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

I mean , i it wouldn't be perfect , but  but it does seem more natural to give a backchannel when  when you 're somehow involved in the topic ,


I mean , i it wouldn't be perfect , but  but it does seem more natural to give a backchannel when  when you 're somehow involved in the topic ,


I mean , i it wouldn't be perfect , but  but it does seem more natural to give a backchannel when  when you 're somehow involved in the topic ,

and the most natural way is for you to have initiated the topic by asking a question.


and the most natural way is for you to have initiated the topic by asking a question.


and the most natural way is for you to have initiated the topic by asking a question.

I think  No. I think it 's  actually I think what 's going on is backchannelling is something that happens in two - party conversations.


I think  No. I think it 's  actually I think what 's going on is backchannelling is something that happens in two - party conversations.


I think  No. I think it 's  actually I think what 's going on is backchannelling is something that happens in two - party conversations.

And if you ask someone a question , you essentially initiating a little two - party conversation.


And if you ask someone a question , you essentially initiating a little two - party conversation.

Well , actu Yeah , when we looked at this


Well , actu Yeah , when we looked at this

So then you 're  so and then you 're expected to backchannel because the person is addressing you directly and not everybody.


Well , actu Yeah , when we looked at this

So then you 're  so and then you 're expected to backchannel because the person is addressing you directly and not everybody.

Exactly. Exactly my point. An - and so this is the expectation thing that  uh , uh ,


So then you 're  so and then you 're expected to backchannel because the person is addressing you directly and not everybody.

Exactly. Exactly my point. An - and so this is the expectation thing that  uh , uh ,


Exactly. Exactly my point. An - and so this is the expectation thing that  uh , uh ,


But in addition , you know , if someone has done this analysis himself and isn't involved in the dyad , but they might also give backchannels to verify what  what the answer is that this  that the  the answerer 's given


But in addition , you know , if someone has done this analysis himself and isn't involved in the dyad , but they might also give backchannels to verify what  what the answer is that this  that the  the answerer 's given

I tell you , I say  I say " uh - huh " a lot ,


I tell you , I say  I say " uh - huh " a lot ,


But there are fewer  I think there are fewer " uh - huhs ".


But there are fewer  I think there are fewer " uh - huhs ".

I mean , just from  We were looking at word frequency lists to try to find the cases that we would allow to have the reject words in between in doing the alignment. You know the ones we wouldn't constrain to be next to the other words.


I mean , just from  We were looking at word frequency lists to try to find the cases that we would allow to have the reject words in between in doing the alignment. You know the ones we wouldn't constrain to be next to the other words.

And " uh - huh " is not as frequent as it sort of would be in Switchboard , if you looked at just a word frequency list of one - word short utterances. And " yeah " is way up there , but not " uh - huh ". And so I was thinking thi it 's not like  you 're being encouraged by everybody else to keep  talking in the meeting. And uh , that 's all , I I 'll stop there , cuz I I think what you say makes a lot of sense.


And " uh - huh " is not as frequent as it sort of would be in Switchboard , if you looked at just a word frequency list of one - word short utterances. And " yeah " is way up there , but not " uh - huh ". And so I was thinking thi it 's not like  you 're being encouraged by everybody else to keep  talking in the meeting. And uh , that 's all , I I 'll stop there , cuz I I think what you say makes a lot of sense.


And " uh - huh " is not as frequent as it sort of would be in Switchboard , if you looked at just a word frequency list of one - word short utterances. And " yeah " is way up there , but not " uh - huh ". And so I was thinking thi it 's not like  you 're being encouraged by everybody else to keep  talking in the meeting. And uh , that 's all , I I 'll stop there , cuz I I think what you say makes a lot of sense.

Well , an And what you say is the  is the re uh , o other side of this , which is that , you know , so th there are lots of channels where you don't have these backchannels , w when a question has been asked and  and these


Well , an And what you say is the  is the re uh , o other side of this , which is that , you know , so th there are lots of channels where you don't have these backchannels , w when a question has been asked and  and these

Right. There 's just probably less backchannelling in general ,


Well , an And what you say is the  is the re uh , o other side of this , which is that , you know , so th there are lots of channels where you don't have these backchannels , w when a question has been asked and  and these

Right. There 's just probably less backchannelling in general ,

Mm - hmm. So that 's good news , really.


Well , an And what you say is the  is the re uh , o other side of this , which is that , you know , so th there are lots of channels where you don't have these backchannels , w when a question has been asked and  and these

Right. There 's just probably less backchannelling in general ,

Mm - hmm. So that 's good news , really.

even if you consider every other person altogether one person in the meeting , but we 'll find out anyway. We were  I guess the other thing we 're  we 're  I should say is that we 're gonna , um try  compare this type of overlap analysis to Switchboard , where


Right. There 's just probably less backchannelling in general ,

Mm - hmm. So that 's good news , really.

even if you consider every other person altogether one person in the meeting , but we 'll find out anyway. We were  I guess the other thing we 're  we 're  I should say is that we 're gonna , um try  compare this type of overlap analysis to Switchboard , where


Mm - hmm. So that 's good news , really.

even if you consider every other person altogether one person in the meeting , but we 'll find out anyway. We were  I guess the other thing we 're  we 're  I should say is that we 're gonna , um try  compare this type of overlap analysis to Switchboard , where

and CallHome , where we have both sides , so that we can try to answer this question of , you know ,  is there really more overlap in meetings or is it just because we don't have the other channel in Switchboard


even if you consider every other person altogether one person in the meeting , but we 'll find out anyway. We were  I guess the other thing we 're  we 're  I should say is that we 're gonna , um try  compare this type of overlap analysis to Switchboard , where

and CallHome , where we have both sides , so that we can try to answer this question of , you know ,  is there really more overlap in meetings or is it just because we don't have the other channel in Switchboard


and CallHome , where we have both sides , so that we can try to answer this question of , you know ,  is there really more overlap in meetings or is it just because we don't have the other channel in Switchboard


and CallHome , where we have both sides , so that we can try to answer this question of , you know ,  is there really more overlap in meetings or is it just because we don't have the other channel in Switchboard

and we don't know what people are doing. Try to create a paper out of that.


and we don't know what people are doing. Try to create a paper out of that.

Yeah. I mean , y y you folks have probably  already told me , but were  were you intending to do a Eurospeech submission , or  ?


Yeah. I mean , y y you folks have probably  already told me , but were  were you intending to do a Eurospeech submission , or  ?

Yeah. Well , we 're still , like , writing the scripts for doing the research , and we will  Yes , we 're gonna try.


Yeah. Well , we 're still , like , writing the scripts for doing the research , and we will  Yes , we 're gonna try.


Yeah. Well , we 're still , like , writing the scripts for doing the research , and we will  Yes , we 're gonna try.

And I was telling Don , do not  take this as an example of how people should work.


And I was telling Don , do not  take this as an example of how people should work.


It is different. In previous years , Eurospeech only had the abstract due by now , not the full paper.


It is different. In previous years , Eurospeech only had the abstract due by now , not the full paper.

And so all our timing was off. I 've given up on trying to do digits. I just don't think that what I have so far makes a Eurospeech paper.


And so all our timing was off. I 've given up on trying to do digits. I just don't think that what I have so far makes a Eurospeech paper.

Well , I 'm no We may be in the same position , and I figured  we 'll try , because that 'll at least get us to the point where we have  We have this really nice database format that Andreas and I were working out that  It  it 's not very fancy. It 's just a ASCII line by line format , but it does give you information


And so all our timing was off. I 've given up on trying to do digits. I just don't think that what I have so far makes a Eurospeech paper.

Well , I 'm no We may be in the same position , and I figured  we 'll try , because that 'll at least get us to the point where we have  We have this really nice database format that Andreas and I were working out that  It  it 's not very fancy. It 's just a ASCII line by line format , but it does give you information

It  Yeah , we 're calling these " spurts " after Chafe. I was trying to find what 's a word for  a continuous region with  pauses around it ?


Well , I 'm no We may be in the same position , and I figured  we 'll try , because that 'll at least get us to the point where we have  We have this really nice database format that Andreas and I were working out that  It  it 's not very fancy. It 's just a ASCII line by line format , but it does give you information

It  Yeah , we 're calling these " spurts " after Chafe. I was trying to find what 's a word for  a continuous region with  pauses around it ?


It  Yeah , we 're calling these " spurts " after Chafe. I was trying to find what 's a word for  a continuous region with  pauses around it ?

Yeah. I know that th the Telecom people use  use " spurt " for that.


Yeah. I know that th the Telecom people use  use " spurt " for that.


And that 's  I mean , I  I was using that for a while when I was doing the rate of speech stuff ,


And that 's  I mean , I  I was using that for a while when I was doing the rate of speech stuff ,

because I  because I looked up in some books and I found  OK , I wanna find a spurt  in which


And that 's  I mean , I  I was using that for a while when I was doing the rate of speech stuff ,

because I  because I looked up in some books and I found  OK , I wanna find a spurt  in which

Ah , right ! It 's just , like , defined by the acoustics.


because I  because I looked up in some books and I found  OK , I wanna find a spurt  in which

Ah , right ! It 's just , like , defined by the acoustics.

and  an because  cuz it 's another question about how  many pauses they put in between them.


Ah , right ! It 's just , like , defined by the acoustics.

and  an because  cuz it 's another question about how  many pauses they put in between them.


and  an because  cuz it 's another question about how  many pauses they put in between them.

But how fast do they do  the words within the spurt ?


But how fast do they do  the words within the spurt ?


But how fast do they do  the words within the spurt ?

Well , that 's what we were calling spurt ,


Well , that 's what we were calling spurt ,


Spurt has the horrible name overloading with other  with hardware at ICSI.


Spurt has the horrible name overloading with other  with hardware at ICSI.

Well , well , Chafe had this wor I think it was Chafe , or somebody had a  the word " spurt " originally ,


Well , well , Chafe had this wor I think it was Chafe , or somebody had a  the word " spurt " originally ,


Well , see , I know S Sue wrote about spurts of development.


Well , see , I know S Sue wrote about spurts of development.

But , in any case , I think it 's a good term ,


But , in any case , I think it 's a good term ,

So we have spurts and we have spurt - ify dot shell and spurt - ify


So we have spurts and we have spurt - ify dot shell and spurt - ify


And then it 's got all  it 's a verb now.


But maybe he speaks about spurts as well


So what we 're doing  uh , this  this is just  maybe someone has s some  some ideas about how to do it better ,


So what we 're doing  uh , this  this is just  maybe someone has s some  some ideas about how to do it better ,

but we  So we 're taking these , uh , alignments from the individual channels. We 're  from each alignment we 're producing , uh , one of these CTM files ,


but we  So we 're taking these , uh , alignments from the individual channels. We 're  from each alignment we 're producing , uh , one of these CTM files ,

which essentially has  it 's just a linear sequence of words with the begin times for every word and the duration.


but we  So we 're taking these , uh , alignments from the individual channels. We 're  from each alignment we 're producing , uh , one of these CTM files ,

which essentially has  it 's just a linear sequence of words with the begin times for every word and the duration.

It looks like a Waves label file almost. Right ?


which essentially has  it 's just a linear sequence of words with the begin times for every word and the duration.

It looks like a Waves label file almost. Right ?


It looks like a Waves label file almost. Right ?

Right. But it has  one  the first column has the meeting name , so it could actually contain several meetings. Um. And the second column is the channel. Third column is the , um , start times of the words and the fourth column is the duration of the words. And then we 're , um  OK. Then we have a messy alignment process where we actually insert into the sequence of words the , uh , tags for , like , where  where sentence  ends of sentence , question marks , um ,  various other things.


Right. But it has  one  the first column has the meeting name , so it could actually contain several meetings. Um. And the second column is the channel. Third column is the , um , start times of the words and the fourth column is the duration of the words. And then we 're , um  OK. Then we have a messy alignment process where we actually insert into the sequence of words the , uh , tags for , like , where  where sentence  ends of sentence , question marks , um ,  various other things.


Right. But it has  one  the first column has the meeting name , so it could actually contain several meetings. Um. And the second column is the channel. Third column is the , um , start times of the words and the fourth column is the duration of the words. And then we 're , um  OK. Then we have a messy alignment process where we actually insert into the sequence of words the , uh , tags for , like , where  where sentence  ends of sentence , question marks , um ,  various other things.

So , Don sort of , um , propagated the punctuation from the original transcriber


So , Don sort of , um , propagated the punctuation from the original transcriber


So , Don sort of , um , propagated the punctuation from the original transcriber

so whether it was , like , question mark or period or ,  um , you know , comma and things like that , and we kept the  and disfluency dashes  uh , kept those in because we sort of wanna know where those are relative to the spurt overlaps


so whether it was , like , question mark or period or ,  um , you know , comma and things like that , and we kept the  and disfluency dashes  uh , kept those in because we sort of wanna know where those are relative to the spurt overlaps


so whether it was , like , question mark or period or ,  um , you know , comma and things like that , and we kept the  and disfluency dashes  uh , kept those in because we sort of wanna know where those are relative to the spurt overlaps

So  so those are actually sort of retro - fitted into the time alignment.


So  so those are actually sort of retro - fitted into the time alignment.


So  so those are actually sort of retro - fitted into the time alignment.

And then we merge all the alignments from the various channels and we sort them by time. And then there 's a  then there 's a process where you now determine the spurts. That is  Actually , no , you do that before you merge the various channels. So you  you id identify by some criterion , which is pause length  you identify the beginnings and ends of these spurts , and you put another set of tags in there to keep those straight.


And then we merge all the alignments from the various channels and we sort them by time. And then there 's a  then there 's a process where you now determine the spurts. That is  Actually , no , you do that before you merge the various channels. So you  you id identify by some criterion , which is pause length  you identify the beginnings and ends of these spurts , and you put another set of tags in there to keep those straight.

And then you merge everything in terms of , you know , linearizing the sequence based on the time marks. And then  you extract the individual channels again , but this time you know where the other people start and end talking  you know , where their spurts start and end. And so you extract the individual channels , uh , one sp spurt by spurt as it were. Um , and inside the words or between the words you now have begin and end  tags for overlaps. So , you  you basically have everything sort of lined up and in a form where you can look at the individual speakers and how their speech relates to the other speakers ' speech.


And then you merge everything in terms of , you know , linearizing the sequence based on the time marks. And then  you extract the individual channels again , but this time you know where the other people start and end talking  you know , where their spurts start and end. And so you extract the individual channels , uh , one sp spurt by spurt as it were. Um , and inside the words or between the words you now have begin and end  tags for overlaps. So , you  you basically have everything sort of lined up and in a form where you can look at the individual speakers and how their speech relates to the other speakers ' speech.

Uh , I mean , I think that 's actually really u useful also


Uh , I mean , I think that 's actually really u useful also

because even if you weren't studying overlaps , if you wanna get a transcription for the far - field mikes , how are you gonna know which words from which speakers occurred at which times relative to each other ? You have to be able to  get a transcript like  like this anyway , just for doing far - field recognition. So , you know , it 's  it 's sort of


because even if you weren't studying overlaps , if you wanna get a transcription for the far - field mikes , how are you gonna know which words from which speakers occurred at which times relative to each other ? You have to be able to  get a transcript like  like this anyway , just for doing far - field recognition. So , you know , it 's  it 's sort of

I thi it 's just an issue we haven't dealt with before , how you time - align things that are overlapping anyway.


I thi it 's just an issue we haven't dealt with before , how you time - align things that are overlapping anyway.


I thi it 's just an issue we haven't dealt with before , how you time - align things that are overlapping anyway.

I mean , i I never thought about it before ,


I mean , i I never thought about it before ,


I mean , s when I came up with the original data  suggested data format based on the transcription graph , there 's capability of doing that sort of thing in there.


I mean , s when I came up with the original data  suggested data format based on the transcription graph , there 's capability of doing that sort of thing in there.

Right. But you can't get it directly from the transcription.


Right. But you can't get it directly from the transcription.

Yeah , this is like a poor man 's ver formatting version. But it 's , you know  It 's clean , it 's just not fancy.


Yeah , this is like a poor man 's ver formatting version. But it 's , you know  It 's clean , it 's just not fancy.


Yeah , this is like a poor man 's ver formatting version. But it 's , you know  It 's clean , it 's just not fancy.

Well , there 's lots of little things. It 's like there 're twelve different scripts which you run and then at the end you have what you want. But , um , at the very last stage we throw away the actual time information. All we care about is whether  that there 's a certain word was overlapped by someone else 's word. So you sort of  at that point , you discretize things into just having overlap or no overlap. Because we figure that 's about the level of analysis that we want to do for this paper.


Well , there 's lots of little things. It 's like there 're twelve different scripts which you run and then at the end you have what you want. But , um , at the very last stage we throw away the actual time information. All we care about is whether  that there 's a certain word was overlapped by someone else 's word. So you sort of  at that point , you discretize things into just having overlap or no overlap. Because we figure that 's about the level of analysis that we want to do for this paper.


Well , there 's lots of little things. It 's like there 're twelve different scripts which you run and then at the end you have what you want. But , um , at the very last stage we throw away the actual time information. All we care about is whether  that there 's a certain word was overlapped by someone else 's word. So you sort of  at that point , you discretize things into just having overlap or no overlap. Because we figure that 's about the level of analysis that we want to do for this paper.

But if you wanted to do a more fine - grained analysis and say , you know , how far into the word is the overlap , you could do that.


But if you wanted to do a more fine - grained analysis and say , you know , how far into the word is the overlap , you could do that.


What 's interesting is it 's exactly what , um , i in discussing with , um , Sue about this ,


What 's interesting is it 's exactly what , um , i in discussing with , um , Sue about this ,

um , she , um , i i i indicated that that  you know , that 's very important for overlap analysis.


um , she , um , i i i indicated that that  you know , that 's very important for overlap analysis.


um , she , um , i i i indicated that that  you know , that 's very important for overlap analysis.

and also I think as a human , like , I don't always hear these in the actual order that they occur. So I can have two foreground speakers , you know , Morgan an and  um , Adam and Jane could all be talking , and I could align each of them to be starting their utterance at the correct time , and then look where they are relative to each other , and that 's not really what I heard.


and also I think as a human , like , I don't always hear these in the actual order that they occur. So I can have two foreground speakers , you know , Morgan an and  um , Adam and Jane could all be talking , and I could align each of them to be starting their utterance at the correct time , and then look where they are relative to each other , and that 's not really what I heard.


and also I think as a human , like , I don't always hear these in the actual order that they occur. So I can have two foreground speakers , you know , Morgan an and  um , Adam and Jane could all be talking , and I could align each of them to be starting their utterance at the correct time , and then look where they are relative to each other , and that 's not really what I heard.

This is  This is Bever 's  Bever 's effect ,


This is  This is Bever 's  Bever 's effect ,


This is  This is Bever 's  Bever 's effect ,

when  where  In psy ps psycho - linguistics you have these experiments where people have perceptual biases a as to what they hear ,


This is  This is Bever 's  Bever 's effect ,

when  where  In psy ps psycho - linguistics you have these experiments where people have perceptual biases a as to what they hear ,

It 's sort of  Yeah , you sort of move things around until you get to a  low information point


when  where  In psy ps psycho - linguistics you have these experiments where people have perceptual biases a as to what they hear ,

It 's sort of  Yeah , you sort of move things around until you get to a  low information point


when  where  In psy ps psycho - linguistics you have these experiments where people have perceptual biases a as to what they hear ,

It 's sort of  Yeah , you sort of move things around until you get to a  low information point

and yo then you can bring in the other person. So it 's  actually not even possible , I think , for any person to listen to a mixed signal , even equalize , and make sure that they have all the words in the right order. So , I guess , we 'll try to write this Eurospeech paper.


It 's sort of  Yeah , you sort of move things around until you get to a  low information point

and yo then you can bring in the other person. So it 's  actually not even possible , I think , for any person to listen to a mixed signal , even equalize , and make sure that they have all the words in the right order. So , I guess , we 'll try to write this Eurospeech paper.


and yo then you can bring in the other person. So it 's  actually not even possible , I think , for any person to listen to a mixed signal , even equalize , and make sure that they have all the words in the right order. So , I guess , we 'll try to write this Eurospeech paper.

I mean , we will write it. Whether they accept it  late or not , I don't know. Um , and the good thing is that we have  It 's sort of a beginning of what Don can use to link the prosodic features from each file to each other.


I mean , we will write it. Whether they accept it  late or not , I don't know. Um , and the good thing is that we have  It 's sort of a beginning of what Don can use to link the prosodic features from each file to each other.

Yeah. That 's the good thing about these pape


Yeah. That 's the good thing about these pape


We - I ju Otherwise we won't get the work done   on our deadline.


We - I ju Otherwise we won't get the work done   on our deadline.

I mean , u u Jane likes to look at data. Maybe , you know , you could  you could look at this format and see if you find anything interesting.


I mean , u u Jane likes to look at data. Maybe , you know , you could  you could look at this format and see if you find anything interesting.


No , it 's  that 's the good thing about these pape paper deadlines and , uh , you know , class projects , and   and things like that ,


Well th th the other thing that  that  that yo that you usually don't tell your graduate students is that these deadlines are actually not that , um , you know , strictly enforced ,


Oh , now it 's out in the public , this  this  this secret information.


i Because these  the conference organizers actually have an interest in getting lots of submissions.


And good ones , good ones , which sometimes means  a little extra time.


By th by the way , this is totally unfair , you may  you may feel ,


By th by the way , this is totally unfair , you may  you may feel ,

but the  the , uh  the morning meeting folks actually have an  an extra month or so.


but the  the , uh  the morning meeting folks actually have an  an extra month or so.


but the  the , uh  the morning meeting folks actually have an  an extra month or so.

Yep. The Aurora  there 's a special Aurora


Yep. The Aurora  there 's a special Aurora


and the Aurora pe people involved in Aurora have till Ma - uh , early May  or something to turn in their paper.


Oh , well maybe we 'll submit to s   Actually


Oh , well maybe we 'll submit to s   Actually

Well , then you can just  Maybe you can submit the digits paper on e for the Aurora session.


Well , then you can just  Maybe you can submit the digits paper on e for the Aurora session.


It 's  it 's not the Aurora  I mean , it  it 's  it 's actually the Aurora task.


Well , maybe it won't be after this  deadline  extension.


Well , maybe it won't be after this  deadline  extension.

But  but the people  I mean , a  a paper that is not on Aurora would probably be more interesting at that point


Well , maybe it won't be after this  deadline  extension.

But  but the people  I mean , a  a paper that is not on Aurora would probably be more interesting at that point

because everybody 's so sick and tired of the Aurora task.


But  but the people  I mean , a  a paper that is not on Aurora would probably be more interesting at that point

because everybody 's so sick and tired of the Aurora task.


because everybody 's so sick and tired of the Aurora task.

Oh , I thought you meant this was just the digits section. I didn't know you meant it was Aurora digits.


Oh , I thought you meant this was just the digits section. I didn't know you meant it was Aurora digits.

Well , no. If you  if you have  it 's to  if you discuss some relation to the Aurora task , like if you use the same


Oh , I thought you meant this was just the digits section. I didn't know you meant it was Aurora digits.

Well , no. If you  if you have  it 's to  if you discuss some relation to the Aurora task , like if you use the same

This is not the Aurora task. So they just do a little grep for


Well , no. If you  if you have  it 's to  if you discuss some relation to the Aurora task , like if you use the same

This is not the Aurora task. So they just do a little grep for

Do  uh , d d Do not  do not  we are not setting a good example.


Well , no. If you  if you have  it 's to  if you discuss some relation to the Aurora task , like if you use the same

This is not the Aurora task. So they just do a little grep for

Do  uh , d d Do not  do not  we are not setting a good example.

Um. Well , a relation other than negation , maybe ,


This is not the Aurora task. So they just do a little grep for

Do  uh , d d Do not  do not  we are not setting a good example.

Um. Well , a relation other than negation , maybe ,


Do  uh , d d Do not  do not  we are not setting a good example.

Um. Well , a relation other than negation , maybe ,


Um. Well , a relation other than negation , maybe ,


Well , I I don't know. I mean , you could  you could do a paper on  what 's wrong with the Aurora task by comparing it to  other ways of doing it.


Well , I I don't know. I mean , you could  you could do a paper on  what 's wrong with the Aurora task by comparing it to  other ways of doing it.

How well does an Aurora system do on  on  you know , on digits collected in a  in this environment ?


How well does an Aurora system do on  on  you know , on digits collected in a  in this environment ?


I think it 's a littl little far - fetched. Nah , I mean , the thing is Aurora 's pretty closed community.


I think it 's a littl little far - fetched. Nah , I mean , the thing is Aurora 's pretty closed community.

I mean , you know , the people who were involved in the   the only people who are allowed to test on that are people who  who made it above a certain threshold in the first round ,


I mean , you know , the people who were involved in the   the only people who are allowed to test on that are people who  who made it above a certain threshold in the first round ,


I mean , you know , the people who were involved in the   the only people who are allowed to test on that are people who  who made it above a certain threshold in the first round ,

uh  w in ninety - nine and it 's  it 's sort of a  it 's  not like a


uh  w in ninety - nine and it 's  it 's sort of a  it 's  not like a

Well , that 's maybe why they don't f know that they have a crummy system. I mean , a crummy back - end. No , I mean  I mean , seriously , if you  if you have a very  No , I 'm sorry.


Well , that 's maybe why they don't f know that they have a crummy system. I mean , a crummy back - end. No , I mean  I mean , seriously , if you  if you have a very  No , I 'm sorry.

No. I didn't mean anybody  any particular system. I meant this H T K back - end.


No. I didn't mean anybody  any particular system. I meant this H T K back - end.


I don't h I don't have any stock in HTK or Entropic or anything.


I don't h I don't have any stock in HTK or Entropic or anything.

No. I mean , this  it it 's the HTK  that is trained on a very limited amount of data.


No. I mean , this  it it 's the HTK  that is trained on a very limited amount of data.


But so , if you  But maybe you should , you know , consider more  using more data , or  I mean


But so , if you  But maybe you should , you know , consider more  using more data , or  I mean

Oh , yeah. I  I really think that that 's true. And they i i


But so , if you  But maybe you should , you know , consider more  using more data , or  I mean

Oh , yeah. I  I really think that that 's true. And they i i

If yo if you sort of hermetically stay within one task and don't look left and right , then you 're gonna


Oh , yeah. I  I really think that that 's true. And they i i

If yo if you sort of hermetically stay within one task and don't look left and right , then you 're gonna


If yo if you sort of hermetically stay within one task and don't look left and right , then you 're gonna

They had something very specific in mind when they designed it. Right ?


They had something very specific in mind when they designed it. Right ?


They had something very specific in mind when they designed it. Right ?

And so  so you can  you can argue about maybe that wasn't the right thing to do , but , you know , they  they  they had something specific.


And so  so you can  you can argue about maybe that wasn't the right thing to do , but , you know , they  they  they had something specific.

But , one of the reasons I have Chuck 's messing around with  with the back - end that you 're not supposed to touch  I mean , for the evaluations , yes , we 'll run a version that hasn't been touched.


And so  so you can  you can argue about maybe that wasn't the right thing to do , but , you know , they  they  they had something specific.

But , one of the reasons I have Chuck 's messing around with  with the back - end that you 're not supposed to touch  I mean , for the evaluations , yes , we 'll run a version that hasn't been touched.

But , uh , one of the reasons I have him messing around with that , because I think it 's sort of an open question that we don't know the answer to. People always say very glibly  that i if you s show improvement on a bad system , that doesn't mean anything , cuz it may not be   show  uh , because , you know , it doesn't tell you anything about the good system.


But , one of the reasons I have Chuck 's messing around with  with the back - end that you 're not supposed to touch  I mean , for the evaluations , yes , we 'll run a version that hasn't been touched.

But , uh , one of the reasons I have him messing around with that , because I think it 's sort of an open question that we don't know the answer to. People always say very glibly  that i if you s show improvement on a bad system , that doesn't mean anything , cuz it may not be   show  uh , because , you know , it doesn't tell you anything about the good system.


But , uh , one of the reasons I have him messing around with that , because I think it 's sort of an open question that we don't know the answer to. People always say very glibly  that i if you s show improvement on a bad system , that doesn't mean anything , cuz it may not be   show  uh , because , you know , it doesn't tell you anything about the good system.

And I  I 've always sort of felt that that depends. You know , that if some peopl If you 're actually are getting at something that has some  conceptual substance to it , it will port.


And I  I 've always sort of felt that that depends. You know , that if some peopl If you 're actually are getting at something that has some  conceptual substance to it , it will port.

And in fact , most methods that people now use were originally tried with something that was not their absolute  best system at some level. But of course , sometimes it doesn't , uh , port. So I think that 's  that 's an interesting question. If we 're getting  three percent error on , uh , u uh , English , uh , nati native speakers ,  um , using the Aurora system , and we do some improvements and bring it from three to two ,  do those same improvements bring , uh , th you know , the SRI system from one point three to  you know , to  point eight ?


And in fact , most methods that people now use were originally tried with something that was not their absolute  best system at some level. But of course , sometimes it doesn't , uh , port. So I think that 's  that 's an interesting question. If we 're getting  three percent error on , uh , u uh , English , uh , nati native speakers ,  um , using the Aurora system , and we do some improvements and bring it from three to two ,  do those same improvements bring , uh , th you know , the SRI system from one point three to  you know , to  point eight ?


And in fact , most methods that people now use were originally tried with something that was not their absolute  best system at some level. But of course , sometimes it doesn't , uh , port. So I think that 's  that 's an interesting question. If we 're getting  three percent error on , uh , u uh , English , uh , nati native speakers ,  um , using the Aurora system , and we do some improvements and bring it from three to two ,  do those same improvements bring , uh , th you know , the SRI system from one point three to  you know , to  point eight ?

Well. You know , so that 's  that 's something we can test.


Well. You know , so that 's  that 's something we can test.


I think we 've   we 've covered that one up extremely well.


I think we 've   we 've covered that one up extremely well.

OK. So , um  Yeah. So tha so we 'll  you know , maybe you guys 'll have  have one. Uh , you  you and , uh  and Dan have  have a paper that  that 's going in.


OK. So , um  Yeah. So tha so we 'll  you know , maybe you guys 'll have  have one. Uh , you  you and , uh  and Dan have  have a paper that  that 's going in.


OK. So , um  Yeah. So tha so we 'll  you know , maybe you guys 'll have  have one. Uh , you  you and , uh  and Dan have  have a paper that  that 's going in.

You know , that 's  that 's pretty solid , on the segmentation  stuff.


OK. So , um  Yeah. So tha so we 'll  you know , maybe you guys 'll have  have one. Uh , you  you and , uh  and Dan have  have a paper that  that 's going in.

You know , that 's  that 's pretty solid , on the segmentation  stuff.

Yeah. Yeah. I will send you the  the final version ,


You know , that 's  that 's pretty solid , on the segmentation  stuff.

Yeah. Yeah. I will send you the  the final version ,

Yeah. And the Aurora folks here will  will definitely get something in on Aurora ,


Yeah. Yeah. I will send you the  the final version ,

Yeah. And the Aurora folks here will  will definitely get something in on Aurora ,

Actually this  this , um  So , there 's another paper.


Yeah. And the Aurora folks here will  will definitely get something in on Aurora ,

Actually this  this , um  So , there 's another paper.


Actually this  this , um  So , there 's another paper.

It 's a Eurospeech paper but not related to meetings. But it 's on digits. So , um , uh , a colleague at SRI developed a improved version of MMIE training.


It 's a Eurospeech paper but not related to meetings. But it 's on digits. So , um , uh , a colleague at SRI developed a improved version of MMIE training.

And he tested it mostly on digits because it 's sort of a  you know , it doesn't take weeks to train it.


And he tested it mostly on digits because it 's sort of a  you know , it doesn't take weeks to train it.

Um. And got some very impressive results , um , with , you know , discriminative , uh , Gaussian training. Um , you know , like , um , error rates  go from  I don't know , in very noisy environment , like from , uh , uh  I for now I  OK , now I have the order of magnit I 'm not sure about the order of magnitude. Was it like from ten percent to  eight percent or from e e you know , point  you know , from one percent to point eight percent ?


Um. And got some very impressive results , um , with , you know , discriminative , uh , Gaussian training. Um , you know , like , um , error rates  go from  I don't know , in very noisy environment , like from , uh , uh  I for now I  OK , now I have the order of magnit I 'm not sure about the order of magnitude. Was it like from ten percent to  eight percent or from e e you know , point  you know , from one percent to point eight percent ?


It got better. That 's the important thing.


It got better. That 's the important thing.

Hey , that 's the same percent relative ,


Hey , that 's the same percent relative ,


Yeah. Um ,  let 's see. I think the only thing we had left was  unless somebody else  Well , there 's a couple things. Uh , one is  anything that , um ,  anybody has to say about Saturday ? Anything we should do in prep for Saturday ? Um  I guess everybody knows about  I mean , u um , Mari was asking  was trying to come up with something like an agenda and we 're sort of fitting around people 's times a bit. But , um ,  clearly when we actually get here we 'll  move things around this , as we need to , but  so you can't absolutely count on it.


Are we meeting in here probably or  ? OK.


We won we wanna  I mean , they 're  there 's gonna be , uh , Jeff , Katrin , Mari and two students.


It seems like too many  too much coming and going.


Because it would be a different kind of meeting ,


but just , you know , maybe some  I mean ,


Maybe the sections that are not right afte you know , after lunch when everybody 's still munching and


Maybe the sections that are not right afte you know , after lunch when everybody 's still munching and

So can you send out a schedule once you know it , jus ?


So can you send out a schedule once you know it , jus ?

OK. Yeah. I guess I sent it around a little bit.


OK. Yeah. I guess I sent it around a little bit.

There 's a res Is it changed now , or  ?


OK. Yeah. I guess I sent it around a little bit.

There 's a res Is it changed now , or  ?

But  I hadn't heard back from Mari after I  I u u uh , brought up the point abou about Andreas 's schedule. So ,  um , maybe when I get back there 'll be  some  some mail from her.


There 's a res Is it changed now , or  ?

But  I hadn't heard back from Mari after I  I u u uh , brought up the point abou about Andreas 's schedule. So ,  um , maybe when I get back there 'll be  some  some mail from her.


But  I hadn't heard back from Mari after I  I u u uh , brought up the point abou about Andreas 's schedule. So ,  um , maybe when I get back there 'll be  some  some mail from her.

I 'm looking forward to seeing your representation. That 'd be , uh


I 'm looking forward to seeing your representation. That 'd be , uh

And w we should get  the two meetings from y


I 'm looking forward to seeing your representation. That 'd be , uh

And w we should get  the two meetings from y

I mean , I know about the first meeting , um , but the other one that you did , the NSA one , which we  hadn't done cuz we weren't running recognition on it , because the non - native speaker


And w we should get  the two meetings from y

I mean , I know about the first meeting , um , but the other one that you did , the NSA one , which we  hadn't done cuz we weren't running recognition on it , because the non - native speaker


I mean , I know about the first meeting , um , but the other one that you did , the NSA one , which we  hadn't done cuz we weren't running recognition on it , because the non - native speaker


But , it would be useful for the  to see what we get  with that one. So.


But , it would be useful for the  to see what we get  with that one. So.

Great. OK. It 's , uh , two thousand eleven twenty - one one thousand.


But , it would be useful for the  to see what we get  with that one. So.

Great. OK. It 's , uh , two thousand eleven twenty - one one thousand.

Great. I sent email when I finished the  that one.


Great. OK. It 's , uh , two thousand eleven twenty - one one thousand.

Great. I sent email when I finished the  that one.


Great. I sent email when I finished the  that one.

That was sort of son Yeah , that 's right. That 's right. That 's much simpler.


Great. I sent email when I finished the  that one.

That was sort of son Yeah , that 's right. That 's right. That 's much simpler.

I don't know what they said but I know the number.


That was sort of son Yeah , that 's right. That 's right. That 's much simpler.

I don't know what they said but I know the number.

Th - that part 's definitely gonna confuse somebody who looks at these later.


I don't know what they said but I know the number.

Th - that part 's definitely gonna confuse somebody who looks at these later.

I mean , this is  we we 're recording secret NSA meetings ?


Th - that part 's definitely gonna confuse somebody who looks at these later.

I mean , this is  we we 're recording secret NSA meetings ?


I mean , this is  we we 're recording secret NSA meetings ?


It 's network services and applications.


I have no idea what they 're talking about.


I have no idea what they 're talking about.

The , um  th the other good thing about the alignments is that , um , it 's not always the machine 's fault if it doesn't work. So , you can actually find , um ,


The , um  th the other good thing about the alignments is that , um , it 's not always the machine 's fault if it doesn't work. So , you can actually find , um ,


You can find , uh , problems with  with the transcripts , um , you know ,


Tha - There are some cases like where the  the wrong speaker  uh , these ca Not a lot , but where the  the wrong person  the  the speech is addre attached to the wrong speaker


Tha - There are some cases like where the  the wrong speaker  uh , these ca Not a lot , but where the  the wrong person  the  the speech is addre attached to the wrong speaker

and you can tell that when you run it. Or at least you can get  clues to it.


and you can tell that when you run it. Or at least you can get  clues to it.

So these are from the early transcriptions that people did on the mixed signals , like what you have.


and you can tell that when you run it. Or at least you can get  clues to it.

So these are from the early transcriptions that people did on the mixed signals , like what you have.

I guess it does w Mm - hmm. It also raises the possibility of , um , using that kind of representation  I mean , I don't know , this 'd be something we 'd wanna check ,  but maybe using that representation for data entry and then displaying it on the channelized , uh , representation , cuz it  I think that the  I mean , my  my preference in terms of , like , looking at the data is to see it  in this kind of musical score format.


So these are from the early transcriptions that people did on the mixed signals , like what you have.

I guess it does w Mm - hmm. It also raises the possibility of , um , using that kind of representation  I mean , I don't know , this 'd be something we 'd wanna check ,  but maybe using that representation for data entry and then displaying it on the channelized , uh , representation , cuz it  I think that the  I mean , my  my preference in terms of , like , looking at the data is to see it  in this kind of musical score format.


So these are from the early transcriptions that people did on the mixed signals , like what you have.

I guess it does w Mm - hmm. It also raises the possibility of , um , using that kind of representation  I mean , I don't know , this 'd be something we 'd wanna check ,  but maybe using that representation for data entry and then displaying it on the channelized , uh , representation , cuz it  I think that the  I mean , my  my preference in terms of , like , looking at the data is to see it  in this kind of musical score format.

And also , s you know , Sue 's preference as well.


I guess it does w Mm - hmm. It also raises the possibility of , um , using that kind of representation  I mean , I don't know , this 'd be something we 'd wanna check ,  but maybe using that representation for data entry and then displaying it on the channelized , uh , representation , cuz it  I think that the  I mean , my  my preference in terms of , like , looking at the data is to see it  in this kind of musical score format.

And also , s you know , Sue 's preference as well.


And also , s you know , Sue 's preference as well.

And  and  but , I mean , this  if this is a better interface for making these kinds of , uh , you know , lo clos local changes , then that 'd be fine , too. I don't  I have no idea. I think this is something that would need to be checked. Yeah.


And also , s you know , Sue 's preference as well.

And  and  but , I mean , this  if this is a better interface for making these kinds of , uh , you know , lo clos local changes , then that 'd be fine , too. I don't  I have no idea. I think this is something that would need to be checked. Yeah.

OK. Th - the other thing I had actually was , I  I didn't realize this till today , but , uh , this is , uh , Jose 's last day.


And  and  but , I mean , this  if this is a better interface for making these kinds of , uh , you know , lo clos local changes , then that 'd be fine , too. I don't  I have no idea. I think this is something that would need to be checked. Yeah.

OK. Th - the other thing I had actually was , I  I didn't realize this till today , but , uh , this is , uh , Jose 's last day.


OK. Th - the other thing I had actually was , I  I didn't realize this till today , but , uh , this is , uh , Jose 's last day.


Because , eh , I leave , eh , the next Sunday.


And I  I would like to  to  to say thank you very much , eh , to all people  in the group and at ICSI ,


uh. And I 'm sorry by the result of overlapping , because , eh ,  I haven't good results , eh , yet but , eh ,  I   I pretend  to  to continuing out to Spain , eh , during the  the following months ,


uh. And I 'm sorry by the result of overlapping , because , eh ,  I haven't good results , eh , yet but , eh ,  I   I pretend  to  to continuing out to Spain , eh , during the  the following months ,

eh , because I have , eh , another ideas but , eh , I haven't enough time to  to   with six months it 's not enough to   to  to research ,


eh , because I have , eh , another ideas but , eh , I haven't enough time to  to   with six months it 's not enough to   to  to research ,


eh , because I have , eh , another ideas but , eh , I haven't enough time to  to   with six months it 's not enough to   to  to research ,

eh , and e i I mean , if , eh , the topic is , eh , so difficult , uh , in my opinion , there isn't


eh , and e i I mean , if , eh , the topic is , eh , so difficult , uh , in my opinion , there isn't

Yeah. Maybe somebody else will come along and will be , uh , interested in working on it and could start off from where you are also , you know. They 'd make use of  of what you 've done.


Yeah. Maybe somebody else will come along and will be , uh , interested in working on it and could start off from where you are also , you know. They 'd make use of  of what you 've done.

Yeah. But , eh , I  I will try to recommend , eh , at , eh ,  the Spanish government but , eh , the following @ @ scholarship , eh , eh ,  eh , will be here  more time , because eh , i in my opinion is  is better ,  eh , for us  to  to spend more time here and to work more time i i in a topic.


Yeah. But , eh , I  I will try to recommend , eh , at , eh ,  the Spanish government but , eh , the following @ @ scholarship , eh , eh ,  eh , will be here  more time , because eh , i in my opinion is  is better ,  eh , for us  to  to spend more time here and to work more time i i in a topic.


It 's difficult. You  e you have , eh  you are lucky , and you  you find a solution  in  in  in some few tim uh , months , eh ? OK. But , eh , I think it 's not , eh , common. But , eh , anyway , thank you. Thank you very much. Eh , I  I bring the chocolate , eh , to    to tear , uh , with  with you ,


uh. I  I hope if you need , eh , something , eh , from us in the future , I  I will be at Spain ,  to you help , uh.


Yeah. OK. I guess , uh , unless somebody has something else , we 'll read  read our digits


get our last bit of , uh , Jose 's  Jose  Jose 's digit


get our last bit of , uh , Jose 's  Jose  Jose 's digit

Are we gonna do them simultaneously or  ?


Are we gonna do them simultaneously or  ?


Are we gonna do them simultaneously or  ?

Ye - ye you prefer , eh , to eat , eh , chocolate , eh , at the coffee break , eh , at the  ?  Or you prefer now , before after  ?


Ye - ye you prefer , eh , to eat , eh , chocolate , eh , at the coffee break , eh , at the  ?  Or you prefer now , before after  ?


Ye - ye you prefer , eh , to eat , eh , chocolate , eh , at the coffee break , eh , at the  ?  Or you prefer now , before after  ?

No , we prefer to keep it for ourselves.


No , we prefer to keep it for ourselves.

Well , we have a s a time  time constraint.


Well , we have a s a time  time constraint.


Well , we have a s a time  time constraint.

So keep it away from that end of the table.


So keep it away from that end of the table.


Well , we 've gotta wait until after di after we take the mikes off.


Well , we 've gotta wait until after di after we take the mikes off.

So are we gonna do digits simultaneously


Well , we 've gotta wait until after di after we take the mikes off.

So are we gonna do digits simultaneously

You  This is our reward if we  do our digi


So are we gonna do digits simultaneously

You  This is our reward if we  do our digi


You  This is our reward if we  do our digi


I  I think , eh , it 's enough , eh , for more peopl for more people  after.


I  I think , eh , it 's enough , eh , for more peopl for more people  after.

We 're gonna  we 're gonna do digits at the same


We 're gonna  we 're gonna do digits at the same


To Andreas , the idea is  is good.  s To eat here.


Oh , yeah. Th - it doesn't  it won't leave this room.


Oh , yeah. Th - it doesn't  it won't leave this room.

Alright , so in the interest of getting to the


Oh , yeah. Th - it doesn't  it won't leave this room.

Alright , so in the interest of getting to the

We could do digits while other people eat.


Alright , so in the interest of getting to the

We could do digits while other people eat.


We could do digits while other people eat.


No , we don't have any data with background eating.


It 's just the rest of the digits  the rest of the digits are very clean ,


um , without a lot of background noise ,


um , without a lot of background noise ,

And it  You have to write down , like , while y what you 're  what ch chocolate you 're eating


um , without a lot of background noise ,

And it  You have to write down , like , while y what you 're  what ch chocolate you 're eating

cuz they might make different sounds , like n nuts  chocolate with nuts , chocolate without nuts.


And it  You have to write down , like , while y what you 're  what ch chocolate you 're eating

cuz they might make different sounds , like n nuts  chocolate with nuts , chocolate without nuts.


cuz they might make different sounds , like n nuts  chocolate with nuts , chocolate without nuts.


Actually   actually kind of careful cuz I have a strong allergy to nuts , so I have to sort of figure out one without th


Actually   actually kind of careful cuz I have a strong allergy to nuts , so I have to sort of figure out one without th

Maybe those ? They 're so  I don't know.


Maybe those ? They 're so  I don't know.


Maybe those ? They 're so  I don't know.

This is  You know , this is a different kind of speech ,


This is  You know , this is a different kind of speech ,


But if I was  eh , but maybe I 'll get some later. Thanks.


But if I was  eh , but maybe I 'll get some later. Thanks.

Well  well , why don't we  ? He  he 's worried about a ticket. Why don't we do a simultaneous one ?


Well  well , why don't we  ? He  he 's worried about a ticket. Why don't we do a simultaneous one ?


And you laughed at me , too , f the first time I said that.


And you laughed at me , too , f the first time I said that.

Remember to read the transcript number , please.


Remember to read the transcript number , please.


You laughed at me , too , the first time I sa said


You have to sort of , um  Jose , if you haven't done this , you have to plug your ears while you 're t talking


You have to sort of , um  Jose , if you haven't done this , you have to plug your ears while you 're t talking

W wait  wait a minute  wait a minute. W we want  we want


You have to sort of , um  Jose , if you haven't done this , you have to plug your ears while you 're t talking

W wait  wait a minute  wait a minute. W we want  we want

so that you don't get confused , I guess.


W wait  wait a minute  wait a minute. W we want  we want

so that you don't get confused , I guess.

Yeah. Oh , you 've done this one before ?


so that you don't get confused , I guess.

Yeah. Oh , you 've done this one before ?

Hey , you 've done this before. Haven't you ?


Yeah. Oh , you 've done this one before ?

Hey , you 've done this before. Haven't you ?


Hey , you 've done this before. Haven't you ?


You 've read  digits together with us , haven't you  I mean , at the same time ?


You 've read  digits together with us , haven't you  I mean , at the same time ?

I 'm not  we  we  Oh , and you haven't done this either.


I 'm not  we  we  Oh , and you haven't done this either.


so yo you 're supposed to pause between the groupings.


You mean that the  the grouping is supposed to be synchronized ?


We - we 'll give everybody the same sheet


We - we 'll give everybody the same sheet

It 's like a  like a Greek  like a Greek choir ?


It 's like a  like a Greek  like a Greek choir ?


We could do the same sheet for everyone.


And then  then we can sing them next time.


And then  then we can sing them next time.

Uh. OK , why don't we go ? Uh , one two three  Go !


Uh. OK , why don't we go ? Uh , one two three  Go !


He 's try No , he 's trying to get good recognition performance.


And uh Hans - uh , Hans - Guenter will be here , um , I think by next  next Tuesday or so.


And uh Hans - uh , Hans - Guenter will be here , um , I think by next  next Tuesday or so.

So he 's  he 's going to be here for about three weeks ,


So he 's  he 's going to be here for about three weeks ,


We might  might end up with some longer collaboration or something.


We might  might end up with some longer collaboration or something.

So he 's gonna look in on everything we 're doing


So he 's gonna look in on everything we 're doing

and give us his  his thoughts. And so it 'll be another  another good person looking at things.


and give us his  his thoughts. And so it 'll be another  another good person looking at things.

Th - that 's his spectral subtraction group ?


Th - that 's his spectral subtraction group ?


Oh , OK. So I guess I should probably talk to him a bit too ?


Oh , OK. So I guess I should probably talk to him a bit too ?

Oh , yeah. Yeah. Yeah. No , he 'll be around for three weeks. He 's , uh , um , very , very , easygoing , easy to talk to , and , uh , very interested in everything.


Oh , yeah. Yeah. Yeah. No , he 'll be around for three weeks. He 's , uh , um , very , very , easygoing , easy to talk to , and , uh , very interested in everything.


Wh - Back when I was a grad student he was here for a , uh , uh  a year or  n six months.


Yeah. Yeah. He 's  he 's done a couple stays here.


Yeah. Yeah. He 's  he 's done a couple stays here.

So , um ,   I guess we got lots to catch up on. And we haven't met for a couple of weeks. We didn't meet last week , Morgan. Um , I went around and talked to everybody , and it seemed like they  they had some new results but rather than them coming up and telling me I figured we should just wait a week and they can tell both  you know , all of us. So , um , why don't we  why don't we start with you , Dave , and then , um , we can go on.


So , um ,   I guess we got lots to catch up on. And we haven't met for a couple of weeks. We didn't meet last week , Morgan. Um , I went around and talked to everybody , and it seemed like they  they had some new results but rather than them coming up and telling me I figured we should just wait a week and they can tell both  you know , all of us. So , um , why don't we  why don't we start with you , Dave , and then , um , we can go on.


So , um ,   I guess we got lots to catch up on. And we haven't met for a couple of weeks. We didn't meet last week , Morgan. Um , I went around and talked to everybody , and it seemed like they  they had some new results but rather than them coming up and telling me I figured we should just wait a week and they can tell both  you know , all of us. So , um , why don't we  why don't we start with you , Dave , and then , um , we can go on.

So , um , since we 're looking at putting this , um  mean log m magnitude spectral subtraction , um , into the SmartKom system , I I did a test seeing if , um , it would work using past only  and plus the present to calculate the mean. So , I did a test , um ,  where I used twelve seconds from the past and the present frame to , um , calculate the mean. And


So , um , since we 're looking at putting this , um  mean log m magnitude spectral subtraction , um , into the SmartKom system , I I did a test seeing if , um , it would work using past only  and plus the present to calculate the mean. So , I did a test , um ,  where I used twelve seconds from the past and the present frame to , um , calculate the mean. And

Twelve seconds  Twelve  twelve seconds back from the current  frame , is that what you mean ?


So , um , since we 're looking at putting this , um  mean log m magnitude spectral subtraction , um , into the SmartKom system , I I did a test seeing if , um , it would work using past only  and plus the present to calculate the mean. So , I did a test , um ,  where I used twelve seconds from the past and the present frame to , um , calculate the mean. And

Twelve seconds  Twelve  twelve seconds back from the current  frame , is that what you mean ?

Uh  Twelve seconds , um , counting back from the end of the current frame ,


Twelve seconds  Twelve  twelve seconds back from the current  frame , is that what you mean ?

Uh  Twelve seconds , um , counting back from the end of the current frame ,

yeah. So it was , um , twen I think it was twenty - one frames and that worked out to about twelve seconds.


Uh  Twelve seconds , um , counting back from the end of the current frame ,

yeah. So it was , um , twen I think it was twenty - one frames and that worked out to about twelve seconds.


yeah. So it was , um , twen I think it was twenty - one frames and that worked out to about twelve seconds.

And compared to , um , do using a twelve second centered window , I think there was a drop in performance but it was just a slight drop.


And compared to , um , do using a twelve second centered window , I think there was a drop in performance but it was just a slight drop.


Um , yeah , I mean , it was pretty  it was pretty tiny. Yeah.


Um , yeah , I mean , it was pretty  it was pretty tiny. Yeah.

Uh - huh. So that was encouraging. And , um , that  that  um , that 's encouraging for  for the idea of using it in an interactive system like And , um , another issue I 'm  I 'm thinking about is in the SmartKom system. So say twe twelve seconds in the earlier test seemed like a good length of time , but what happens if you have less than twelve seconds ? And , um  So I w bef before , um  Back in May , I did some experiments using , say , two seconds , or four seconds , or six seconds. In those I trained the models using mean subtraction with the means calculated over two seconds , or four seconds , or six seconds. And , um , here , I was curious , what if I trained the models using twelve seconds but I f I gave it a situation where the test set I was  subtracted using two seconds , or four seconds , or six seconds. And , um  So I did that for about three different conditions. And , um  I mean , I th I think it was , um , four se I think  I think it was , um , something like four seconds and , um , six seconds , and eight seconds. Something like that. And it seems like it  it  it hurts compared to if you actually train the models  using th that same length of time but it  it doesn't hurt that much. Um , u usually less than point five percent , although I think I did see one where it was a point eight percent or so rise in word error rate. But this is , um , w where , um , even if I train on the , uh , model , and mean subtracted it with the same length of time as in the test , it  the word error rate is around , um , ten percent or nine percent. So it doesn't seem like that big a d a difference.


Um , yeah , I mean , it was pretty  it was pretty tiny. Yeah.

Uh - huh. So that was encouraging. And , um , that  that  um , that 's encouraging for  for the idea of using it in an interactive system like And , um , another issue I 'm  I 'm thinking about is in the SmartKom system. So say twe twelve seconds in the earlier test seemed like a good length of time , but what happens if you have less than twelve seconds ? And , um  So I w bef before , um  Back in May , I did some experiments using , say , two seconds , or four seconds , or six seconds. In those I trained the models using mean subtraction with the means calculated over two seconds , or four seconds , or six seconds. And , um , here , I was curious , what if I trained the models using twelve seconds but I f I gave it a situation where the test set I was  subtracted using two seconds , or four seconds , or six seconds. And , um  So I did that for about three different conditions. And , um  I mean , I th I think it was , um , four se I think  I think it was , um , something like four seconds and , um , six seconds , and eight seconds. Something like that. And it seems like it  it  it hurts compared to if you actually train the models  using th that same length of time but it  it doesn't hurt that much. Um , u usually less than point five percent , although I think I did see one where it was a point eight percent or so rise in word error rate. But this is , um , w where , um , even if I train on the , uh , model , and mean subtracted it with the same length of time as in the test , it  the word error rate is around , um , ten percent or nine percent. So it doesn't seem like that big a d a difference.

But it  but looking at it the other way , isn't it  what you 're saying that it didn't help you to have the longer time for training , if you were going to have a short time for


Uh - huh. So that was encouraging. And , um , that  that  um , that 's encouraging for  for the idea of using it in an interactive system like And , um , another issue I 'm  I 'm thinking about is in the SmartKom system. So say twe twelve seconds in the earlier test seemed like a good length of time , but what happens if you have less than twelve seconds ? And , um  So I w bef before , um  Back in May , I did some experiments using , say , two seconds , or four seconds , or six seconds. In those I trained the models using mean subtraction with the means calculated over two seconds , or four seconds , or six seconds. And , um , here , I was curious , what if I trained the models using twelve seconds but I f I gave it a situation where the test set I was  subtracted using two seconds , or four seconds , or six seconds. And , um  So I did that for about three different conditions. And , um  I mean , I th I think it was , um , four se I think  I think it was , um , something like four seconds and , um , six seconds , and eight seconds. Something like that. And it seems like it  it  it hurts compared to if you actually train the models  using th that same length of time but it  it doesn't hurt that much. Um , u usually less than point five percent , although I think I did see one where it was a point eight percent or so rise in word error rate. But this is , um , w where , um , even if I train on the , uh , model , and mean subtracted it with the same length of time as in the test , it  the word error rate is around , um , ten percent or nine percent. So it doesn't seem like that big a d a difference.

But it  but looking at it the other way , isn't it  what you 're saying that it didn't help you to have the longer time for training , if you were going to have a short time for

I mean , why would you do it , if you knew that you were going to have short windows in testing.


But it  but looking at it the other way , isn't it  what you 're saying that it didn't help you to have the longer time for training , if you were going to have a short time for

I mean , why would you do it , if you knew that you were going to have short windows in testing.


I mean , why would you do it , if you knew that you were going to have short windows in testing.

Yeah , it seems like for your  I mean , in normal situations you would never get twelve seconds of speech , right ? I 'm not  e u


I mean , why would you do it , if you knew that you were going to have short windows in testing.

Yeah , it seems like for your  I mean , in normal situations you would never get twelve seconds of speech , right ? I 'm not  e u

You need twelve seconds in the past to estimate , right ? Or l or you 're looking at six sec  seconds in future and six in


Yeah , it seems like for your  I mean , in normal situations you would never get twelve seconds of speech , right ? I 'm not  e u

You need twelve seconds in the past to estimate , right ? Or l or you 're looking at six sec  seconds in future and six in


You need twelve seconds in the past to estimate , right ? Or l or you 're looking at six sec  seconds in future and six in


N n uh  For the test it 's just twelve seconds in the past.


N n uh  For the test it 's just twelve seconds in the past.

Is this twelve seconds of  uh , regardless of speech or silence ? Or twelve seconds of speech ?


Is this twelve seconds of  uh , regardless of speech or silence ? Or twelve seconds of speech ?


The other thing , um , which maybe relates a little bit to something else we 've talked about in terms of windowing and so on is , that , um , I wonder if you trained with twelve seconds , and then when you were two seconds in you used two seconds , and when you were four seconds in , you used four seconds , and when you were six  and you basically build up to the twelve seconds. So that if you have very long utterances you have the best ,


The other thing , um , which maybe relates a little bit to something else we 've talked about in terms of windowing and so on is , that , um , I wonder if you trained with twelve seconds , and then when you were two seconds in you used two seconds , and when you were four seconds in , you used four seconds , and when you were six  and you basically build up to the twelve seconds. So that if you have very long utterances you have the best ,

but if you have shorter utterances you use what you can.


The other thing , um , which maybe relates a little bit to something else we 've talked about in terms of windowing and so on is , that , um , I wonder if you trained with twelve seconds , and then when you were two seconds in you used two seconds , and when you were four seconds in , you used four seconds , and when you were six  and you basically build up to the twelve seconds. So that if you have very long utterances you have the best ,

but if you have shorter utterances you use what you can.

Right. And that 's actually what we 're planning to do in


but if you have shorter utterances you use what you can.

Right. And that 's actually what we 're planning to do in


but if you have shorter utterances you use what you can.

Right. And that 's actually what we 're planning to do in

But  s so I g So I guess the que the question I was trying to get at with those experiments is , " does it matter what models you use ? Does it matter how much time y you use to calculate the mean when you were , um , tra doing the training data ? "


Right. And that 's actually what we 're planning to do in

But  s so I g So I guess the que the question I was trying to get at with those experiments is , " does it matter what models you use ? Does it matter how much time y you use to calculate the mean when you were , um , tra doing the training data ? "

Right. But I mean the other thing is that that 's  I mean , the other way of looking at this , going back to , uh , mean cepstral subtraction versus RASTA kind of things , is that you could look at mean cepstral subtraction , especially the way you 're doing it , uh , as being a kind of filter. And so , the other thing is just to design a filter. You know , basically you 're  you 're  you 're doing a high - pass filter or a band - pass filter of some sort and  and just design a filter. And then , you know , a filter will have a certain behavior and you loo can look at the start up behavior when you start up with nothing.


But  s so I g So I guess the que the question I was trying to get at with those experiments is , " does it matter what models you use ? Does it matter how much time y you use to calculate the mean when you were , um , tra doing the training data ? "

Right. But I mean the other thing is that that 's  I mean , the other way of looking at this , going back to , uh , mean cepstral subtraction versus RASTA kind of things , is that you could look at mean cepstral subtraction , especially the way you 're doing it , uh , as being a kind of filter. And so , the other thing is just to design a filter. You know , basically you 're  you 're  you 're doing a high - pass filter or a band - pass filter of some sort and  and just design a filter. And then , you know , a filter will have a certain behavior and you loo can look at the start up behavior when you start up with nothing.


But  s so I g So I guess the que the question I was trying to get at with those experiments is , " does it matter what models you use ? Does it matter how much time y you use to calculate the mean when you were , um , tra doing the training data ? "

Right. But I mean the other thing is that that 's  I mean , the other way of looking at this , going back to , uh , mean cepstral subtraction versus RASTA kind of things , is that you could look at mean cepstral subtraction , especially the way you 're doing it , uh , as being a kind of filter. And so , the other thing is just to design a filter. You know , basically you 're  you 're  you 're doing a high - pass filter or a band - pass filter of some sort and  and just design a filter. And then , you know , a filter will have a certain behavior and you loo can look at the start up behavior when you start up with nothing.

And  and , you know , it will , uh , if you have an IIR filter for instance , it will , um , uh , not behave in the steady - state way that you would like it to behave until you get a long enough period , but , um , uh , by just constraining yourself to have your filter be only a subtraction of the mean , you 're kind of , you know , tying your hands behind your back because there 's  filters have all sorts of be temporal and spectral behaviors.


Right. But I mean the other thing is that that 's  I mean , the other way of looking at this , going back to , uh , mean cepstral subtraction versus RASTA kind of things , is that you could look at mean cepstral subtraction , especially the way you 're doing it , uh , as being a kind of filter. And so , the other thing is just to design a filter. You know , basically you 're  you 're  you 're doing a high - pass filter or a band - pass filter of some sort and  and just design a filter. And then , you know , a filter will have a certain behavior and you loo can look at the start up behavior when you start up with nothing.

And  and , you know , it will , uh , if you have an IIR filter for instance , it will , um , uh , not behave in the steady - state way that you would like it to behave until you get a long enough period , but , um , uh , by just constraining yourself to have your filter be only a subtraction of the mean , you 're kind of , you know , tying your hands behind your back because there 's  filters have all sorts of be temporal and spectral behaviors.


And  and , you know , it will , uh , if you have an IIR filter for instance , it will , um , uh , not behave in the steady - state way that you would like it to behave until you get a long enough period , but , um , uh , by just constraining yourself to have your filter be only a subtraction of the mean , you 're kind of , you know , tying your hands behind your back because there 's  filters have all sorts of be temporal and spectral behaviors.

And the only thing , you know , consistent that we know about is that you want to get rid of the very low frequency component.


And the only thing , you know , consistent that we know about is that you want to get rid of the very low frequency component.

But do you really want to calculate the mean ? And you neglect all the silence regions  or you just use everything that 's twelve seconds , and


And the only thing , you know , consistent that we know about is that you want to get rid of the very low frequency component.

But do you really want to calculate the mean ? And you neglect all the silence regions  or you just use everything that 's twelve seconds , and

Um , you  do you mean in my tests so far ?


But do you really want to calculate the mean ? And you neglect all the silence regions  or you just use everything that 's twelve seconds , and

Um , you  do you mean in my tests so far ?


Um , you  do you mean in my tests so far ?


Just  There 's just inter - word silences.


Just  There 's just inter - word silences.

Mm - hmm. And they are , like , pretty short. Shor


Mm - hmm. And they are , like , pretty short. Shor


Yeah. Mm - hmm. So you really need a lot of speech to estimate the mean of it.


Yeah. Mm - hmm. So you really need a lot of speech to estimate the mean of it.

Well , if I only use six seconds , it still works pretty well.


Yeah. Mm - hmm. So you really need a lot of speech to estimate the mean of it.

Well , if I only use six seconds , it still works pretty well.

I saw in my test before. I was trying twelve seconds cuz that was the best  in my test before


Well , if I only use six seconds , it still works pretty well.

I saw in my test before. I was trying twelve seconds cuz that was the best  in my test before


I saw in my test before. I was trying twelve seconds cuz that was the best  in my test before

and that increasing past twelve seconds didn't seem to help.


and that increasing past twelve seconds didn't seem to help.

th um , yeah , I guess it 's something I need to play with more to decide how to set that up for the SmartKom system. Like , may maybe if I trained on six seconds it would work better when I only had two seconds or four seconds , and


th um , yeah , I guess it 's something I need to play with more to decide how to set that up for the SmartKom system. Like , may maybe if I trained on six seconds it would work better when I only had two seconds or four seconds , and


th um , yeah , I guess it 's something I need to play with more to decide how to set that up for the SmartKom system. Like , may maybe if I trained on six seconds it would work better when I only had two seconds or four seconds , and

Yeah , and again , if you take this filtering perspective and if you essentially have it build up over time. I mean , if you computed means over two and then over four , and over six , essentially what you 're getting at is a kind of , uh , ramp up of a filter anyway. And so you may  may just want to think of it as a filter. But , uh , if you do that , then , um , in practice somebody using the SmartKom system , one would think   if they 're using it for a while , it means that their first utterance , instead of , you know , getting , uh , a forty percent error rate reduction , they 'll get a  uh , over what , uh , you 'd get without this , uh , um , policy , uh , you get thirty percent. And then the second utterance that you give , they get the full  you know , uh , full benefit of it if it 's this ongoing thing.


Yeah , and again , if you take this filtering perspective and if you essentially have it build up over time. I mean , if you computed means over two and then over four , and over six , essentially what you 're getting at is a kind of , uh , ramp up of a filter anyway. And so you may  may just want to think of it as a filter. But , uh , if you do that , then , um , in practice somebody using the SmartKom system , one would think   if they 're using it for a while , it means that their first utterance , instead of , you know , getting , uh , a forty percent error rate reduction , they 'll get a  uh , over what , uh , you 'd get without this , uh , um , policy , uh , you get thirty percent. And then the second utterance that you give , they get the full  you know , uh , full benefit of it if it 's this ongoing thing.

Oh , so you  you cache the utterances ? That 's how you get your , uh


Oh , so you  you cache the utterances ? That 's how you get your , uh

that 's  If somebody 's using a system to ask for directions or something ,


that 's  If somebody 's using a system to ask for directions or something ,


that 's  If somebody 's using a system to ask for directions or something ,

you know , they 'll say something first. And  and to begin with if it doesn't get them quite right , ma m maybe they 'll come back and say , " excuse me ? "


you know , they 'll say something first. And  and to begin with if it doesn't get them quite right , ma m maybe they 'll come back and say , " excuse me ? "

uh , or some  I mean it should have some policy like that anyway.


uh , or some  I mean it should have some policy like that anyway.

And  and , uh , uh , in any event they might ask a second question. And it 's not like what he 's doing doesn't , uh , improve things. It does improve things , just not as much as he would like. And so , uh , there 's a higher probability of it making an error , uh , in the first utterance.


uh , or some  I mean it should have some policy like that anyway.

And  and , uh , uh , in any event they might ask a second question. And it 's not like what he 's doing doesn't , uh , improve things. It does improve things , just not as much as he would like. And so , uh , there 's a higher probability of it making an error , uh , in the first utterance.

What would be really cool is if you could have  uh , this probably  users would never like this  but if you had  could have a system where ,  before they began to use it they had to introduce themselves , verbally.


And  and , uh , uh , in any event they might ask a second question. And it 's not like what he 's doing doesn't , uh , improve things. It does improve things , just not as much as he would like. And so , uh , there 's a higher probability of it making an error , uh , in the first utterance.

What would be really cool is if you could have  uh , this probably  users would never like this  but if you had  could have a system where ,  before they began to use it they had to introduce themselves , verbally.


And  and , uh , uh , in any event they might ask a second question. And it 's not like what he 's doing doesn't , uh , improve things. It does improve things , just not as much as he would like. And so , uh , there 's a higher probability of it making an error , uh , in the first utterance.

What would be really cool is if you could have  uh , this probably  users would never like this  but if you had  could have a system where ,  before they began to use it they had to introduce themselves , verbally.

You know. " Hi , my name is so - and - so ,


What would be really cool is if you could have  uh , this probably  users would never like this  but if you had  could have a system where ,  before they began to use it they had to introduce themselves , verbally.

You know. " Hi , my name is so - and - so ,


You know. " Hi , my name is so - and - so ,

I 'm from blah - blah - blah. " And you could use that initial speech to do all these adaptations and


I 'm from blah - blah - blah. " And you could use that initial speech to do all these adaptations and


I 'm from blah - blah - blah. " And you could use that initial speech to do all these adaptations and

Oh , the other thing I guess which  which , uh , I don't know much about  as much as I should about the rest of the system but  but , um , couldn't you , uh , if you  if you sort of did a first pass I don't know what kind of , uh , uh , capability we have at the moment for  for doing second passes on  on , uh , uh , some kind of little  small lattice , or a graph , or confusion network , or something. But if you did first pass with , um , the  with  either without the mean sub subtraction or with a  a very short time one , and then , um , once you , uh , actually had the whole utterance in , if you did , um , the , uh , uh , longer time version then , based on everything that you had , um , and then at that point only used it to distinguish between , you know , top N , um , possible utterances or something , you  you might  it might not take very much time. I mean , I know in the large vocabulary stu uh , uh , systems , people were evaluating on in the past , some people really pushed everything in to make it in one pass but other people didn't and had multiple passes. And , um , the argument , um , against multiple passes was u u has often been " but we want to this to be r you know  have a nice interactive response ". And the counterargument to that which , say , uh , BBN I think had ,  was " yeah , but our second responses are  second , uh , passes and third passes are really , really fast ".


Oh , the other thing I guess which  which , uh , I don't know much about  as much as I should about the rest of the system but  but , um , couldn't you , uh , if you  if you sort of did a first pass I don't know what kind of , uh , uh , capability we have at the moment for  for doing second passes on  on , uh , uh , some kind of little  small lattice , or a graph , or confusion network , or something. But if you did first pass with , um , the  with  either without the mean sub subtraction or with a  a very short time one , and then , um , once you , uh , actually had the whole utterance in , if you did , um , the , uh , uh , longer time version then , based on everything that you had , um , and then at that point only used it to distinguish between , you know , top N , um , possible utterances or something , you  you might  it might not take very much time. I mean , I know in the large vocabulary stu uh , uh , systems , people were evaluating on in the past , some people really pushed everything in to make it in one pass but other people didn't and had multiple passes. And , um , the argument , um , against multiple passes was u u has often been " but we want to this to be r you know  have a nice interactive response ". And the counterargument to that which , say , uh , BBN I think had ,  was " yeah , but our second responses are  second , uh , passes and third passes are really , really fast ".


Oh , the other thing I guess which  which , uh , I don't know much about  as much as I should about the rest of the system but  but , um , couldn't you , uh , if you  if you sort of did a first pass I don't know what kind of , uh , uh , capability we have at the moment for  for doing second passes on  on , uh , uh , some kind of little  small lattice , or a graph , or confusion network , or something. But if you did first pass with , um , the  with  either without the mean sub subtraction or with a  a very short time one , and then , um , once you , uh , actually had the whole utterance in , if you did , um , the , uh , uh , longer time version then , based on everything that you had , um , and then at that point only used it to distinguish between , you know , top N , um , possible utterances or something , you  you might  it might not take very much time. I mean , I know in the large vocabulary stu uh , uh , systems , people were evaluating on in the past , some people really pushed everything in to make it in one pass but other people didn't and had multiple passes. And , um , the argument , um , against multiple passes was u u has often been " but we want to this to be r you know  have a nice interactive response ". And the counterargument to that which , say , uh , BBN I think had ,  was " yeah , but our second responses are  second , uh , passes and third passes are really , really fast ".

So , um , if  if your second pass takes a millisecond who cares ? Um.


Oh , the other thing I guess which  which , uh , I don't know much about  as much as I should about the rest of the system but  but , um , couldn't you , uh , if you  if you sort of did a first pass I don't know what kind of , uh , uh , capability we have at the moment for  for doing second passes on  on , uh , uh , some kind of little  small lattice , or a graph , or confusion network , or something. But if you did first pass with , um , the  with  either without the mean sub subtraction or with a  a very short time one , and then , um , once you , uh , actually had the whole utterance in , if you did , um , the , uh , uh , longer time version then , based on everything that you had , um , and then at that point only used it to distinguish between , you know , top N , um , possible utterances or something , you  you might  it might not take very much time. I mean , I know in the large vocabulary stu uh , uh , systems , people were evaluating on in the past , some people really pushed everything in to make it in one pass but other people didn't and had multiple passes. And , um , the argument , um , against multiple passes was u u has often been " but we want to this to be r you know  have a nice interactive response ". And the counterargument to that which , say , uh , BBN I think had ,  was " yeah , but our second responses are  second , uh , passes and third passes are really , really fast ".

So , um , if  if your second pass takes a millisecond who cares ? Um.

S so , um , the  the idea of the second pass would be waiting till you have more recorded speech ? Or  ?


So , um , if  if your second pass takes a millisecond who cares ? Um.

S so , um , the  the idea of the second pass would be waiting till you have more recorded speech ? Or  ?

Yeah , so if it turned out to be a problem , that you didn't have enough speech because you need a longer  longer window to do this processing , then , uh , one tactic is  you know , looking at the larger system and not just at the front - end stuff   is to take in , um , the speech with some simpler mechanism or shorter time mechanism ,


S so , um , the  the idea of the second pass would be waiting till you have more recorded speech ? Or  ?

Yeah , so if it turned out to be a problem , that you didn't have enough speech because you need a longer  longer window to do this processing , then , uh , one tactic is  you know , looking at the larger system and not just at the front - end stuff   is to take in , um , the speech with some simpler mechanism or shorter time mechanism ,

um , do the best you can , and come up with some al possible alternates of what might have been said. And , uh , either in the form of an N - best list or in the form of a lattice , or  or confusion network , or whatever.


Yeah , so if it turned out to be a problem , that you didn't have enough speech because you need a longer  longer window to do this processing , then , uh , one tactic is  you know , looking at the larger system and not just at the front - end stuff   is to take in , um , the speech with some simpler mechanism or shorter time mechanism ,

um , do the best you can , and come up with some al possible alternates of what might have been said. And , uh , either in the form of an N - best list or in the form of a lattice , or  or confusion network , or whatever.


um , do the best you can , and come up with some al possible alternates of what might have been said. And , uh , either in the form of an N - best list or in the form of a lattice , or  or confusion network , or whatever.

And then the decoding of that is much , much faster or can be much , much faster if it isn't a big bushy network. And you can decode that now with speech that you 've actually processed using this longer time , uh , subtraction.


And then the decoding of that is much , much faster or can be much , much faster if it isn't a big bushy network. And you can decode that now with speech that you 've actually processed using this longer time , uh , subtraction.

So I mean , it 's  it 's common that people do this sort of thing where they do more things that are more complex or require looking over more time , whatever , in some kind of second pass.


So I mean , it 's  it 's common that people do this sort of thing where they do more things that are more complex or require looking over more time , whatever , in some kind of second pass.

um , and again , if the second pass is really , really fast  Uh , another one I 've heard of is  is in  in connected digit stuff , um , going back and l and through backtrace and finding regions that are considered to be a d a digit , but , uh , which have very low energy.


um , and again , if the second pass is really , really fast  Uh , another one I 've heard of is  is in  in connected digit stuff , um , going back and l and through backtrace and finding regions that are considered to be a d a digit , but , uh , which have very low energy.

So , uh  I mean , there 's lots of things you can do in second passes , at all sorts of levels. Anyway , I 'm throwing too many things out. But.


So , uh  I mean , there 's lots of things you can do in second passes , at all sorts of levels. Anyway , I 'm throwing too many things out. But.


Yep. Um , so , the last two weeks was , like  So I 've been working on that Wiener filtering. And , uh , found that , uh , s single  like , I just do a s normal Wiener filtering , like the standard method of Wiener filtering. And that doesn't actually give me any improvement over like  I mean , uh , b it actually improves over the baseline but it 's not like  it doesn't meet something like fifty percent or something. So , I 've been playing with the v


Yep. Um , so , the last two weeks was , like  So I 've been working on that Wiener filtering. And , uh , found that , uh , s single  like , I just do a s normal Wiener filtering , like the standard method of Wiener filtering. And that doesn't actually give me any improvement over like  I mean , uh , b it actually improves over the baseline but it 's not like  it doesn't meet something like fifty percent or something. So , I 've been playing with the v

Improves over the base line MFCC system ? Yeah.


Yep. Um , so , the last two weeks was , like  So I 've been working on that Wiener filtering. And , uh , found that , uh , s single  like , I just do a s normal Wiener filtering , like the standard method of Wiener filtering. And that doesn't actually give me any improvement over like  I mean , uh , b it actually improves over the baseline but it 's not like  it doesn't meet something like fifty percent or something. So , I 've been playing with the v

Improves over the base line MFCC system ? Yeah.

Yeah. Yeah. Yeah. So , um  So that 's  The improvement is somewhere around , like , thirty percent over the baseline.


Yep. Um , so , the last two weeks was , like  So I 've been working on that Wiener filtering. And , uh , found that , uh , s single  like , I just do a s normal Wiener filtering , like the standard method of Wiener filtering. And that doesn't actually give me any improvement over like  I mean , uh , b it actually improves over the baseline but it 's not like  it doesn't meet something like fifty percent or something. So , I 've been playing with the v

Improves over the base line MFCC system ? Yeah.

Yeah. Yeah. Yeah. So , um  So that 's  The improvement is somewhere around , like , thirty percent over the baseline.

Is that using  in combination with something else ?


Improves over the base line MFCC system ? Yeah.

Yeah. Yeah. Yeah. So , um  So that 's  The improvement is somewhere around , like , thirty percent over the baseline.

Is that using  in combination with something else ?


Yeah. Yeah. Yeah. So , um  So that 's  The improvement is somewhere around , like , thirty percent over the baseline.

Is that using  in combination with something else ?


Is that using  in combination with something else ?


No , no , but I mean in combination with our on - line normalization or with the LDA ?


No , no , but I mean in combination with our on - line normalization or with the LDA ?

Yeah , yeah , yeah , yeah. So I just plug in the Wiener filtering.


Yeah , yeah , yeah , yeah. So I just plug in the Wiener filtering.


So , does it g does that mean it gets worse ? Or  ?


So , does it g does that mean it gets worse ? Or  ?

No. It actually improves over the baseline of not having a Wiener filter in the whole system. Like I have an LDA f LDA plus on - line normalization , and then I plug in the Wiener filter in that ,