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14
I mean there 's a bunch of things in this note to various people
uh Broadcast News.
especially I guess um with Jane that that would help for since we have this new data now uh in order to go from the transcripts more easily to um just the words that the recognizer would use for scoring. I had to deal with some of it by hand but I think a lot of it can be automated s by
Oh one thing I guess I didn't get so you know the language model was straight from from bigram from Switchboard the acoustic models were also from Switchboard or or
false
QMSum_225
uh Broadcast News.
especially I guess um with Jane that that would help for since we have this new data now uh in order to go from the transcripts more easily to um just the words that the recognizer would use for scoring. I had to deal with some of it by hand but I think a lot of it can be automated s by
Oh one thing I guess I didn't get so you know the language model was straight from from bigram from Switchboard the acoustic models were also from Switchboard or or
Yeah.
false
QMSum_225
especially I guess um with Jane that that would help for since we have this new data now uh in order to go from the transcripts more easily to um just the words that the recognizer would use for scoring. I had to deal with some of it by hand but I think a lot of it can be automated s by
Oh one thing I guess I didn't get so you know the language model was straight from from bigram from Switchboard the acoustic models were also from Switchboard or or
Yeah.
Yeah.
false
QMSum_225
Oh one thing I guess I didn't get so you know the language model was straight from from bigram from Switchboard the acoustic models were also from Switchboard or or
Yeah.
Yeah.
So they didn't have anything from this acoustic data in yet ?
false
QMSum_225
Yeah.
Yeah.
So they didn't have anything from this acoustic data in yet ?
That 's amazing.
false
QMSum_225
Yeah.
So they didn't have anything from this acoustic data in yet ?
That 's amazing.
Yeah , so that 's great.
false
QMSum_225
So they didn't have anything from this acoustic data in yet ?
That 's amazing.
Yeah , so that 's great.
No.
false
QMSum_225
That 's amazing.
Yeah , so that 's great.
No.
OK.
false
QMSum_225
Yeah , so that 's great.
No.
OK.
And actually we actually um used Switchboard telephone bandwidth models
false
QMSum_225
No.
OK.
And actually we actually um used Switchboard telephone bandwidth models
That 's amazing.
false
QMSum_225
OK.
And actually we actually um used Switchboard telephone bandwidth models
That 's amazing.
Well that 's those are the only we ones there are ,
false
QMSum_225
And actually we actually um used Switchboard telephone bandwidth models
That 's amazing.
Well that 's those are the only we ones there are ,
Yeah.
false
QMSum_225
That 's amazing.
Well that 's those are the only we ones there are ,
Yeah.
which I guess
false
QMSum_225
Well that 's those are the only we ones there are ,
Yeah.
which I guess
I was just gonna say ,
false
QMSum_225
Yeah.
which I guess
I was just gonna say ,
so that 's the on that 's the only acoustic training data that we have a lot of
false
QMSum_225
which I guess
I was just gonna say ,
so that 's the on that 's the only acoustic training data that we have a lot of
yeah.
false
QMSum_225
I was just gonna say ,
so that 's the on that 's the only acoustic training data that we have a lot of
yeah.
I mean
false
QMSum_225
so that 's the on that 's the only acoustic training data that we have a lot of
yeah.
I mean
Yeah.
false
QMSum_225
yeah.
I mean
Yeah.
Right.
false
QMSum_225
I mean
Yeah.
Right.
and I guess Ramana , so a guy at SRI said that um there 's not a huge amount of difference going from
false
QMSum_225
Yeah.
Right.
and I guess Ramana , so a guy at SRI said that um there 's not a huge amount of difference going from
Right.
false
QMSum_225
Right.
and I guess Ramana , so a guy at SRI said that um there 's not a huge amount of difference going from
Right.
it 's it 's not like we probably lose a huge amount but we won't know because we don't have any full band models for s conversational speech.
false
QMSum_225
and I guess Ramana , so a guy at SRI said that um there 's not a huge amount of difference going from
Right.
it 's it 's not like we probably lose a huge amount but we won't know because we don't have any full band models for s conversational speech.
It 's probably not as bad as going f using full band models on telephone band speech
false
QMSum_225
Right.
it 's it 's not like we probably lose a huge amount but we won't know because we don't have any full band models for s conversational speech.
It 's probably not as bad as going f using full band models on telephone band speech
So.
false
QMSum_225
it 's it 's not like we probably lose a huge amount but we won't know because we don't have any full band models for s conversational speech.
It 's probably not as bad as going f using full band models on telephone band speech
So.
Oh yeah.
false
QMSum_225
It 's probably not as bad as going f using full band models on telephone band speech
So.
Oh yeah.
Right.
false
QMSum_225
So.
Oh yeah.
Right.
right ?
false
QMSum_225
Oh yeah.
Right.
right ?
Yeah.
false
QMSum_225
Right.
right ?
Yeah.
Yeah ,
false
QMSum_225
right ?
Yeah.
Yeah ,
Right , so it 's so
false
QMSum_225
Yeah.
Yeah ,
Right , so it 's so
but for Broadcast News when we we played around between the two there wasn't a huge loss.
false
QMSum_225
Yeah ,
Right , so it 's so
but for Broadcast News when we we played around between the two there wasn't a huge loss.
Right , it was not a big deal.
false
QMSum_225
Right , so it 's so
but for Broadcast News when we we played around between the two there wasn't a huge loss.
Right , it was not a big deal.
Yeah
false
QMSum_225
but for Broadcast News when we we played around between the two there wasn't a huge loss.
Right , it was not a big deal.
Yeah
I should I should say that the language model is not just Switchboard
false
QMSum_225
Right , it was not a big deal.
Yeah
I should I should say that the language model is not just Switchboard
so I wou so that 's good.
false
QMSum_225
Yeah
I should I should say that the language model is not just Switchboard
so I wou so that 's good.
Although combining em worked well.
false
QMSum_225
I should I should say that the language model is not just Switchboard
so I wou so that 's good.
Although combining em worked well.
it 's also I mean there 's uh actually more data is from Broadcast News but with a little less weight
false
QMSum_225
so I wou so that 's good.
Although combining em worked well.
it 's also I mean there 's uh actually more data is from Broadcast News but with a little less weight
Yeah.
false
QMSum_225
Although combining em worked well.
it 's also I mean there 's uh actually more data is from Broadcast News but with a little less weight
Yeah.
uh because
false
QMSum_225
it 's also I mean there 's uh actually more data is from Broadcast News but with a little less weight
Yeah.
uh because
Uh - huh.
false
QMSum_225
Yeah.
uh because
Uh - huh.
Like Trent Lott must have been from
false
QMSum_225
uh because
Uh - huh.
Like Trent Lott must have been from
mm - hmm , right.
false
QMSum_225
Uh - huh.
Like Trent Lott must have been from
mm - hmm , right.
I guess Switchboard was before
false
QMSum_225
Like Trent Lott must have been from
mm - hmm , right.
I guess Switchboard was before
Um By the way just for fun we also ran ,
false
QMSum_225
mm - hmm , right.
I guess Switchboard was before
Um By the way just for fun we also ran ,
uh.
false
QMSum_225
I guess Switchboard was before
Um By the way just for fun we also ran ,
uh.
Good point.
false
QMSum_225
Um By the way just for fun we also ran ,
uh.
Good point.
I mean our complete system starts by doing ge a gender detection
false
QMSum_225
uh.
Good point.
I mean our complete system starts by doing ge a gender detection
Mm - hmm.
false
QMSum_225
Good point.
I mean our complete system starts by doing ge a gender detection
Mm - hmm.
so just for the heck of it I ran that
false
QMSum_225
I mean our complete system starts by doing ge a gender detection
Mm - hmm.
so just for the heck of it I ran that
And it said a hundred percent male ?
false
QMSum_225
Mm - hmm.
so just for the heck of it I ran that
And it said a hundred percent male ?
um and it might be reassuring for everybody to know that it got all the genders right.
false
QMSum_225
so just for the heck of it I ran that
And it said a hundred percent male ?
um and it might be reassuring for everybody to know that it got all the genders right.
The j
false
QMSum_225
And it said a hundred percent male ?
um and it might be reassuring for everybody to know that it got all the genders right.
The j
Yeah so
false
QMSum_225
um and it might be reassuring for everybody to know that it got all the genders right.
The j
Yeah so
Oh it did ?
false
QMSum_225
The j
Yeah so
Oh it did ?
Oh that 's I 'm glad.
false
QMSum_225
Yeah so
Oh it did ?
Oh that 's I 'm glad.
It got all two genders ?
false
QMSum_225
Oh it did ?
Oh that 's I 'm glad.
It got all two genders ?
Yeah but you know Jane and Adam have you kn about equal performance
false
QMSum_225
Oh that 's I 'm glad.
It got all two genders ?
Yeah but you know Jane and Adam have you kn about equal performance
Yeah. Yes.
false
QMSum_225
It got all two genders ?
Yeah but you know Jane and Adam have you kn about equal performance
Yeah. Yes.
and uh and that 's interesting cuz I think the their language models are quite different so and I I 'm pretty sure from listening to Eric that , you know given the words he was saying and given his pronunciation that the reason that he 's so much worse is the lapel.
false
QMSum_225
Yeah but you know Jane and Adam have you kn about equal performance
Yeah. Yes.
and uh and that 's interesting cuz I think the their language models are quite different so and I I 'm pretty sure from listening to Eric that , you know given the words he was saying and given his pronunciation that the reason that he 's so much worse is the lapel.
Yeah.
false
QMSum_225
Yeah. Yes.
and uh and that 's interesting cuz I think the their language models are quite different so and I I 'm pretty sure from listening to Eric that , you know given the words he was saying and given his pronunciation that the reason that he 's so much worse is the lapel.
Yeah.
Right.
false
QMSum_225
and uh and that 's interesting cuz I think the their language models are quite different so and I I 'm pretty sure from listening to Eric that , you know given the words he was saying and given his pronunciation that the reason that he 's so much worse is the lapel.
Yeah.
Right.
That makes a lot of sense ,
false
QMSum_225
Yeah.
Right.
That makes a lot of sense ,
So it 's nice now if we can just sort of eliminate the lapel one when when we get new microphones
false
QMSum_225
Right.
That makes a lot of sense ,
So it 's nice now if we can just sort of eliminate the lapel one when when we get new microphones
yeah. Very possible.
false
QMSum_225
That makes a lot of sense ,
So it 's nice now if we can just sort of eliminate the lapel one when when we get new microphones
yeah. Very possible.
Yeah I I I would bet on that too
false
QMSum_225
So it 's nice now if we can just sort of eliminate the lapel one when when we get new microphones
yeah. Very possible.
Yeah I I I would bet on that too
that would be worth it
false
QMSum_225
yeah. Very possible.
Yeah I I I would bet on that too
that would be worth it
cuz he certainly in that when as a as a burp user he was he was a pretty uh strong one.
false
QMSum_225
Yeah I I I would bet on that too
that would be worth it
cuz he certainly in that when as a as a burp user he was he was a pretty uh strong one.
um Yeah
false
QMSum_225
that would be worth it
cuz he certainly in that when as a as a burp user he was he was a pretty uh strong one.
um Yeah
Sheep.
false
QMSum_225
cuz he certainly in that when as a as a burp user he was he was a pretty uh strong one.
um Yeah
Sheep.
he he he sounded to me just from he sounded like a ,
false
QMSum_225
um Yeah
Sheep.
he he he sounded to me just from he sounded like a ,
Yeah.
false
QMSum_225
Sheep.
he he he sounded to me just from he sounded like a ,
Yeah.
what 's it a sheep or a goat ?
false
QMSum_225
he he he sounded to me just from he sounded like a ,
Yeah.
what 's it a sheep or a goat ?
Sheep.
false
QMSum_225
Yeah.
what 's it a sheep or a goat ?
Sheep.
A sheep.
false
QMSum_225
what 's it a sheep or a goat ?
Sheep.
A sheep.
Sheep ,
false
QMSum_225
Sheep.
A sheep.
Sheep ,
Baah.
false
QMSum_225
A sheep.
Sheep ,
Baah.
Yeah. Sheep is good.
false
QMSum_225
Sheep ,
Baah.
Yeah. Sheep is good.
right. Sounded good.
false
QMSum_225
Baah.
Yeah. Sheep is good.
right. Sounded good.
Yeah.
false
QMSum_225
Yeah. Sheep is good.
right. Sounded good.
Yeah.
Right so um so I guess the good news is that
false
QMSum_225
right. Sounded good.
Yeah.
Right so um so I guess the good news is that
Mm - hmm.
false
QMSum_225
Yeah.
Right so um so I guess the good news is that
Mm - hmm.
and and again this is without a lot of the sort of bells and whistles that we c can do with the SRI system and we 'll have more data and we can also start to maybe adapt the language models once we have enough meetings. So this is only twenty minutes of one meeting with no no tailoring at all.
false
QMSum_225
Right so um so I guess the good news is that
Mm - hmm.
and and again this is without a lot of the sort of bells and whistles that we c can do with the SRI system and we 'll have more data and we can also start to maybe adapt the language models once we have enough meetings. So this is only twenty minutes of one meeting with no no tailoring at all.
I mean clearly there are um with just a small amount of uh actual meeting transcriptions uh thrown into the language model you can probably do quite a bit better because the
false
QMSum_225
Mm - hmm.
and and again this is without a lot of the sort of bells and whistles that we c can do with the SRI system and we 'll have more data and we can also start to maybe adapt the language models once we have enough meetings. So this is only twenty minutes of one meeting with no no tailoring at all.
I mean clearly there are um with just a small amount of uh actual meeting transcriptions uh thrown into the language model you can probably do quite a bit better because the
Yeah. The voca the vocabulary especially
false
QMSum_225
and and again this is without a lot of the sort of bells and whistles that we c can do with the SRI system and we 'll have more data and we can also start to maybe adapt the language models once we have enough meetings. So this is only twenty minutes of one meeting with no no tailoring at all.
I mean clearly there are um with just a small amount of uh actual meeting transcriptions uh thrown into the language model you can probably do quite a bit better because the
Yeah. The voca the vocabulary especially
Or just dictionary.
false
QMSum_225
I mean clearly there are um with just a small amount of uh actual meeting transcriptions uh thrown into the language model you can probably do quite a bit better because the
Yeah. The voca the vocabulary especially
Or just dictionary.
yeah.
false
QMSum_225
Yeah. The voca the vocabulary especially
Or just dictionary.
yeah.
Not that much the vocabulary actually
false
QMSum_225
Or just dictionary.
yeah.
Not that much the vocabulary actually
Yeah , so.
false
QMSum_225
yeah.
Not that much the vocabulary actually
Yeah , so.
I think um well we have to see but it 's uh
false
QMSum_225
Not that much the vocabulary actually
Yeah , so.
I think um well we have to see but it 's uh
Yeah. It 's pretty good um so then
false
QMSum_225
Yeah , so.
I think um well we have to see but it 's uh
Yeah. It 's pretty good um so then
Have to add PZM and so on
false
QMSum_225
I think um well we have to see but it 's uh
Yeah. It 's pretty good um so then
Have to add PZM and so on
And I have to try it on the far field mike
false
QMSum_225
Yeah. It 's pretty good um so then
Have to add PZM and so on
And I have to try it on the far field mike
but
false
QMSum_225
Have to add PZM and so on
And I have to try it on the far field mike
but
PZM
false
QMSum_225
And I have to try it on the far field mike
but
PZM
yeah.
false
QMSum_225
but
PZM
yeah.
and then there 's things like for the transcription I got when someone has a digit in the transcript I don't know if they said , you know one one or eleven and I don't know if they said Tcl or TCL. there 's things like that where , you know the um we 'll probably have to ask the transcribers to indicate some of those kinds of things but in general it was really good and I 'm hoping and this is this is good news because that means the force alignments should be good and if the force alignments , I mean it 's good news anyway but if the force alignments are good we can get all kinds of information. For example about , you know prosodic information and speaker overlaps and so forth directly from the aligned times. Um so that 'll be something that actually in order to assess the forced alignment um we need s some linguists or some people to look at it and say are these boundaries in about the right place. Because it 's just gonna give us time marks
false
QMSum_225
PZM
yeah.
and then there 's things like for the transcription I got when someone has a digit in the transcript I don't know if they said , you know one one or eleven and I don't know if they said Tcl or TCL. there 's things like that where , you know the um we 'll probably have to ask the transcribers to indicate some of those kinds of things but in general it was really good and I 'm hoping and this is this is good news because that means the force alignments should be good and if the force alignments , I mean it 's good news anyway but if the force alignments are good we can get all kinds of information. For example about , you know prosodic information and speaker overlaps and so forth directly from the aligned times. Um so that 'll be something that actually in order to assess the forced alignment um we need s some linguists or some people to look at it and say are these boundaries in about the right place. Because it 's just gonna give us time marks
But you know
false
QMSum_225
yeah.
and then there 's things like for the transcription I got when someone has a digit in the transcript I don't know if they said , you know one one or eleven and I don't know if they said Tcl or TCL. there 's things like that where , you know the um we 'll probably have to ask the transcribers to indicate some of those kinds of things but in general it was really good and I 'm hoping and this is this is good news because that means the force alignments should be good and if the force alignments , I mean it 's good news anyway but if the force alignments are good we can get all kinds of information. For example about , you know prosodic information and speaker overlaps and so forth directly from the aligned times. Um so that 'll be something that actually in order to assess the forced alignment um we need s some linguists or some people to look at it and say are these boundaries in about the right place. Because it 's just gonna give us time marks
But you know
Well we 've done that for one meeting.
false
QMSum_225
and then there 's things like for the transcription I got when someone has a digit in the transcript I don't know if they said , you know one one or eleven and I don't know if they said Tcl or TCL. there 's things like that where , you know the um we 'll probably have to ask the transcribers to indicate some of those kinds of things but in general it was really good and I 'm hoping and this is this is good news because that means the force alignments should be good and if the force alignments , I mean it 's good news anyway but if the force alignments are good we can get all kinds of information. For example about , you know prosodic information and speaker overlaps and so forth directly from the aligned times. Um so that 'll be something that actually in order to assess the forced alignment um we need s some linguists or some people to look at it and say are these boundaries in about the right place. Because it 's just gonna give us time marks
But you know
Well we 've done that for one meeting.
so. For forced alignment.
false
QMSum_225
But you know
Well we 've done that for one meeting.
so. For forced alignment.
Uh oh oh f not for words
false
QMSum_225