elplaguister/DTS_line_datasets · Datasets at Hugging Face

topic_shift bool 2 classes	utterance stringlengths 1 7.9k	session_id stringlengths 7 14
false	Mm - hmm.	QMSum_120
false	I I I think that you could make the opposite argument that the well - matched case is a fantasy.	QMSum_120
false	Mm - hmm.	QMSum_120
false	You know , so ,	QMSum_120
false	Uh - huh.	QMSum_120
false	I think the thing is is that if you look at the well - matched case versus the po you know , the the medium and the and the fo and then the mismatched case , um , we 're seeing really , really big differences in performance. Right ? And and y you wouldn't like that to be the case. You wouldn't like that as soon as you step outside You know , a lot of the the cases it 's is	QMSum_120
false	Well , that 'll teach them to roll their window up.	QMSum_120
false	I mean , in these cases , if you go from the the , uh I mean , I don't remember the numbers right off , but if you if you go from the well - matched case to the medium , it 's not an enormous difference in the in the the training - testing situation , and and and it 's a really big performance drop.	QMSum_120
false	Mm - hmm.	QMSum_120
false	You know , so , um Yeah , I mean the reference one , for instance this is back old on , uh on Italian uh , was like six percent error for the well - matched and eighteen for the medium - matched and sixty for the for highly - mismatched. Uh , and , you know , with these other systems we we helped it out quite a bit , but still there 's there 's something like a factor of two or something between well - matched and medium - matched. And so I think that if what you 're if the goal of this is to come up with robust features , it does mean So you could argue , in fact , that the well - matched is something you shouldn't be looking at at all , that that the goal is to come up with features that will still give you reasonable performance , you know , with again gentle degregra degradation , um , even though the the testing condition is not the same as the training.	QMSum_120
false	Hmm.	QMSum_120
false	So , you know , I I could argue strongly that something like the medium mismatch , which is you know not compl pathological but I mean , what was the the medium - mismatch condition again ?	QMSum_120
false	Um , it 's Yeah. Medium mismatch is everything with the far microphone , but trained on , like , low noisy condition , like low speed and or stopped car and tested on high - speed conditions , I think , like on a highway and	QMSum_120
false	Right.	QMSum_120
false	So	QMSum_120
false	So it 's still the same same microphone in both cases ,	QMSum_120
false	Same microphone but Yeah.	QMSum_120
false	but , uh , it 's there 's a mismatch between the car conditions. And that 's uh , you could argue that 's a pretty realistic situation	QMSum_120
false	Yeah.	QMSum_120
false	Mm - hmm.	QMSum_120
false	and , uh , I 'd almost argue for weighting that highest. But the way they have it now , it 's I guess it 's it 's They they compute the relative improvement first and then average that with a weighting ?	QMSum_120
false	Yeah.	QMSum_120
false	And so then the that that makes the highly - matched the really big thing.	QMSum_120
false	Mm - hmm.	QMSum_120
false	Um , so , u i since they have these three categories , it seems like the reasonable thing to do is to go across the languages and to come up with an improvement for each of those.	QMSum_120
false	Mm - hmm.	QMSum_120
false	Just say " OK , in the in the highly - matched case this is what happens , in the m the , uh this other m medium if this happens , in the highly - mismatched that happens ".	QMSum_120
false	Mm - hmm.	QMSum_120
false	And , uh , you should see , uh , a gentle degradation through that.	QMSum_120
false	Mmm.	QMSum_120
false	Um. But I don't know.	QMSum_120
false	Yeah.	QMSum_120
false	I think that that I I I gather that in these meetings it 's it 's really tricky to make anything ac make any policy change because everybody has has , uh , their own opinion	QMSum_120
false	Mm - hmm.	QMSum_120
false	and I don't know.	QMSum_120
false	Yeah.	QMSum_120
false	Yeah.	QMSum_120
false	Uh , so Yeah. Yeah , but there is probably a a big change that will be made is that the the baseline th they want to have a new baseline , perhaps , which is , um , MFCC but with a voice activity detector. And apparently , uh , some people are pushing to still keep this fifty percent number. So they want to have at least fifty percent improvement on the baseline , but w which would be a much better baseline.	QMSum_120
false	Mm - hmm. Mm - hmm.	QMSum_120
false	And if we look at the result that Sunil sent , just putting the VAD in the baseline improved , like , more than twenty percent ,	QMSum_120
false	Mm - hmm.	QMSum_120
false	which would mean then then mean that fifty percent on this new baseline is like , well , more than sixty percent improvement on on o e e uh	QMSum_120
false	So nobody would be there , probably. Right ?	QMSum_120
false	Right now , nobody would be there , but Yeah.	QMSum_120
false	Good. Work to do.	QMSum_120
false	Uh - huh.	QMSum_120
true	So whose VAD is Is is this a ?	QMSum_120
false	Uh , they didn't decide yet. I guess i this was one point of the conference call also , but mmm , so I don't know. Um , but Yeah.	QMSum_120
false	Oh.	QMSum_120
false	Oh , I I think th that would be good. I mean , it 's not that the design of the VAD isn't important , but it 's just that it it it does seem to be i uh , a lot of work to do a good job on on that and as well as being a lot of work to do a good job on the feature design ,	QMSum_120
false	Yeah.	QMSum_120
false	so	QMSum_120
false	Yeah.	QMSum_120
false	if we can cut down on that maybe we can make some progress.	QMSum_120
false	M Yeah.	QMSum_120
false	Hmm.	QMSum_120
false	But I guess perhaps I don't know w Yeah. Uh , yeah. Per - e s s someone told that perhaps it 's not fair to do that because the , um to make a good VAD you don't have enough to with the the features that are the baseline features. So mmm , you need more features. So you really need to put more more in the in in the front - end.	QMSum_120
false	Yeah.	QMSum_120
false	So i	QMSum_120
false	Um ,	QMSum_120
false	sure. But i bu	QMSum_120
false	Wait a minute. I I 'm confused.	QMSum_120
false	Yeah.	QMSum_120
false	Wha - what do you mean ?	QMSum_120
false	Yeah , if i	QMSum_120
false	So y so you m s Yeah , but Well , let 's say for ins see , MFCC for instance doesn't have anything in it , uh , related to the pitch. So just just for example. So suppose you 've that what you really wanna do is put a good pitch detector on there and if it gets an unambiguous	QMSum_120
false	Oh , oh. I see.	QMSum_120
false	Mm - hmm.	QMSum_120
false	if it gets an unambiguous result then you 're definitely in a in a in a voice in a , uh , s region with speech. Uh.	QMSum_120
false	So there 's this assumption that the v the voice activity detector can only use the MFCC ?	QMSum_120
false	That 's not clear , but this e	QMSum_120
false	Well , for the baseline.	QMSum_120
false	Yeah.	QMSum_120
false	So so if you use other features then y But it 's just a question of what is your baseline. Right ? What is it that you 're supposed to do better than ?	QMSum_120
false	I g Yeah.	QMSum_120
false	And so having the baseline be the MFCC 's means that people could choose to pour their ener their effort into trying to do a really good VAD	QMSum_120
false	I don't s But they seem like two separate issues.	QMSum_120
false	or tryi They 're sort of separate.	QMSum_120
false	Right ? I mean	QMSum_120
false	Unfortunately there 's coupling between them , which is part of what I think Stephane is getting to , is that you can choose your features in such a way as to improve the VAD.	QMSum_120
false	Yeah.	QMSum_120
false	And you also can choose your features in such a way as to prove improve recognition. They may not be the same thing.	QMSum_120
false	But it seems like you should do both.	QMSum_120
false	You should do both	QMSum_120
false	Right ?	QMSum_120
false	and and I I think that this still makes I still think this makes sense as a baseline. It 's just saying , as a baseline , we know	QMSum_120
false	Mmm.	QMSum_120
false	you know , we had the MFCC 's before , lots of people have done voice activity detectors ,	QMSum_120
false	Mm - hmm.	QMSum_120
false	you might as well pick some voice activity detector and make that the baseline , just like you picked some version of HTK and made that the baseline.	QMSum_120
false	Yeah. Right.	QMSum_120
false	And then let 's try and make everything better. Um , and if one of the ways you make it better is by having your features be better features for the VAD then that 's so be it.	QMSum_120
false	Mm - hmm.	QMSum_120
false	But , uh , uh , uh , at least you have a starting point that 's um , cuz i i some of the some of the people didn't have a VAD at all , I guess. Right ? And and	QMSum_120
false	Yeah.	QMSum_120
false	then they they looked pretty bad and and in fact what they were doing wasn't so bad at all.	QMSum_120
false	Mm - hmm. Mm - hmm.	QMSum_120
false	But , um.	QMSum_120
false	Yeah. It seems like you should try to make your baseline as good as possible. And if it turns out that you can't improve on that , well , I mean , then , you know , nobody wins and you just use MFCC. Right ?	QMSum_120
false	Yeah. I mean , it seems like , uh , it should include sort of the current state of the art that you want are trying to improve , and MFCC 's , you know , or PLP or something it seems like reasonable baseline for the features , and anybody doing this task , uh , is gonna have some sort of voice activity detection at some level , in some way. They might use the whole recognizer to do it but rather than a separate thing , but but they 'll have it on some level. So , um.	QMSum_120

false

Mm - hmm.

QMSum_120

false

I I I think that you could make the opposite argument that the well - matched case is a fantasy.

QMSum_120

false

Mm - hmm.

QMSum_120

false

You know , so ,

QMSum_120

false

Uh - huh.

QMSum_120

false

I think the thing is is that if you look at the well - matched case versus the po you know , the the medium and the and the fo and then the mismatched case , um , we 're seeing really , really big differences in performance. Right ? And and y you wouldn't like that to be the case. You wouldn't like that as soon as you step outside You know , a lot of the the cases it 's is

QMSum_120

false

Well , that 'll teach them to roll their window up.

QMSum_120

false

I mean , in these cases , if you go from the the , uh I mean , I don't remember the numbers right off , but if you if you go from the well - matched case to the medium , it 's not an enormous difference in the in the the training - testing situation , and and and it 's a really big performance drop.

QMSum_120

false

Mm - hmm.

QMSum_120

false

You know , so , um Yeah , I mean the reference one , for instance this is back old on , uh on Italian uh , was like six percent error for the well - matched and eighteen for the medium - matched and sixty for the for highly - mismatched. Uh , and , you know , with these other systems we we helped it out quite a bit , but still there 's there 's something like a factor of two or something between well - matched and medium - matched. And so I think that if what you 're if the goal of this is to come up with robust features , it does mean So you could argue , in fact , that the well - matched is something you shouldn't be looking at at all , that that the goal is to come up with features that will still give you reasonable performance , you know , with again gentle degregra degradation , um , even though the the testing condition is not the same as the training.

QMSum_120

false

Hmm.

QMSum_120

false

So , you know , I I could argue strongly that something like the medium mismatch , which is you know not compl pathological but I mean , what was the the medium - mismatch condition again ?

QMSum_120

false

Um , it 's Yeah. Medium mismatch is everything with the far microphone , but trained on , like , low noisy condition , like low speed and or stopped car and tested on high - speed conditions , I think , like on a highway and

QMSum_120

false

Right.

QMSum_120

false

So

QMSum_120

false

So it 's still the same same microphone in both cases ,

QMSum_120

false

Same microphone but Yeah.

QMSum_120

false

but , uh , it 's there 's a mismatch between the car conditions. And that 's uh , you could argue that 's a pretty realistic situation

QMSum_120

false

Yeah.

QMSum_120

false

Mm - hmm.

QMSum_120

false

and , uh , I 'd almost argue for weighting that highest. But the way they have it now , it 's I guess it 's it 's They they compute the relative improvement first and then average that with a weighting ?

QMSum_120

false

Yeah.

QMSum_120

false

And so then the that that makes the highly - matched the really big thing.

QMSum_120

false

Mm - hmm.

QMSum_120

false

Um , so , u i since they have these three categories , it seems like the reasonable thing to do is to go across the languages and to come up with an improvement for each of those.

QMSum_120

false

Mm - hmm.

QMSum_120

false

Just say " OK , in the in the highly - matched case this is what happens , in the m the , uh this other m medium if this happens , in the highly - mismatched that happens ".

QMSum_120

false

Mm - hmm.

QMSum_120

false

And , uh , you should see , uh , a gentle degradation through that.

QMSum_120

false

Mmm.

QMSum_120

false

Um. But I don't know.

QMSum_120

false

Yeah.

QMSum_120

false

I think that that I I I gather that in these meetings it 's it 's really tricky to make anything ac make any policy change because everybody has has , uh , their own opinion

QMSum_120

false

Mm - hmm.

QMSum_120

false

and I don't know.

QMSum_120

false

Yeah.

QMSum_120

false

Yeah.

QMSum_120

false

Uh , so Yeah. Yeah , but there is probably a a big change that will be made is that the the baseline th they want to have a new baseline , perhaps , which is , um , MFCC but with a voice activity detector. And apparently , uh , some people are pushing to still keep this fifty percent number. So they want to have at least fifty percent improvement on the baseline , but w which would be a much better baseline.

QMSum_120

false

Mm - hmm. Mm - hmm.

QMSum_120

false

And if we look at the result that Sunil sent , just putting the VAD in the baseline improved , like , more than twenty percent ,

QMSum_120

false

Mm - hmm.

QMSum_120

false

which would mean then then mean that fifty percent on this new baseline is like , well , more than sixty percent improvement on on o e e uh

QMSum_120

false

So nobody would be there , probably. Right ?

QMSum_120

false

Right now , nobody would be there , but Yeah.

QMSum_120

false

Good. Work to do.

QMSum_120

false

Uh - huh.

QMSum_120

true

So whose VAD is Is is this a ?

QMSum_120

false

Uh , they didn't decide yet. I guess i this was one point of the conference call also , but mmm , so I don't know. Um , but Yeah.

QMSum_120

false

Oh.

QMSum_120

false

Oh , I I think th that would be good. I mean , it 's not that the design of the VAD isn't important , but it 's just that it it it does seem to be i uh , a lot of work to do a good job on on that and as well as being a lot of work to do a good job on the feature design ,

QMSum_120

false

Yeah.

QMSum_120

false

so

QMSum_120

false

Yeah.

QMSum_120

false

if we can cut down on that maybe we can make some progress.

QMSum_120

false

M Yeah.

QMSum_120

false

Hmm.

QMSum_120

false

But I guess perhaps I don't know w Yeah. Uh , yeah. Per - e s s someone told that perhaps it 's not fair to do that because the , um to make a good VAD you don't have enough to with the the features that are the baseline features. So mmm , you need more features. So you really need to put more more in the in in the front - end.

QMSum_120

false

Yeah.

QMSum_120

false

So i

QMSum_120

false

Um ,

QMSum_120

false

sure. But i bu

QMSum_120

false

Wait a minute. I I 'm confused.

QMSum_120

false

Yeah.

QMSum_120

false

Wha - what do you mean ?

QMSum_120

false

Yeah , if i

QMSum_120

false

So y so you m s Yeah , but Well , let 's say for ins see , MFCC for instance doesn't have anything in it , uh , related to the pitch. So just just for example. So suppose you 've that what you really wanna do is put a good pitch detector on there and if it gets an unambiguous

QMSum_120

false

Oh , oh. I see.

QMSum_120

false

Mm - hmm.

QMSum_120

false

if it gets an unambiguous result then you 're definitely in a in a in a voice in a , uh , s region with speech. Uh.

QMSum_120

false

So there 's this assumption that the v the voice activity detector can only use the MFCC ?

QMSum_120

false

That 's not clear , but this e

QMSum_120

false

Well , for the baseline.

QMSum_120

false

Yeah.

QMSum_120

false

So so if you use other features then y But it 's just a question of what is your baseline. Right ? What is it that you 're supposed to do better than ?

QMSum_120

false

I g Yeah.

QMSum_120

false

And so having the baseline be the MFCC 's means that people could choose to pour their ener their effort into trying to do a really good VAD

QMSum_120

false

I don't s But they seem like two separate issues.

QMSum_120

false

or tryi They 're sort of separate.

QMSum_120

false

Right ? I mean

QMSum_120

false

Unfortunately there 's coupling between them , which is part of what I think Stephane is getting to , is that you can choose your features in such a way as to improve the VAD.

QMSum_120

false

Yeah.

QMSum_120

false

And you also can choose your features in such a way as to prove improve recognition. They may not be the same thing.

QMSum_120

false

But it seems like you should do both.

QMSum_120

false

You should do both

QMSum_120

false

Right ?

QMSum_120

false

and and I I think that this still makes I still think this makes sense as a baseline. It 's just saying , as a baseline , we know

QMSum_120

false

Mmm.

QMSum_120

false

you know , we had the MFCC 's before , lots of people have done voice activity detectors ,

QMSum_120

false

Mm - hmm.

QMSum_120

false

you might as well pick some voice activity detector and make that the baseline , just like you picked some version of HTK and made that the baseline.

QMSum_120

false

Yeah. Right.

QMSum_120

false

And then let 's try and make everything better. Um , and if one of the ways you make it better is by having your features be better features for the VAD then that 's so be it.

QMSum_120

false

Mm - hmm.

QMSum_120

false

But , uh , uh , uh , at least you have a starting point that 's um , cuz i i some of the some of the people didn't have a VAD at all , I guess. Right ? And and

QMSum_120

false

Yeah.

QMSum_120

false

then they they looked pretty bad and and in fact what they were doing wasn't so bad at all.

QMSum_120

false

Mm - hmm. Mm - hmm.

QMSum_120

false

But , um.

QMSum_120

false

Yeah. It seems like you should try to make your baseline as good as possible. And if it turns out that you can't improve on that , well , I mean , then , you know , nobody wins and you just use MFCC. Right ?

QMSum_120

false

Yeah. I mean , it seems like , uh , it should include sort of the current state of the art that you want are trying to improve , and MFCC 's , you know , or PLP or something it seems like reasonable baseline for the features , and anybody doing this task , uh , is gonna have some sort of voice activity detection at some level , in some way. They might use the whole recognizer to do it but rather than a separate thing , but but they 'll have it on some level. So , um.

QMSum_120