close - talking microphone.


at least want to retrain on these alignments , which should be better because they come from close - talking microphone.

Yeah , the  that was my idea. I mean , if  if it ha if it is not the same labeling which is taking the spaces.


Yeah , the  that was my idea. I mean , if  if it ha if it is not the same labeling which is taking the spaces.


so the VAD was trained on maybe different set of labels for channel zero and channel one


was the alignments were w were different for  s certainly different because they were independently trained.


was the alignments were w were different for  s certainly different because they were independently trained.

We didn't copy the channel zero alignments to channel one.


We didn't copy the channel zero alignments to channel one.


But for the new alignments what you generated , you just copied the channel zero to channel one , right ? Yeah.


But for the new alignments what you generated , you just copied the channel zero to channel one , right ? Yeah.

Right. Yeah. Um. And eh , hhh actually when we look at  at the VAD ,  for some utterances it 's almost perfect , I mean , it just dropped one frame , the first frame of speech or  So there are some utterances where it 's almost one hundred percent VAD performance.


But for the new alignments what you generated , you just copied the channel zero to channel one , right ? Yeah.

Right. Yeah. Um. And eh , hhh actually when we look at  at the VAD ,  for some utterances it 's almost perfect , I mean , it just dropped one frame , the first frame of speech or  So there are some utterances where it 's almost one hundred percent VAD performance.

Uh , but  Yeah. Mmm  Yep. So the next thing is um , I have the spreadsheet for three different system. But for this you only have to look right now on the SpeechDat - Car performance uh , because I didn't test  so  I didn't test the spectral subtraction on TI - digits yet. Uh , so you have three she sheets. One is the um proposal - one system. Actually , it 's not exe exactly proposal - one. It 's the system that Sunil just described. Um , but with uh , Wiener filtering from um , France Telecom included. Um , so this gives like fifty - seven point seven percent , uh , s uh , error rate reduction on the SpeechDat - Car data. Mmm , and then I have two sheets where it 's for a system where  uh , so it 's again the same system. But in this case we have spectral subtraction with a maximum overestimation factor of two point five. Uh , there is smoothing of the gain trajectory with some kind of uh , low - pass filter , which has forty milliseconds latency. And then , after subtraction um , I add a constant to the energies and I have two cases d where  The first case is where the constant is twenty - five DB below the mean speech energy and the other is thirty DB below. Um , and for these s two system we have like fifty - five point , uh , five - percent improvement , and fifty - eight point one. So again , it 's around fifty - six , fifty - seven. Uh


Right. Yeah. Um. And eh , hhh actually when we look at  at the VAD ,  for some utterances it 's almost perfect , I mean , it just dropped one frame , the first frame of speech or  So there are some utterances where it 's almost one hundred percent VAD performance.

Uh , but  Yeah. Mmm  Yep. So the next thing is um , I have the spreadsheet for three different system. But for this you only have to look right now on the SpeechDat - Car performance uh , because I didn't test  so  I didn't test the spectral subtraction on TI - digits yet. Uh , so you have three she sheets. One is the um proposal - one system. Actually , it 's not exe exactly proposal - one. It 's the system that Sunil just described. Um , but with uh , Wiener filtering from um , France Telecom included. Um , so this gives like fifty - seven point seven percent , uh , s uh , error rate reduction on the SpeechDat - Car data. Mmm , and then I have two sheets where it 's for a system where  uh , so it 's again the same system. But in this case we have spectral subtraction with a maximum overestimation factor of two point five. Uh , there is smoothing of the gain trajectory with some kind of uh , low - pass filter , which has forty milliseconds latency. And then , after subtraction um , I add a constant to the energies and I have two cases d where  The first case is where the constant is twenty - five DB below the mean speech energy and the other is thirty DB below. Um , and for these s two system we have like fifty - five point , uh , five - percent improvement , and fifty - eight point one. So again , it 's around fifty - six , fifty - seven. Uh

Cuz I notice the TI - digits number is exactly the same for these last two ?


Uh , but  Yeah. Mmm  Yep. So the next thing is um , I have the spreadsheet for three different system. But for this you only have to look right now on the SpeechDat - Car performance uh , because I didn't test  so  I didn't test the spectral subtraction on TI - digits yet. Uh , so you have three she sheets. One is the um proposal - one system. Actually , it 's not exe exactly proposal - one. It 's the system that Sunil just described. Um , but with uh , Wiener filtering from um , France Telecom included. Um , so this gives like fifty - seven point seven percent , uh , s uh , error rate reduction on the SpeechDat - Car data. Mmm , and then I have two sheets where it 's for a system where  uh , so it 's again the same system. But in this case we have spectral subtraction with a maximum overestimation factor of two point five. Uh , there is smoothing of the gain trajectory with some kind of uh , low - pass filter , which has forty milliseconds latency. And then , after subtraction um , I add a constant to the energies and I have two cases d where  The first case is where the constant is twenty - five DB below the mean speech energy and the other is thirty DB below. Um , and for these s two system we have like fifty - five point , uh , five - percent improvement , and fifty - eight point one. So again , it 's around fifty - six , fifty - seven. Uh

Cuz I notice the TI - digits number is exactly the same for these last two ?

Yeah , because I didn't  For the France Telecom uh , spectral subtraction included in the  our system , the TI - digits number are the right one , but not for the other system because I didn't test it yet  this system , including  with spectral subtraction on the TI - digits data. I just tested it on SpeechDat - Car.


Cuz I notice the TI - digits number is exactly the same for these last two ?

Yeah , because I didn't  For the France Telecom uh , spectral subtraction included in the  our system , the TI - digits number are the right one , but not for the other system because I didn't test it yet  this system , including  with spectral subtraction on the TI - digits data. I just tested it on SpeechDat - Car.

Mm - hmm. So  so  so these numbers are simply


Yeah , because I didn't  For the France Telecom uh , spectral subtraction included in the  our system , the TI - digits number are the right one , but not for the other system because I didn't test it yet  this system , including  with spectral subtraction on the TI - digits data. I just tested it on SpeechDat - Car.

Mm - hmm. So  so  so these numbers are simply


Mm - hmm. So  so  so these numbers are simply


So you  so you just should look at that fifty - eight perc point O nine percent and so on.


So this  So by  uh , by  by reducing the noise a  a decent threshold like minus thirty DB , it 's like  Uh , you are like r r reducing the floor of the noisy regions , right ?


I 'm sorry. So when you say minus twenty - five or minus thirty DB , with respect to what ?


I 'm sorry. So when you say minus twenty - five or minus thirty DB , with respect to what ?

To the average um , speech energy which is estimated on the world database.


I 'm sorry. So when you say minus twenty - five or minus thirty DB , with respect to what ?

To the average um , speech energy which is estimated on the world database.

OK , so basically you 're creating a signal - to - noise ratio of twenty - five or thirty DB ?


To the average um , speech energy which is estimated on the world database.

OK , so basically you 're creating a signal - to - noise ratio of twenty - five or thirty DB ?


OK , so basically you 're creating a signal - to - noise ratio of twenty - five or thirty DB ?


i When  when you have this ,  after you subtracted it , I mean , then you get something w w with this , uh , where you set the values to zero and then you simply add an additive constant again.


i When  when you have this ,  after you subtracted it , I mean , then you get something w w with this , uh , where you set the values to zero and then you simply add an additive constant again.

So you shift it somehow. This  this whole curve is shifted again.


i When  when you have this ,  after you subtracted it , I mean , then you get something w w with this , uh , where you set the values to zero and then you simply add an additive constant again.

So you shift it somehow. This  this whole curve is shifted again.

But did you do that before the thresholding to zero ,


So you shift it somehow. This  this whole curve is shifted again.

But did you do that before the thresholding to zero ,


But did you do that before the thresholding to zero ,


Yeah , because then the  then you would have less of that phenomenon.


But still , when you do this and you take the log after that , it  it reduce the  the variance.


But still , when you do this and you take the log after that , it  it reduce the  the variance.

Yeah , that will reduce the variance. That 'll help. But maybe if you does  do it before you get less of these funny - looking things he 's drawing.


Yeah , that will reduce the variance. That 'll help. But maybe if you does  do it before you get less of these funny - looking things he 's drawing.


Yeah , that will reduce the variance. That 'll help. But maybe if you does  do it before you get less of these funny - looking things he 's drawing.

So before it 's like adding this , col to the  to the  o exi original


So before it 's like adding this , col to the  to the  o exi original


So before it 's like adding this , col to the  to the  o exi original

Right at the point where you 've done the subtraction.


Right at the point where you 've done the subtraction.


Right at the point where you 've done the subtraction.

Um , essentially you 're adding a constant into everything.


Um , essentially you 're adding a constant into everything.

But the way Stephane did it , it is exactly the way I have implemented in the phone , so.


Um , essentially you 're adding a constant into everything.

But the way Stephane did it , it is exactly the way I have implemented in the phone , so.

Oh , yeah , better do it different , then. Yeah.


But the way Stephane did it , it is exactly the way I have implemented in the phone , so.

Oh , yeah , better do it different , then. Yeah.


But the way Stephane did it , it is exactly the way I have implemented in the phone , so.

Oh , yeah , better do it different , then. Yeah.

Just you  you just ta you just set it for a particular signal - to - noise ratio that you want ?


Oh , yeah , better do it different , then. Yeah.

Just you  you just ta you just set it for a particular signal - to - noise ratio that you want ?


Just you  you just ta you just set it for a particular signal - to - noise ratio that you want ?

Yeah I  I made s similar investigations like Stephane did here , just uh , adding this constant and  and looking how dependent is it on the value of the constant


Yeah I  I made s similar investigations like Stephane did here , just uh , adding this constant and  and looking how dependent is it on the value of the constant


Yeah I  I made s similar investigations like Stephane did here , just uh , adding this constant and  and looking how dependent is it on the value of the constant

and then , must choose them somehow  to give on average the best results for a certain range of the signal - to - noise ratios.


and then , must choose them somehow  to give on average the best results for a certain range of the signal - to - noise ratios.


Oh , it 's clear. I should have gi given other results. Also it 's clear when you don't add noise , it 's much worse. Like , around five percent worse I guess.


Oh , it 's clear. I should have gi given other results. Also it 's clear when you don't add noise , it 's much worse. Like , around five percent worse I guess.

And if you add too much noise it get worse also. And it seems that  right now this  this is c a constant that does not depend on   on anything that you can learn from the utterance. It 's just a constant noise addition. Um. And I  I think w w


Oh , it 's clear. I should have gi given other results. Also it 's clear when you don't add noise , it 's much worse. Like , around five percent worse I guess.

And if you add too much noise it get worse also. And it seems that  right now this  this is c a constant that does not depend on   on anything that you can learn from the utterance. It 's just a constant noise addition. Um. And I  I think w w

I  I 'm sorry. Then  then I 'm confused.


And if you add too much noise it get worse also. And it seems that  right now this  this is c a constant that does not depend on   on anything that you can learn from the utterance. It 's just a constant noise addition. Um. And I  I think w w

I  I 'm sorry. Then  then I 'm confused.


And if you add too much noise it get worse also. And it seems that  right now this  this is c a constant that does not depend on   on anything that you can learn from the utterance. It 's just a constant noise addition. Um. And I  I think w w

I  I 'm sorry. Then  then I 'm confused.

I thought  you 're saying it doesn't depend on the utterance but I thought you were adding an amount that was twenty - five DB down from the signal energy.


I  I 'm sorry. Then  then I 'm confused.

I thought  you 're saying it doesn't depend on the utterance but I thought you were adding an amount that was twenty - five DB down from the signal energy.

Yeah , so the way I did that ,  i I just measured the average speech energy of the  all the Italian data.


I thought  you 're saying it doesn't depend on the utterance but I thought you were adding an amount that was twenty - five DB down from the signal energy.

Yeah , so the way I did that ,  i I just measured the average speech energy of the  all the Italian data.


I thought  you 're saying it doesn't depend on the utterance but I thought you were adding an amount that was twenty - five DB down from the signal energy.

Yeah , so the way I did that ,  i I just measured the average speech energy of the  all the Italian data.

And then  I  I have  I used this as mean speech energy. Mm - hmm.


Yeah , so the way I did that ,  i I just measured the average speech energy of the  all the Italian data.

And then  I  I have  I used this as mean speech energy. Mm - hmm.

Oh , it 's just a constant amount over all.


And then  I  I have  I used this as mean speech energy. Mm - hmm.

Oh , it 's just a constant amount over all.


Oh , it 's just a constant amount over all.

wha what I observed is that for Italian and Spanish ,  when you go to thirty and twenty - five DB ,  uh it  it 's good.


wha what I observed is that for Italian and Spanish ,  when you go to thirty and twenty - five DB ,  uh it  it 's good.


wha what I observed is that for Italian and Spanish ,  when you go to thirty and twenty - five DB ,  uh it  it 's good.

It stays  In this range , it 's , uh , the p u well , the performance of the  this algorithm is quite good. But for Finnish ,  you have a degradation already when you go from thirty - five to thirty and then from thirty to twenty - five. And  I have the feeling that maybe it 's because just Finnish has a mean energy that 's lower than  than the other databases. And due to this the thresholds should be


It stays  In this range , it 's , uh , the p u well , the performance of the  this algorithm is quite good. But for Finnish ,  you have a degradation already when you go from thirty - five to thirty and then from thirty to twenty - five. And  I have the feeling that maybe it 's because just Finnish has a mean energy that 's lower than  than the other databases. And due to this the thresholds should be

the  the a the noise addition should be lower


It stays  In this range , it 's , uh , the p u well , the performance of the  this algorithm is quite good. But for Finnish ,  you have a degradation already when you go from thirty - five to thirty and then from thirty to twenty - five. And  I have the feeling that maybe it 's because just Finnish has a mean energy that 's lower than  than the other databases. And due to this the thresholds should be

the  the a the noise addition should be lower

But in  I mean , in the real thing you 're not gonna be able to measure what people are doing over half an hour or an hour , or anything , right ?


the  the a the noise addition should be lower

But in  I mean , in the real thing you 're not gonna be able to measure what people are doing over half an hour or an hour , or anything , right ?


the  the a the noise addition should be lower

But in  I mean , in the real thing you 're not gonna be able to measure what people are doing over half an hour or an hour , or anything , right ?

So you have to come up with this number from something else.


But in  I mean , in the real thing you 're not gonna be able to measure what people are doing over half an hour or an hour , or anything , right ?

So you have to come up with this number from something else.


So you have to come up with this number from something else.

Uh , but you are not doing it now language dependent ? Or  ?


So you have to come up with this number from something else.

Uh , but you are not doing it now language dependent ? Or  ?

It 's not. It 's just something that 's fixed.


Uh , but you are not doing it now language dependent ? Or  ?

It 's not. It 's just something that 's fixed.


It 's not. It 's just something that 's fixed.


But what he is doing language dependent is measuring what that number i reference is that he comes down twenty - five down from.


But what he is doing language dependent is measuring what that number i reference is that he comes down twenty - five down from.

Because I did it  I started working on Italian. I obtained this average energy


Because I did it  I started working on Italian. I obtained this average energy


Um , yeah , so the next thing is to use this as  as maybe initialization


But   And I expect improvement at least in Finnish because eh  the way


But   And I expect improvement at least in Finnish because eh  the way

Well , um , for Italian and Spanish it 's  th this value works good but not necessarily for Finnish. Mmm. But unfortunately there is , like , this forty millisecond latency and , um  Yeah , so I would try to somewhat reduce this @ @. I already know that if I completely remove this latency , so.  um ,  it  um there is a three percent hit on Italian.


Well , um , for Italian and Spanish it 's  th this value works good but not necessarily for Finnish. Mmm. But unfortunately there is , like , this forty millisecond latency and , um  Yeah , so I would try to somewhat reduce this @ @. I already know that if I completely remove this latency , so.  um ,  it  um there is a three percent hit on Italian.


Yeah. Your  your smoothing was @ @  uh , over this s so to say , the  the factor of the Wiener. And then it 's , uh  What was it ? This


Yeah. Your  your smoothing was @ @  uh , over this s so to say , the  the factor of the Wiener. And then it 's , uh  What was it ? This

this smoothing , it was over the subtraction factor , so to say.


Yeah. Your  your smoothing was @ @  uh , over this s so to say , the  the factor of the Wiener. And then it 's , uh  What was it ? This

this smoothing , it was over the subtraction factor , so to say.

It 's a smoothing over the  the gain of the subtraction algorithm.


this smoothing , it was over the subtraction factor , so to say.

It 's a smoothing over the  the gain of the subtraction algorithm.

Was this done  Mm - hmm. And  and you are looking into the future , into the past.


It 's a smoothing over the  the gain of the subtraction algorithm.

Was this done  Mm - hmm. And  and you are looking into the future , into the past.


Was this done  Mm - hmm. And  and you are looking into the future , into the past.


And did  did you try simply to smooth um to smooth the  the  t to  to smooth stronger the  the envelope ?


Because I mean , it should have a similar effect if you


Because I mean , it should have a similar effect if you

I mean , you  you have now several stages of smoothing , so to say. You start up. As far as I remember you  you smooth somehow the envelope , you smooth somehow the noise estimate ,


I mean , you  you have now several stages of smoothing , so to say. You start up. As far as I remember you  you smooth somehow the envelope , you smooth somehow the noise estimate ,

and   and later on you smooth also this subtraction factor.


I mean , you  you have now several stages of smoothing , so to say. You start up. As far as I remember you  you smooth somehow the envelope , you smooth somehow the noise estimate ,

and   and later on you smooth also this subtraction factor.

Uh , no , it 's  it 's just the gain that 's smoothed actually


and   and later on you smooth also this subtraction factor.

Uh , no , it 's  it 's just the gain that 's smoothed actually

Uh , actually I d I do all the smoothing.


Uh , no , it 's  it 's just the gain that 's smoothed actually

Uh , actually I d I do all the smoothing.


Uh , actually I d I do all the smoothing.


Yeah. No , in this case it 's just the gain.


But the way it 's done is that um , for low gain , there is this non nonlinear smoothing actually. For low gains um , I use the smoothed sm uh , smoothed version but  for high gain @ @  it 's  I don't smooth.


But the way it 's done is that um , for low gain , there is this non nonlinear smoothing actually. For low gains um , I use the smoothed sm uh , smoothed version but  for high gain @ @  it 's  I don't smooth.

Uh. Mm - hmm. I just , uh  it  Experience shows you , if  if you do the   The best is to do the smoo smoothing as early as possible.


But the way it 's done is that um , for low gain , there is this non nonlinear smoothing actually. For low gains um , I use the smoothed sm uh , smoothed version but  for high gain @ @  it 's  I don't smooth.

Uh. Mm - hmm. I just , uh  it  Experience shows you , if  if you do the   The best is to do the smoo smoothing as early as possible.

So w when you start up. I mean , you start up with the  with the  somehow with the noisy envelope.


Uh. Mm - hmm. I just , uh  it  Experience shows you , if  if you do the   The best is to do the smoo smoothing as early as possible.

So w when you start up. I mean , you start up with the  with the  somehow with the noisy envelope.


So w when you start up. I mean , you start up with the  with the  somehow with the noisy envelope.


So w when you start up. I mean , you start up with the  with the  somehow with the noisy envelope.

Mm - hmm. Uh , yeah , I could try this. Um.


Mm - hmm. Uh , yeah , I could try this. Um.


Mm - hmm. Uh , yeah , I could try this. Um.

So , before estimating the SNR , @ @ smooth the envelope.


So , before estimating the SNR , @ @ smooth the envelope.

Mm - hmm. But  Yeah. Then I  I would need to find a way to like smooth less also when there is high energy. Cuz I noticed that it  it helps a little bit to s like smooth more during low energy portions and less during speech ,


Mm - hmm. But  Yeah. Then I  I would need to find a way to like smooth less also when there is high energy. Cuz I noticed that it  it helps a little bit to s like smooth more during low energy portions and less during speech ,

because if you smooth then y you kind of distort the speech.


because if you smooth then y you kind of distort the speech.


Yeah , I think when w you  you could do it in this way that you say , if you  if I 'm  you have somehow a noise estimate ,


Yeah , I think when w you  you could do it in this way that you say , if you  if I 'm  you have somehow a noise estimate ,

and , if you say I 'm  I 'm  with my envelope I 'm close to this noise estimate ,


and , if you say I 'm  I 'm  with my envelope I 'm close to this noise estimate ,

then you have a bad signal - to - noise ratio and then you  you would like to have a stronger smoothing.


then you have a bad signal - to - noise ratio and then you  you would like to have a stronger smoothing.

So you could  you could base it on your estimation of the signal - to - noise ratio on your actual


So you could  you could base it on your estimation of the signal - to - noise ratio on your actual

Yeah , or some silence probability from the VAD if you have


So you could  you could base it on your estimation of the signal - to - noise ratio on your actual

Yeah , or some silence probability from the VAD if you have

Um , yeah , but I don't trust  the current VAD. So.


Yeah , or some silence probability from the VAD if you have

Um , yeah , but I don't trust  the current VAD. So.

Yeah , uh , so not  not right now maybe.


Um , yeah , but I don't trust  the current VAD. So.

Yeah , uh , so not  not right now maybe.


Yeah , uh , so not  not right now maybe.


s So to summarize the performance of these , SpeechDat - Car results is similar than  than yours so to say.


s So to summarize the performance of these , SpeechDat - Car results is similar than  than yours so to say.

Yeah , so the fifty - eight is like the be some fifty - six point


s So to summarize the performance of these , SpeechDat - Car results is similar than  than yours so to say.

Yeah , so the fifty - eight is like the be some fifty - six point

Y you have  you have fifty - six point four


Yeah , so the fifty - eight is like the be some fifty - six point

Y you have  you have fifty - six point four


Y you have  you have fifty - six point four

and  and   and dependent on this additive constant , it is s better or  or worse.


and  and   and dependent on this additive constant , it is s better or  or worse.


And ,  yeah , i i i the condition where it 's better than your approach , it 's  it  just because maybe it 's better on well matched and that the weight on well matched is  is bigger ,


if you don't weigh differently the different condition , you can see that your  well , the win the two - stage Wiener filtering is maybe better or


if you don't weigh differently the different condition , you can see that your  well , the win the two - stage Wiener filtering is maybe better or

It 's better for high mismatch , right ?


It 's better for high mismatch , right ?

Mm - hmm. But a little bit worse for well matched.


It 's better for high mismatch , right ?

Mm - hmm. But a little bit worse for well matched.

So over all it gets , yeah , worse for the well matched condition , so y


Mm - hmm. But a little bit worse for well matched.

So over all it gets , yeah , worse for the well matched condition , so y


So over all it gets , yeah , worse for the well matched condition , so y

Uh , that 's  that 's the best thing , is like the French Telecom system is optimized for the well matched condition. They c


Uh , that 's  that 's the best thing , is like the French Telecom system is optimized for the well matched condition. They c


Uh , that 's  that 's the best thing , is like the French Telecom system is optimized for the well matched condition. They c

Yeah. So they know that the weighting is good for the well matched , and so there 's  everywhere the well matched 's s s performance is very good for the French Telecom.


Yeah. So they know that the weighting is good for the well matched , and so there 's  everywhere the well matched 's s s performance is very good for the French Telecom.


T we are  we may also have to do something similar @ @.


T we are  we may also have to do something similar @ @.

Well , our tradition here has always been to focus on the mismatched.


Well , our tradition here has always been to focus on the mismatched.


Before I started working on this Aurora.


Well , I only say that the  this is , a summary of the  of all the VTS experiments and say that the result in the last  um , for Italian  the last experiment for Italian ,  are bad. I make a mistake when I write. Up at D I copy  one of the bad result.


Well , I only say that the  this is , a summary of the  of all the VTS experiments and say that the result in the last  um , for Italian  the last experiment for Italian ,  are bad. I make a mistake when I write. Up at D I copy  one of the bad result.

And  There.  You know , this. Um , well. If we put everything , we improve a lot u the spectral use of the VTS but the final result  are not still mmm , good  like the Wiener filter for example. I don't know. Maybe it 's  @ @  it 's possible to  to have the same result.


And  There.  You know , this. Um , well. If we put everything , we improve a lot u the spectral use of the VTS but the final result  are not still mmm , good  like the Wiener filter for example. I don't know. Maybe it 's  @ @  it 's possible to  to have the same result.

I don't know exactly. Mmm. Because I have ,  mmm ,  worse result in medium mismatch and high mismatch.


And  There.  You know , this. Um , well. If we put everything , we improve a lot u the spectral use of the VTS but the final result  are not still mmm , good  like the Wiener filter for example. I don't know. Maybe it 's  @ @  it 's possible to  to have the same result.

I don't know exactly. Mmm. Because I have ,  mmm ,  worse result in medium mismatch and high mismatch.

You s you have a better r Yeah. You have some results that are good for the high mismatch.


I don't know exactly. Mmm. Because I have ,  mmm ,  worse result in medium mismatch and high mismatch.

You s you have a better r Yeah. You have some results that are good for the high mismatch.

And  Yeah. I someti are more or less similar but  but are worse. And still I don't have the result for TI - digits. The program is training. Maybe for this weekend I will have result TI - digits and I can complete that s like this. Well.


You s you have a better r Yeah. You have some results that are good for the high mismatch.

And  Yeah. I someti are more or less similar but  but are worse. And still I don't have the result for TI - digits. The program is training. Maybe for this weekend I will have result TI - digits and I can complete that s like this. Well.

One thing that I  note are not here in this result  but are speak  are spoken before with Sunil I  I improve my result using clean LDA filter.


And  Yeah. I someti are more or less similar but  but are worse. And still I don't have the result for TI - digits. The program is training. Maybe for this weekend I will have result TI - digits and I can complete that s like this. Well.

One thing that I  note are not here in this result  but are speak  are spoken before with Sunil I  I improve my result using clean LDA filter.


One thing that I  note are not here in this result  but are speak  are spoken before with Sunil I  I improve my result using clean LDA filter.


One thing that I  note are not here in this result  but are speak  are spoken before with Sunil I  I improve my result using clean LDA filter.

If I use ,  eh , the LDA filter that are training with the noisy speech ,  that hurts the res my results.


If I use ,  eh , the LDA filter that are training with the noisy speech ,  that hurts the res my results.

So what are these numbers here ? Are these with the clean or with the noisy ?


So what are these numbers here ? Are these with the clean or with the noisy ?

With the noise I have worse result , that if I doesn't use it.


With the noise I have worse result , that if I doesn't use it.


With the noise I have worse result , that if I doesn't use it.

But m that may be because  with this technique  we are using really  really clean speech. The speech  the  representation that go to the HTK is really clean speech because it 's from the dictionary , the code book and maybe from that. I don't know.


But m that may be because  with this technique  we are using really  really clean speech. The speech  the  representation that go to the HTK is really clean speech because it 's from the dictionary , the code book and maybe from that. I don't know.

Because I think that you  did some experiments using the two  the two LDA filter , clean and noi and noise ,


Because I think that you  did some experiments using the two  the two LDA filter , clean and noi and noise ,


Because I think that you  did some experiments using the two  the two LDA filter , clean and noi and noise ,

Um , yeah , I did that but it doesn't matter on SpeechDat - Car , but , it matters , uh , a lot on TI - digits.


Um , yeah , I did that but it doesn't matter on SpeechDat - Car , but , it matters , uh , a lot on TI - digits.


Um , yeah , I did that but it doesn't matter on SpeechDat - Car , but , it matters , uh , a lot on TI - digits.

Yeah , d uh , it 's much better when you  we used the clean derived LDA filter.


Yeah , d uh , it 's much better when you  we used the clean derived LDA filter.


Uh , but , yeah , Sunil in  in your result it 's


Uh , but , yeah , Sunil in  in your result it 's

I  I 'll try the cle No , I  I  my result is with the noisy  noisy LDA.


I  I 'll try the cle No , I  I  my result is with the noisy  noisy LDA.


It 's with the noisy. Yeah. It 's  it 's not the clean LDA.


It 's with the noisy. Yeah. It 's  it 's not the clean LDA.

It 's  In  in the front sheet , I have like  like the summary. Yeah.


It 's  In  in the front sheet , I have like  like the summary. Yeah.


It 's  In  in the front sheet , I have like  like the summary. Yeah.

Oh. This is  Your results are all with the clean LDA result ?


Oh. This is  Your results are all with the clean LDA result ?


But I observe my case it 's in , uh , uh , at least on SpeechDat - Car it doesn't matter but TI - digits it 's like two or three percent absolute , uh ,  better.


But I observe my case it 's in , uh , uh , at least on SpeechDat - Car it doesn't matter but TI - digits it 's like two or three percent absolute , uh ,  better.

On TI - digits this matters. Absolute. Uh


But I observe my case it 's in , uh , uh , at least on SpeechDat - Car it doesn't matter but TI - digits it 's like two or three percent absolute , uh ,  better.

On TI - digits this matters. Absolute. Uh

So you really might wanna try the clean I think.


On TI - digits this matters. Absolute. Uh

So you really might wanna try the clean I think.

Yeah , I  I  I will have to look at it. Yeah , that 's true.


So you really might wanna try the clean I think.

Yeah , I  I  I will have to look at it. Yeah , that 's true.

Yeah. Yeah , that could be sizeable right there.


Yeah , I  I  I will have to look at it. Yeah , that 's true.

Yeah. Yeah , that could be sizeable right there.


Yeah. Yeah , that could be sizeable right there.


Maybe you  you are leaving in  in about two weeks Carmen. No ?


Maybe you  you are leaving in  in about two weeks Carmen. No ?

Yeah. So I mean , if  if  if I would put it  put on the head of a project mana manager  I  I  I I would say , uh , um  I mean there is not so much time left now.


Yeah. So I mean , if  if  if I would put it  put on the head of a project mana manager  I  I  I I would say , uh , um  I mean there is not so much time left now.

I mean , if   um , what  what I would do is I  I  I would pick @ @  the best consolation , which you think , and  c create  create all the results for the whole database that you get to the final number as  as Sunil did it


I mean , if   um , what  what I would do is I  I  I would pick @ @  the best consolation , which you think , and  c create  create all the results for the whole database that you get to the final number as  as Sunil did it

and  um and maybe also to  to write somehow a document where you describe your approach , and what you have done.


Oh , maybe it 's the turning  turning off and turning on of the mike , right ?


OK. OK. So , um I guess we are  um  gonna do the digits at the end. Uh


Yeah , that 's the mike number there , uh  Uh , mike number five , and  channel  channel four.


Oh , I 'm channel  must be channel one. Channel one ?


Oh , I 'm channel  must be channel one. Channel one ?

Yes , OK. OK. So uh  I also copied uh the results that we all got in the mail I think from uh   from OGI and we 'll go  go through them also. So where are we on   on uh   our runs ?


Oh , I 'm channel  must be channel one. Channel one ?

Yes , OK. OK. So uh  I also copied uh the results that we all got in the mail I think from uh   from OGI and we 'll go  go through them also. So where are we on   on uh   our runs ?

Uh so.  uh  We  So  As I was already said , we  we mainly focused on uh four kind of features.


Yes , OK. OK. So uh  I also copied uh the results that we all got in the mail I think from uh   from OGI and we 'll go  go through them also. So where are we on   on uh   our runs ?

Uh so.  uh  We  So  As I was already said , we  we mainly focused on uh four kind of features.


Yes , OK. OK. So uh  I also copied uh the results that we all got in the mail I think from uh   from OGI and we 'll go  go through them also. So where are we on   on uh   our runs ?

Uh so.  uh  We  So  As I was already said , we  we mainly focused on uh four kind of features.

The PLP , the PLP with JRASTA , the MSG , and the MFCC from the baseline Aurora.


Uh so.  uh  We  So  As I was already said , we  we mainly focused on uh four kind of features.

The PLP , the PLP with JRASTA , the MSG , and the MFCC from the baseline Aurora.


The PLP , the PLP with JRASTA , the MSG , and the MFCC from the baseline Aurora.

Uh , and we focused for the  the test part on the English and the Italian. Um. We 've trained uh several neural networks on  so  on the TI - digits English  and on the Italian data and also on the broad uh  English uh French and uh Spanish databases. Mmm , so there 's our result tables here , for the tandem approach , and um , actually what we  we @ @ observed is that if the network is trained on the task data it works pretty well.


The PLP , the PLP with JRASTA , the MSG , and the MFCC from the baseline Aurora.

Uh , and we focused for the  the test part on the English and the Italian. Um. We 've trained uh several neural networks on  so  on the TI - digits English  and on the Italian data and also on the broad uh  English uh French and uh Spanish databases. Mmm , so there 's our result tables here , for the tandem approach , and um , actually what we  we @ @ observed is that if the network is trained on the task data it works pretty well.

OK. Our  our uh   There 's a   We 're pausing for a photo


Uh , and we focused for the  the test part on the English and the Italian. Um. We 've trained uh several neural networks on  so  on the TI - digits English  and on the Italian data and also on the broad uh  English uh French and uh Spanish databases. Mmm , so there 's our result tables here , for the tandem approach , and um , actually what we  we @ @ observed is that if the network is trained on the task data it works pretty well.

OK. Our  our uh   There 's a   We 're pausing for a photo


Uh , and we focused for the  the test part on the English and the Italian. Um. We 've trained uh several neural networks on  so  on the TI - digits English  and on the Italian data and also on the broad uh  English uh French and uh Spanish databases. Mmm , so there 's our result tables here , for the tandem approach , and um , actually what we  we @ @ observed is that if the network is trained on the task data it works pretty well.

OK. Our  our uh   There 's a   We 're pausing for a photo

How about over th from the front of the room ?


OK. Our  our uh   There 's a   We 're pausing for a photo

How about over th from the front of the room ?


How about over th from the front of the room ?

We 're pausing for a photo opportunity here. Uh.  Uh. So.


We 're pausing for a photo opportunity here. Uh.  Uh. So.


He 's facing this way. What ? OK , this  this would be a  good section for our silence detection.


OK. So um ,  you were saying  about the training data  Yeah.


OK. So um ,  you were saying  about the training data  Yeah.

Yeah , so if the network is trained on the task data um  tandem works pretty well. And uh actually we have uh , results are similar Only on ,


OK. So um ,  you were saying  about the training data  Yeah.

Yeah , so if the network is trained on the task data um  tandem works pretty well. And uh actually we have uh , results are similar Only on ,

Do you mean if it 's trained only on  On data from just that task ,


Yeah , so if the network is trained on the task data um  tandem works pretty well. And uh actually we have uh , results are similar Only on ,

Do you mean if it 's trained only on  On data from just that task ,


Do you mean if it 's trained only on  On data from just that task ,

Just that task. But actually we didn't train network on  uh both types of data I mean  uh  phonetically ba phonetically balanced uh data and task data.


Just that task. But actually we didn't train network on  uh both types of data I mean  uh  phonetically ba phonetically balanced uh data and task data.


Just that task. But actually we didn't train network on  uh both types of data I mean  uh  phonetically ba phonetically balanced uh data and task data.

We only did either task  task data or  uh broad  data.


We only did either task  task data or  uh broad  data.


We only did either task  task data or  uh broad  data.

So how  I mean  clearly it 's gonna be good then


So how  I mean  clearly it 's gonna be good then


So how  I mean  clearly it 's gonna be good then

but the question is how much  worse is it  if you have broad data ? I mean ,  my assump From what I saw from the earlier results , uh I guess last week ,  was that um ,  if you  trained on one language and tested on another , say , that  the results were  were relatively poor.


but the question is how much  worse is it  if you have broad data ? I mean ,  my assump From what I saw from the earlier results , uh I guess last week ,  was that um ,  if you  trained on one language and tested on another , say , that  the results were  were relatively poor.

But  but the question is if you train on one language  but you have a broad coverage  and then test in another ,  does that   is that improve things  i c in comparison ?


But  but the question is if you train on one language  but you have a broad coverage  and then test in another ,  does that   is that improve things  i c in comparison ?

No , no , no. Different lang So  um  If you train on TI - digits  and test on Italian digits ,  you do poorly ,  let 's say.


No , no , no. Different lang So  um  If you train on TI - digits  and test on Italian digits ,  you do poorly ,  let 's say.

I don't have the numbers in front of me ,


I don't have the numbers in front of me ,

so I 'm just imagining. E So , you didn't train on  TIMIT and test on   on Italian digits , say ?


I don't have the numbers in front of me ,

so I 'm just imagining. E So , you didn't train on  TIMIT and test on   on Italian digits , say ?

We  No , we did four  four kind of  of testing , actually. The first testing is  with task data  So , with nets trained on task data. So for Italian on the Italian speech @ @. The second test is trained on a single language um with broad database , but the same language as the t task data.


so I 'm just imagining. E So , you didn't train on  TIMIT and test on   on Italian digits , say ?

We  No , we did four  four kind of  of testing , actually. The first testing is  with task data  So , with nets trained on task data. So for Italian on the Italian speech @ @. The second test is trained on a single language um with broad database , but the same language as the t task data.


so I 'm just imagining. E So , you didn't train on  TIMIT and test on   on Italian digits , say ?

We  No , we did four  four kind of  of testing , actually. The first testing is  with task data  So , with nets trained on task data. So for Italian on the Italian speech @ @. The second test is trained on a single language um with broad database , but the same language as the t task data.

But for Italian we choose Spanish which  we assume is close to Italian. The third test is by using , um the three language database


We  No , we did four  four kind of  of testing , actually. The first testing is  with task data  So , with nets trained on task data. So for Italian on the Italian speech @ @. The second test is trained on a single language um with broad database , but the same language as the t task data.

But for Italian we choose Spanish which  we assume is close to Italian. The third test is by using , um the three language database


But for Italian we choose Spanish which  we assume is close to Italian. The third test is by using , um the three language database


But for Italian we choose Spanish which  we assume is close to Italian. The third test is by using , um the three language database

It has three languages. That 's including the w the   the


It has three languages. That 's including the w the   the


Yeah And the fourth test is uh  excluding from these three languages the language  that is  the task language.


Yeah And the fourth test is uh  excluding from these three languages the language  that is  the task language.

Oh , OK , yeah , so , that is what I wanted to know.


Yeah And the fourth test is uh  excluding from these three languages the language  that is  the task language.

Oh , OK , yeah , so , that is what I wanted to know.

I just wasn't saying it very well , I guess.


Oh , OK , yeah , so , that is what I wanted to know.

I just wasn't saying it very well , I guess.

Uh , yeah. So um  for uh TI - digits for ins example  uh when we go from TI - digits training to  TIMIT training  uh we lose  uh around ten percent , uh. The error rate increase u of  of  of ten percent , relative.


I just wasn't saying it very well , I guess.

Uh , yeah. So um  for uh TI - digits for ins example  uh when we go from TI - digits training to  TIMIT training  uh we lose  uh around ten percent , uh. The error rate increase u of  of  of ten percent , relative.


I just wasn't saying it very well , I guess.

Uh , yeah. So um  for uh TI - digits for ins example  uh when we go from TI - digits training to  TIMIT training  uh we lose  uh around ten percent , uh. The error rate increase u of  of  of ten percent , relative.

So this is not so bad. And then when we jump to the multilingual data it 's uh it become worse and , well Around uh , let 's say ,  twenty perc twenty percent further.


Uh , yeah. So um  for uh TI - digits for ins example  uh when we go from TI - digits training to  TIMIT training  uh we lose  uh around ten percent , uh. The error rate increase u of  of  of ten percent , relative.

So this is not so bad. And then when we jump to the multilingual data it 's uh it become worse and , well Around uh , let 's say ,  twenty perc twenty percent further.


So this is not so bad. And then when we jump to the multilingual data it 's uh it become worse and , well Around uh , let 's say ,  twenty perc twenty percent further.


Twenty to  to thirty percent further. Yeah.


Twenty to  to thirty percent further. Yeah.

And so , remind me , the multilingual stuff is just the broad data. Right ? It 's not the digits.


Twenty to  to thirty percent further. Yeah.

And so , remind me , the multilingual stuff is just the broad data. Right ? It 's not the digits.

So it 's the combination of  two things there. It 's  removing the  task specific  training and  it 's adding other languages.


And so , remind me , the multilingual stuff is just the broad data. Right ? It 's not the digits.

So it 's the combination of  two things there. It 's  removing the  task specific  training and  it 's adding other languages.


So it 's the combination of  two things there. It 's  removing the  task specific  training and  it 's adding other languages.


So it 's the combination of  two things there. It 's  removing the  task specific  training and  it 's adding other languages.

But the first step is al already removing the task s specific from  from


But the first step is al already removing the task s specific from  from


Yeah. Uh  So , basically when it 's trained on the  the multilingual broad data  um or number  so , the  the  ratio of our error rates uh with the  baseline error rate is around  uh one point one.


Yeah. Uh  So , basically when it 's trained on the  the multilingual broad data  um or number  so , the  the  ratio of our error rates uh with the  baseline error rate is around  uh one point one.

Yes.  And it 's something like one point three of  of the  uh


Yeah. Uh  So , basically when it 's trained on the  the multilingual broad data  um or number  so , the  the  ratio of our error rates uh with the  baseline error rate is around  uh one point one.

Yes.  And it 's something like one point three of  of the  uh

I i if you compare everything to the first case at the baseline , you get something like one point one for the  for the using the same language but a different task , and something like one point three  for three  three languages  broad stuff.


Yes.  And it 's something like one point three of  of the  uh

I i if you compare everything to the first case at the baseline , you get something like one point one for the  for the using the same language but a different task , and something like one point three  for three  three languages  broad stuff.

No no no. Uh same language we are at uh  for at English at O point eight. So it improves ,  compared to the baseline. But  So. Le - let me.


I i if you compare everything to the first case at the baseline , you get something like one point one for the  for the using the same language but a different task , and something like one point three  for three  three languages  broad stuff.

No no no. Uh same language we are at uh  for at English at O point eight. So it improves ,  compared to the baseline. But  So. Le - let me.


No no no. Uh same language we are at uh  for at English at O point eight. So it improves ,  compared to the baseline. But  So. Le - let me.

I  I  I meant something different by baseline


I  I  I meant something different by baseline


OK , fine. Let 's  let 's use the conventional meaning of baseline.


OK , fine. Let 's  let 's use the conventional meaning of baseline.

I  I  By baseline here I meant  uh using the task specific data.


I  I  By baseline here I meant  uh using the task specific data.

But uh   uh , because that 's what you were just doing with this ten percent.


But uh   uh , because that 's what you were just doing with this ten percent.

So I was just  I just trying to understand that.


So I was just  I just trying to understand that.

So if we call  a factor of w just one , just normalized to one , the word error rate  that you have  for using TI - digits as  as  training and TI - digits as test ,


So if we call  a factor of w just one , just normalized to one , the word error rate  that you have  for using TI - digits as  as  training and TI - digits as test ,


but   but uh , uh the same  task and so on.


but   but uh , uh the same  task and so on.

If we call that " one " ,  then what you 're saying is  that the word error rate  for the same language but using  uh different training data than you 're testing on , say TIMIT and so forth ,  it 's one point one.


but   but uh , uh the same  task and so on.

If we call that " one " ,  then what you 're saying is  that the word error rate  for the same language but using  uh different training data than you 're testing on , say TIMIT and so forth ,  it 's one point one.

Mm - hmm. Yeah , it 's around one point one.


If we call that " one " ,  then what you 're saying is  that the word error rate  for the same language but using  uh different training data than you 're testing on , say TIMIT and so forth ,  it 's one point one.

Mm - hmm. Yeah , it 's around one point one.


Mm - hmm. Yeah , it 's around one point one.

you  do  go to  three languages including the English ,  it 's something like one point three. That 's what you were just saying , I think.


you  do  go to  three languages including the English ,  it 's something like one point three. That 's what you were just saying , I think.


So , it 's an additional thirty percent.


So , it 's an additional thirty percent.

What would you say ? Around one point four


What would you say ? Around one point four

And if you exclude  English ,  from this combination , what 's that ?


And if you exclude  English ,  from this combination , what 's that ?

If we exclude English ,  um  there is  not much difference with the  data with English.


If we exclude English ,  um  there is  not much difference with the  data with English.

That 's interesting.  That 's interesting. Do you see ? Because  Uh ,


That 's interesting.  That 's interesting. Do you see ? Because  Uh ,


That 's interesting.  That 's interesting. Do you see ? Because  Uh ,

so  No , that  that 's important. So what  what it 's saying here is just that " yes , there is a reduction  in performance ,  when you don't  um  have the s  when you don't have  um


so  No , that  that 's important. So what  what it 's saying here is just that " yes , there is a reduction  in performance ,  when you don't  um  have the s  when you don't have  um


No , actually  it 's interesting. So it 's  So when you go to a different task , there 's actually not so  different. It 's when you went to these  So what 's the difference between two and three ? Between the one point one case and the one point four case ? I 'm confused.


No , actually  it 's interesting. So it 's  So when you go to a different task , there 's actually not so  different. It 's when you went to these  So what 's the difference between two and three ? Between the one point one case and the one point four case ? I 'm confused.

Yeah. The only difference it 's  is that it 's multilingual  Um


No , actually  it 's interesting. So it 's  So when you go to a different task , there 's actually not so  different. It 's when you went to these  So what 's the difference between two and three ? Between the one point one case and the one point four case ? I 'm confused.

Yeah. The only difference it 's  is that it 's multilingual  Um

Cuz in both  in both  both of those cases , you don't have the same task.


Yeah. The only difference it 's  is that it 's multilingual  Um

Cuz in both  in both  both of those cases , you don't have the same task.


Yeah. The only difference it 's  is that it 's multilingual  Um

Cuz in both  in both  both of those cases , you don't have the same task.

So is  is the training data for the  for this one point four case  does it include the training data for the one point one case ?


Cuz in both  in both  both of those cases , you don't have the same task.

So is  is the training data for the  for this one point four case  does it include the training data for the one point one case ?


So is  is the training data for the  for this one point four case  does it include the training data for the one point one case ?


actually ? Yeah. Um. The English data   No , the multilingual databases are two times the  broad English  data. We just wanted to keep this , w well , not too huge. So.


actually ? Yeah. Um. The English data   No , the multilingual databases are two times the  broad English  data. We just wanted to keep this , w well , not too huge. So.

So it 's two times , but it includes the  but it includes the broad English data.


actually ? Yeah. Um. The English data   No , the multilingual databases are two times the  broad English  data. We just wanted to keep this , w well , not too huge. So.

So it 's two times , but it includes the  but it includes the broad English data.

And the broad English data is what you got this one point one  with. So that 's TIMIT basically right ?


So it 's two times , but it includes the  but it includes the broad English data.

And the broad English data is what you got this one point one  with. So that 's TIMIT basically right ?


And the broad English data is what you got this one point one  with. So that 's TIMIT basically right ?


And the broad English data is what you got this one point one  with. So that 's TIMIT basically right ?

So it 's band - limited TIMIT. This is all eight kilohertz sampling.


So it 's band - limited TIMIT. This is all eight kilohertz sampling.


So you have band - limited TIMIT ,  gave you uh almost as good as a result as using TI - digits  on a TI - digits test. OK ?


So you have band - limited TIMIT ,  gave you uh almost as good as a result as using TI - digits  on a TI - digits test. OK ?

Um  and  um But ,  when you add in more training data but keep the neural net the same size ,  it  um performs worse on the TI - digits. OK , now all of this is   This is noisy  TI - digits , I assume ? Both training and test ?


So you have band - limited TIMIT ,  gave you uh almost as good as a result as using TI - digits  on a TI - digits test. OK ?

Um  and  um But ,  when you add in more training data but keep the neural net the same size ,  it  um performs worse on the TI - digits. OK , now all of this is   This is noisy  TI - digits , I assume ? Both training and test ?

Yeah. OK. Um OK. Well.  We  we  we may just need to uh  So I mean it 's interesting that h going to a different  different task didn't seem to hurt us that much , and going to a different language um It doesn't seem to matter  The difference between three and four is not particularly great , so that means that  whether you have the language in or not is not such a big deal.


Um  and  um But ,  when you add in more training data but keep the neural net the same size ,  it  um performs worse on the TI - digits. OK , now all of this is   This is noisy  TI - digits , I assume ? Both training and test ?

Yeah. OK. Um OK. Well.  We  we  we may just need to uh  So I mean it 's interesting that h going to a different  different task didn't seem to hurt us that much , and going to a different language um It doesn't seem to matter  The difference between three and four is not particularly great , so that means that  whether you have the language in or not is not such a big deal.


Um  and  um But ,  when you add in more training data but keep the neural net the same size ,  it  um performs worse on the TI - digits. OK , now all of this is   This is noisy  TI - digits , I assume ? Both training and test ?

Yeah. OK. Um OK. Well.  We  we  we may just need to uh  So I mean it 's interesting that h going to a different  different task didn't seem to hurt us that much , and going to a different language um It doesn't seem to matter  The difference between three and four is not particularly great , so that means that  whether you have the language in or not is not such a big deal.

It sounds like um  uh  we may need to have more  of uh things that are similar to a target language or  I mean.  You have the same number of parameters in the neural net , you haven't increased the size of the neural net , and maybe there 's just   just not enough  complexity to it to represent  the variab increased variability in the  in the training set. That  that could be. Um  So , what about  So these are results with  uh th  that you 're describing now , that  they are pretty similar for the different features or   or uh


Yeah. OK. Um OK. Well.  We  we  we may just need to uh  So I mean it 's interesting that h going to a different  different task didn't seem to hurt us that much , and going to a different language um It doesn't seem to matter  The difference between three and four is not particularly great , so that means that  whether you have the language in or not is not such a big deal.

It sounds like um  uh  we may need to have more  of uh things that are similar to a target language or  I mean.  You have the same number of parameters in the neural net , you haven't increased the size of the neural net , and maybe there 's just   just not enough  complexity to it to represent  the variab increased variability in the  in the training set. That  that could be. Um  So , what about  So these are results with  uh th  that you 're describing now , that  they are pretty similar for the different features or   or uh


It sounds like um  uh  we may need to have more  of uh things that are similar to a target language or  I mean.  You have the same number of parameters in the neural net , you haven't increased the size of the neural net , and maybe there 's just   just not enough  complexity to it to represent  the variab increased variability in the  in the training set. That  that could be. Um  So , what about  So these are results with  uh th  that you 're describing now , that  they are pretty similar for the different features or   or uh


Um. The  Yeah. For the PLP with JRASTA the   the  we  This is quite the same  tendency ,  with a slight increase of the error rate ,  uh if we go to  to TIMIT. And then it 's  it gets worse with the multilingual. Um. Yeah. There  there is a difference actually with  b between PLP and JRASTA is that  JRASTA  seems to  perform better with the highly mismatched  condition  but slightly  slightly worse  for the well matched condition. Mmm.


Um. The  Yeah. For the PLP with JRASTA the   the  we  This is quite the same  tendency ,  with a slight increase of the error rate ,  uh if we go to  to TIMIT. And then it 's  it gets worse with the multilingual. Um. Yeah. There  there is a difference actually with  b between PLP and JRASTA is that  JRASTA  seems to  perform better with the highly mismatched  condition  but slightly  slightly worse  for the well matched condition. Mmm.

I have a suggestion , actually , even though it 'll delay us slightly , would  would you mind  running into the other room and making  copies of this ? Cuz we 're all sort of  If we c if we could look at it , while we 're talking , I think it 'd be


Um. The  Yeah. For the PLP with JRASTA the   the  we  This is quite the same  tendency ,  with a slight increase of the error rate ,  uh if we go to  to TIMIT. And then it 's  it gets worse with the multilingual. Um. Yeah. There  there is a difference actually with  b between PLP and JRASTA is that  JRASTA  seems to  perform better with the highly mismatched  condition  but slightly  slightly worse  for the well matched condition. Mmm.

I have a suggestion , actually , even though it 'll delay us slightly , would  would you mind  running into the other room and making  copies of this ? Cuz we 're all sort of  If we c if we could look at it , while we 're talking , I think it 'd be

uh   Uh , I 'll  I 'll sing a song or dance or something while you  do it , too.


I have a suggestion , actually , even though it 'll delay us slightly , would  would you mind  running into the other room and making  copies of this ? Cuz we 're all sort of  If we c if we could look at it , while we 're talking , I think it 'd be

uh   Uh , I 'll  I 'll sing a song or dance or something while you  do it , too.


uh   Uh , I 'll  I 'll sing a song or dance or something while you  do it , too.


uh   Uh , I 'll  I 'll sing a song or dance or something while you  do it , too.

Go ahead. Ah , while you 're gone I 'll ask s some of my questions.


Go ahead. Ah , while you 're gone I 'll ask s some of my questions.


Go ahead. Ah , while you 're gone I 'll ask s some of my questions.

Yeah. Uh , this way and just slightly to the left , yeah.


Yeah. Uh , this way and just slightly to the left , yeah.

The um  What was  Was this number  forty or  It was roughly the same as this one ,  he said ? When you had the two language versus the three language ?


The um  What was  Was this number  forty or  It was roughly the same as this one ,  he said ? When you had the two language versus the three language ?


It sometimes , actually , depends on what features you 're using.


It sometimes , actually , depends on what features you 're using.

I mean. That 's interesting because  it  it seems like what it 's saying is not so much that you got hurt  uh because  you  uh didn't have so much representation of English , because in the other case you don't get hurt any more , at least when  it seemed like uh it  it might simply be a case that you have something that is just much more diverse ,


I mean. That 's interesting because  it  it seems like what it 's saying is not so much that you got hurt  uh because  you  uh didn't have so much representation of English , because in the other case you don't get hurt any more , at least when  it seemed like uh it  it might simply be a case that you have something that is just much more diverse ,


I mean. That 's interesting because  it  it seems like what it 's saying is not so much that you got hurt  uh because  you  uh didn't have so much representation of English , because in the other case you don't get hurt any more , at least when  it seemed like uh it  it might simply be a case that you have something that is just much more diverse ,

but you have the same number of parameters representing it.


I mean. That 's interesting because  it  it seems like what it 's saying is not so much that you got hurt  uh because  you  uh didn't have so much representation of English , because in the other case you don't get hurt any more , at least when  it seemed like uh it  it might simply be a case that you have something that is just much more diverse ,

but you have the same number of parameters representing it.

Mm - hmm. I wonder  were um all three of these nets  using the same output ? This multi - language  uh labelling ?


but you have the same number of parameters representing it.

Mm - hmm. I wonder  were um all three of these nets  using the same output ? This multi - language  uh labelling ?

He was using uh sixty - four phonemes from  SAMPA.


Mm - hmm. I wonder  were um all three of these nets  using the same output ? This multi - language  uh labelling ?

He was using uh sixty - four phonemes from  SAMPA.


He was using uh sixty - four phonemes from  SAMPA.

So this would   From this you would say , " well , it doesn't really matter if we put Finnish  into  the training of the neural net ,  if there 's  gonna be ,  you know , Finnish in the test data. " Right ?


So this would   From this you would say , " well , it doesn't really matter if we put Finnish  into  the training of the neural net ,  if there 's  gonna be ,  you know , Finnish in the test data. " Right ?

Well , it 's  it sounds   I mean , we have to be careful , cuz we haven't gotten a good result yet.


So this would   From this you would say , " well , it doesn't really matter if we put Finnish  into  the training of the neural net ,  if there 's  gonna be ,  you know , Finnish in the test data. " Right ?

Well , it 's  it sounds   I mean , we have to be careful , cuz we haven't gotten a good result yet.

And comparing different bad results can be  tricky.


Well , it 's  it sounds   I mean , we have to be careful , cuz we haven't gotten a good result yet.

And comparing different bad results can be  tricky.


And comparing different bad results can be  tricky.

But I  I  I   I think it does suggest that it 's not so much uh  uh cross  language as cross type of speech.


But I  I  I   I think it does suggest that it 's not so much uh  uh cross  language as cross type of speech.

It 's  it 's um   But we did  Oh yeah , the other thing I was asking him , though , is that I think that in the case  Yeah , you  you do have to be careful because of com compounded results. I think we got some earlier results  in which you trained on one language and tested on another and you didn't have  three , but you just had one  language. So you trained on  one type of digits and tested on another. Didn - Wasn't there something of that ? Where you ,  say , trained on Spanish and tested on  on TI - digits , or the other way around ? Something like that ?


It 's  it 's um   But we did  Oh yeah , the other thing I was asking him , though , is that I think that in the case  Yeah , you  you do have to be careful because of com compounded results. I think we got some earlier results  in which you trained on one language and tested on another and you didn't have  three , but you just had one  language. So you trained on  one type of digits and tested on another. Didn - Wasn't there something of that ? Where you ,  say , trained on Spanish and tested on  on TI - digits , or the other way around ? Something like that ?

I thought there was something like that ,  that he showed me  last week. We 'll have to wait till we get


I thought there was something like that ,  that he showed me  last week. We 'll have to wait till we get

Um , This may have been what I was asking before , Stephane , but   but , um , wasn't there something that you did ,  where you trained  on one language and tested on another ? I mean no  no mixture but just


Um , This may have been what I was asking before , Stephane , but   but , um , wasn't there something that you did ,  where you trained  on one language and tested on another ? I mean no  no mixture but just


Training on a single language , you mean , and testing on the other one ?


So the only  task that 's similar to this is the training on two languages , and  that


So the only  task that 's similar to this is the training on two languages , and  that

But we 've done a bunch of things where we just trained on one language. Right ? I mean , you haven't  you haven't done all your tests on multiple languages.


So the only  task that 's similar to this is the training on two languages , and  that

But we 've done a bunch of things where we just trained on one language. Right ? I mean , you haven't  you haven't done all your tests on multiple languages.

Uh , No. Either thi this is test with  uh the same language  but from the broad data , or it 's test with  uh different languages also from the broad data , excluding the  So , it 's  it 's three or  three and four.


But we 've done a bunch of things where we just trained on one language. Right ? I mean , you haven't  you haven't done all your tests on multiple languages.

Uh , No. Either thi this is test with  uh the same language  but from the broad data , or it 's test with  uh different languages also from the broad data , excluding the  So , it 's  it 's three or  three and four.

Did you do different languages from digits ?


Uh , No. Either thi this is test with  uh the same language  but from the broad data , or it 's test with  uh different languages also from the broad data , excluding the  So , it 's  it 's three or  three and four.

Did you do different languages from digits ?

Uh. No. You mean  training digits  on one language and using the net  to recognize on the other ?


Did you do different languages from digits ?

Uh. No. You mean  training digits  on one language and using the net  to recognize on the other ?


Uh. No. You mean  training digits  on one language and using the net  to recognize on the other ?

See , I thought you showed me something like that last week. You had a  you had a little


See , I thought you showed me something like that last week. You had a  you had a little


See , I thought you showed me something like that last week. You had a  you had a little

These numbers are uh  ratio to baseline ?


These numbers are uh  ratio to baseline ?


These numbers are uh  ratio to baseline ?

This  this chart  this table that we 're looking at  is um , show is all testing for TI - digits , or  ?


This  this chart  this table that we 're looking at  is um , show is all testing for TI - digits , or  ?


and it 's divided in three  rows  of four  four rows each.


and it 's divided in three  rows  of four  four rows each.

And the first four rows is well - matched , then the s the second group of four rows is mismatched , and  finally highly mismatched. And then the lower part is for Italian and it 's the same   the same thing.


And the first four rows is well - matched , then the s the second group of four rows is mismatched , and  finally highly mismatched. And then the lower part is for Italian and it 's the same   the same thing.

So , so the upper part is training  TI - digits ?


And the first four rows is well - matched , then the s the second group of four rows is mismatched , and  finally highly mismatched. And then the lower part is for Italian and it 's the same   the same thing.

So , so the upper part is training  TI - digits ?

So. It 's  it 's the HTK results , I mean. So it 's  HTK training testings  with different kind of features


So , so the upper part is training  TI - digits ?

So. It 's  it 's the HTK results , I mean. So it 's  HTK training testings  with different kind of features

and what appears in the  uh left column is  the networks that are used for doing this.


So. It 's  it 's the HTK results , I mean. So it 's  HTK training testings  with different kind of features

and what appears in the  uh left column is  the networks that are used for doing this.


and what appears in the  uh left column is  the networks that are used for doing this.


and what appears in the  uh left column is  the networks that are used for doing this.

Well , What was is that i What was it that you had  done  last week when you showed  Do you remember ? Wh - when you showed me  the  your table last week ?


Well , What was is that i What was it that you had  done  last week when you showed  Do you remember ? Wh - when you showed me  the  your table last week ?

It - It was part of these results. Mmm. Mmm.


Well , What was is that i What was it that you had  done  last week when you showed  Do you remember ? Wh - when you showed me  the  your table last week ?

It - It was part of these results. Mmm. Mmm.

So where is the baseline  for the TI - digits  located in here ?