elplaguister/DTS_line_datasets · Datasets at Hugging Face

topic_shift bool 2 classes	utterance stringlengths 1 7.9k	session_id stringlengths 7 14
false	I beg your pardon ?	QMSum_78
false	Well	QMSum_78
false	Which is ?	QMSum_78
false	What 's the interesting stuff ?	QMSum_78
false	I beg your pardon ?	QMSum_78
false	Yeah.	QMSum_78
false	Yeah. Th - now you get to tell us what 's the interesting part.	QMSum_78
false	Please specify.	QMSum_78
false	But	QMSum_78
false	Well , uh , I guess the work that 's been done on segmentation would be most	QMSum_78
false	Yeah.	QMSum_78
false	I think that would be a good thing to start with.	QMSum_78
false	Yeah.	QMSum_78
false	OK. Um , and , um , the other thing , uh , which I 'll just say very briefly that maybe relates to that a little bit , which is that , um , uh , one of the suggestions that came up in a brief meeting I had the other day when I was in Spain with , uh , Manolo Pardo and Javier , uh , Ferreiros , who was here before , was , um , why not start with what they had before but add in the non - silence boundaries. So , in what Javier did before when they were doing , um h he was looking for , uh , speaker change points.	QMSum_78
false	Mm - hmm.	QMSum_78
false	Um. As a simplification , he originally did this only using silence as , uh , a putative , uh , speaker change point.	QMSum_78
false	Yeah.	QMSum_78
false	And , uh , he did not , say , look at points where you were changing broad sp uh , phonetic class , for instance. And for Broadcast News , that was fine. Here obviously it 's not.	QMSum_78
false	Yeah.	QMSum_78
false	And , um , so one of the things that they were pushing in d in discussing with me is , um , w why are you spending so much time , uh , on the , uh , feature issue , uh , when perhaps if you sort of deal with what you were using before	QMSum_78
false	Uh - huh.	QMSum_78
false	and then just broadened it a bit , instead of just ta using silence as putative change point also ?	QMSum_78
false	Nnn , yeah.	QMSum_78
false	So then you 've got you already have the super - structure with Gaussians and H - you know , simple H M Ms and so forth. And you you might So there was a there was a little bit of a a a a difference of opinion because I I thought that it was it 's interesting to look at what features are useful.	QMSum_78
false	Yeah.	QMSum_78
false	But , uh , on the other hand I saw that the they had a good point that , uh , if we had something that worked for many cases before , maybe starting from there a little bit Because ultimately we 're gonna end up with some s su kind of structure like that ,	QMSum_78
false	Yeah.	QMSum_78
false	where you have some kind of simple HMM and you 're testing the hypothesis that , uh , there is a change.	QMSum_78
false	Yeah.	QMSum_78
false	So so anyway , I just reporting that.	QMSum_78
false	OK.	QMSum_78
false	But , uh , uh So. Yeah , why don't we do the speech - nonspeech discussion ?	QMSum_78
false	Yeah. Do I I hear you you didn't	QMSum_78
false	Speech - nonspeech ? OK.	QMSum_78
false	Uh - huh. Yeah.	QMSum_78
false	Um , so , uh , what we basically did so far was using the mixed file to to detect s speech or nonspeech portions in that.	QMSum_78
false	Mm - hmm.	QMSum_78
false	And what I did so far is I just used our old Munich system , which is an HMM - ba based system with Gaussian mixtures for s speech and nonspeech. And it was a system which used only one Gaussian for silence and one Gaussian for speech. And now I added , uh , multi - mixture possibility for for speech and nonspeech.	QMSum_78
false	Mm - hmm. Mm - hmm.	QMSum_78
false	And I did some training on on one dialogue , which was transcribed by Yeah. We we did a nons s speech - nonspeech transcription.	QMSum_78
false	Jose.	QMSum_78
false	Adam , Dave , and I , we did , for that dialogue and I trained it on that. And I did some pre - segmentations for for Jane. And I 'm not sure how good they are or what what the transcribers say. They they can use it or ?	QMSum_78
false	Uh , they they think it 's a terrific improvement. And , um , it real it just makes a a world of difference.	QMSum_78
false	Hmm.	QMSum_78
false	And , um , y you also did some something in addition which was , um , for those in which there was , uh , quiet speakers in the mix.	QMSum_78
false	Yeah. Uh , yeah. That that was one one one thing , uh , why I added more mixtures for for the speech. So I saw that there were loud loudly speaking speakers and quietly speaking speakers.	QMSum_78
false	Mm - hmm.	QMSum_78
false	And so I did two mixtures , one for the loud speakers and one for the quiet speakers.	QMSum_78
false	And did you hand - label who was loud and who was quiet , or did you just ?	QMSum_78
false	I did that for for five minutes of one dialogue	QMSum_78
false	Right.	QMSum_78
false	and that was enough to to train the system.	QMSum_78
false	W What ?	QMSum_78
false	Yeah.	QMSum_78
false	And so it it adapts , uh , on while running. So.	QMSum_78
false	What kind of , uh , front - end processing did you do ?	QMSum_78
false	Hopefully.	QMSum_78
false	OK.	QMSum_78
false	It 's just our our old Munich , uh , loudness - based spectrum on mel scale twenty twenty critical bands and then loudness.	QMSum_78
false	Mm - hmm.	QMSum_78
false	And four additional features , which is energy , loudness , modified loudness , and zero crossing rate. So it 's twenty - four twenty - four features.	QMSum_78
false	Mmm.	QMSum_78
false	Mm - hmm.	QMSum_78
false	And you also provided me with several different versions ,	QMSum_78
false	Yeah.	QMSum_78
false	which I compared.	QMSum_78
false	Yeah.	QMSum_78
false	And so you change parameters. What do you wanna say something about the parameters that you change ?	QMSum_78
false	Yeah. You can specify the minimum length of speech or and silence portions which you want. And so I did some some modifications in those parameters , basically changing the minimum minimum length for s for silence to have , er to have , um yeah to have more or less , uh , silence portions in inserted. So.	QMSum_78
false	Right. So this would work well for , uh , pauses and utterance boundaries and things like that.	QMSum_78
false	Yeah.	QMSum_78
false	Yeah. Yeah.	QMSum_78
false	But for overlap I imagine that doesn't work at all ,	QMSum_78
false	Yeah.	QMSum_78
false	Yeah.	QMSum_78
false	that you 'll have plenty of s sections that are	QMSum_78
false	Yeah.	QMSum_78
false	Yeah.	QMSum_78
false	That 's it. Yeah.	QMSum_78
false	Mm - hmm , mm - hmm.	QMSum_78
false	Yeah.	QMSum_78
false	But	QMSum_78
false	That 's true. But it it saves so much time the the transcribers	QMSum_78
false	Um	QMSum_78
false	Yep.	QMSum_78
false	just enormous , enormous savings. Fantastic.	QMSum_78
false	That 's great. Um , just qu one quickly , uh , still on the features. So you have these twenty - four features.	QMSum_78
false	Yeah.	QMSum_78
false	Uh , a lot of them are spectral features. Is there a a transformation , uh , like principal components transformation or something ?	QMSum_78
false	No.	QMSum_78
false	Yeah. It was IS two.	QMSum_78
false	No. W w we originally we did that	QMSum_78
false	Just	QMSum_78
false	but we saw , uh , when we used it , uh , f for our close - talking microphone , which yeah , for our for our recognizer in Munich we saw that w it 's it 's not it 's not so necessary. It it works as well f with with without , uh , a LDA or something.	QMSum_78
false	OK. OK. No , I was j curious.	QMSum_78
false	Yeah.	QMSum_78
false	Mm - hmm.	QMSum_78
false	Yeah , I don't think it 's a big deal for this application ,	QMSum_78
false	Yeah.	QMSum_78
false	Right.	QMSum_78

false

I beg your pardon ?

QMSum_78

false

Well

QMSum_78

false

Which is ?

QMSum_78

false

What 's the interesting stuff ?

QMSum_78

false

I beg your pardon ?

QMSum_78

false

Yeah.

QMSum_78

false

Yeah. Th - now you get to tell us what 's the interesting part.

QMSum_78

false

Please specify.

QMSum_78

false

But

QMSum_78

false

Well , uh , I guess the work that 's been done on segmentation would be most

QMSum_78

false

Yeah.

QMSum_78

false

I think that would be a good thing to start with.

QMSum_78

false

Yeah.

QMSum_78

false

OK. Um , and , um , the other thing , uh , which I 'll just say very briefly that maybe relates to that a little bit , which is that , um , uh , one of the suggestions that came up in a brief meeting I had the other day when I was in Spain with , uh , Manolo Pardo and Javier , uh , Ferreiros , who was here before , was , um , why not start with what they had before but add in the non - silence boundaries. So , in what Javier did before when they were doing , um h he was looking for , uh , speaker change points.

QMSum_78

false

Mm - hmm.

QMSum_78

false

Um. As a simplification , he originally did this only using silence as , uh , a putative , uh , speaker change point.

QMSum_78

false

Yeah.

QMSum_78

false

And , uh , he did not , say , look at points where you were changing broad sp uh , phonetic class , for instance. And for Broadcast News , that was fine. Here obviously it 's not.

QMSum_78

false

Yeah.

QMSum_78

false

And , um , so one of the things that they were pushing in d in discussing with me is , um , w why are you spending so much time , uh , on the , uh , feature issue , uh , when perhaps if you sort of deal with what you were using before

QMSum_78

false

Uh - huh.

QMSum_78

false

and then just broadened it a bit , instead of just ta using silence as putative change point also ?

QMSum_78

false

Nnn , yeah.

QMSum_78

false

So then you 've got you already have the super - structure with Gaussians and H - you know , simple H M Ms and so forth. And you you might So there was a there was a little bit of a a a a difference of opinion because I I thought that it was it 's interesting to look at what features are useful.

QMSum_78

false

Yeah.

QMSum_78

false

But , uh , on the other hand I saw that the they had a good point that , uh , if we had something that worked for many cases before , maybe starting from there a little bit Because ultimately we 're gonna end up with some s su kind of structure like that ,

QMSum_78

false

Yeah.

QMSum_78

false

where you have some kind of simple HMM and you 're testing the hypothesis that , uh , there is a change.

QMSum_78

false

Yeah.

QMSum_78

false

So so anyway , I just reporting that.

QMSum_78

false

OK.

QMSum_78

false

But , uh , uh So. Yeah , why don't we do the speech - nonspeech discussion ?

QMSum_78

false

Yeah. Do I I hear you you didn't

QMSum_78

false

Speech - nonspeech ? OK.

QMSum_78

false

Uh - huh. Yeah.

QMSum_78

false

Um , so , uh , what we basically did so far was using the mixed file to to detect s speech or nonspeech portions in that.

QMSum_78

false

Mm - hmm.

QMSum_78

false

And what I did so far is I just used our old Munich system , which is an HMM - ba based system with Gaussian mixtures for s speech and nonspeech. And it was a system which used only one Gaussian for silence and one Gaussian for speech. And now I added , uh , multi - mixture possibility for for speech and nonspeech.

QMSum_78

false

Mm - hmm. Mm - hmm.

QMSum_78

false

And I did some training on on one dialogue , which was transcribed by Yeah. We we did a nons s speech - nonspeech transcription.

QMSum_78

false

Jose.

QMSum_78

false

Adam , Dave , and I , we did , for that dialogue and I trained it on that. And I did some pre - segmentations for for Jane. And I 'm not sure how good they are or what what the transcribers say. They they can use it or ?

QMSum_78

false

Uh , they they think it 's a terrific improvement. And , um , it real it just makes a a world of difference.

QMSum_78

false

Hmm.

QMSum_78

false

And , um , y you also did some something in addition which was , um , for those in which there was , uh , quiet speakers in the mix.

QMSum_78

false

Yeah. Uh , yeah. That that was one one one thing , uh , why I added more mixtures for for the speech. So I saw that there were loud loudly speaking speakers and quietly speaking speakers.

QMSum_78

false

Mm - hmm.

QMSum_78

false

And so I did two mixtures , one for the loud speakers and one for the quiet speakers.

QMSum_78

false

And did you hand - label who was loud and who was quiet , or did you just ?

QMSum_78

false

I did that for for five minutes of one dialogue

QMSum_78

false

Right.

QMSum_78

false

and that was enough to to train the system.

QMSum_78

false

W What ?

QMSum_78

false

Yeah.

QMSum_78

false

And so it it adapts , uh , on while running. So.

QMSum_78

false

What kind of , uh , front - end processing did you do ?

QMSum_78

false

Hopefully.

QMSum_78

false

OK.

QMSum_78

false

It 's just our our old Munich , uh , loudness - based spectrum on mel scale twenty twenty critical bands and then loudness.

QMSum_78

false

Mm - hmm.

QMSum_78

false

And four additional features , which is energy , loudness , modified loudness , and zero crossing rate. So it 's twenty - four twenty - four features.

QMSum_78

false

Mmm.

QMSum_78

false

Mm - hmm.

QMSum_78

false

And you also provided me with several different versions ,

QMSum_78

false

Yeah.

QMSum_78

false

which I compared.

QMSum_78

false

Yeah.

QMSum_78

false

And so you change parameters. What do you wanna say something about the parameters that you change ?

QMSum_78

false

Yeah. You can specify the minimum length of speech or and silence portions which you want. And so I did some some modifications in those parameters , basically changing the minimum minimum length for s for silence to have , er to have , um yeah to have more or less , uh , silence portions in inserted. So.

QMSum_78

false

Right. So this would work well for , uh , pauses and utterance boundaries and things like that.

QMSum_78

false

Yeah.

QMSum_78

false

Yeah. Yeah.

QMSum_78

false

But for overlap I imagine that doesn't work at all ,

QMSum_78

false

Yeah.

QMSum_78

false

Yeah.

QMSum_78

false

that you 'll have plenty of s sections that are

QMSum_78

false

Yeah.

QMSum_78

false

Yeah.

QMSum_78

false

That 's it. Yeah.

QMSum_78

false

Mm - hmm , mm - hmm.

QMSum_78

false

Yeah.

QMSum_78

false

But

QMSum_78

false

That 's true. But it it saves so much time the the transcribers

QMSum_78

false

Um

QMSum_78

false

Yep.

QMSum_78

false

just enormous , enormous savings. Fantastic.

QMSum_78

false

That 's great. Um , just qu one quickly , uh , still on the features. So you have these twenty - four features.

QMSum_78

false

Yeah.

QMSum_78

false

Uh , a lot of them are spectral features. Is there a a transformation , uh , like principal components transformation or something ?

QMSum_78

false

No.

QMSum_78

false

Yeah. It was IS two.

QMSum_78

false

No. W w we originally we did that

QMSum_78

false

Just

QMSum_78

false

but we saw , uh , when we used it , uh , f for our close - talking microphone , which yeah , for our for our recognizer in Munich we saw that w it 's it 's not it 's not so necessary. It it works as well f with with without , uh , a LDA or something.

QMSum_78

false

OK. OK. No , I was j curious.

QMSum_78

false

Yeah.

QMSum_78

false

Mm - hmm.

QMSum_78

false

Yeah , I don't think it 's a big deal for this application ,

QMSum_78

false

Yeah.

QMSum_78

false

Right.

QMSum_78