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cairocode
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IEMO_WAV_Diff_2_Curriculum

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audio
audio
label
int64
valence
float64
arousal
float64
domination
float64
arousal_norm
float64
valence_norm
float64
speakerID
int64
utterance_id
string
transcript
string
speaker_id
int64
emotion
string
difficulty
float64
curriculum_order
int64
overall_agreement
float64
fleiss_kappa
float64
krippendorff_alpha
float64
valence_std
float64
arousal_std
float64
dominance_std
float64
valence_icc
float64
arousal_icc
float64
dominance_icc
float64
n_categorical_evaluators
int64
n_dimensional_evaluators
int64
consensus_valence
float64
consensus_arousal
float64
consensus_dominance
float64
0
2.5
2.5
2.5
3.75
-1.25
2
Ses01F_impro01_F000
Excuse me.
2
neutral
0.175
6,688
0.353391
null
null
0.471405
0.471405
0.816497
0
0
0
4
3
2.5
2.5
2.5
0
2.5
2.5
2.5
3.75
-1.25
2
Ses01F_impro01_F001
Yeah.
2
neutral
0.175
5,561
0.382149
null
null
0.471405
0.471405
0.471405
0
0
0
4
3
2.5
2.5
2.5
0
2.5
2.5
2.5
3.75
-1.25
2
Ses01F_impro01_F002
Is there a problem?
2
neutral
0.175
7,433
0.327556
-0.333333
0
0.471405
0.471405
0.471405
0
0
0
4
3
2.5
2.5
2.5
0
2.5
3.5
2
6.25
-1.25
2
Ses01F_impro01_F005
Well what's the problem? Let me change it.
2
neutral
0.625
7,424
0.327556
-0.333333
0
0.471405
0.471405
0.471405
0
0
0
4
3
2.5
3.5
2
0
2.5
3.5
3.5
6.25
-1.25
2
Ses01F_impro01_F014
Clearly. You know, do you have like a supervisor or something?
2
neutral
0.475
7,425
0.327556
-0.333333
0
0.471405
0.471405
0.471405
0
0
0
4
3
2.5
3.5
3.5
0
2
2
2
2.5
-2.5
2
Ses01F_impro02_F003
I guess, you know, everybody has to make sacrifices.
2
neutral
0.775
6,765
0.342865
-0.333333
0
0
0.942809
0.942809
null
0
0
4
3
2
2
2
0
2.5
2.5
2.5
3.75
-1.25
2
Ses01F_impro02_F006
There's babies over there, though, that need mothers.
2
neutral
0.175
7,072
0.327556
-0.333333
0
0.471405
0.471405
0.471405
0
0
0
4
3
2.5
2.5
2.5
0
2
1
2.5
0
-2.5
2
Ses01F_impro02_F016
I have to find sitters.
2
neutral
1.225
5,116
0.392675
-0.333333
0
0
0.471405
0.816497
null
0
0
4
3
2
1
2.5
0
2.5
2.5
2.5
3.75
-1.25
2
Ses01F_impro02_F018
And your mother is close by.
2
neutral
0.175
8,565
0.302907
-0.333333
0
0.471405
0.471405
0.816497
0
0
0
4
3
2.5
2.5
2.5
0
3
3
1.5
5
0
1
Ses01F_impro02_M015
I know.
1
neutral
0.525
8,605
0.293884
-0.333333
0
0.471405
0.942809
0.471405
0
0
0
4
3
3
3
1.5
0
3.6667
3
2
5
1.66675
2
Ses01F_impro03_F018
Penny slots. That's what he plays.
2
neutral
0.497244
8,858
0.287909
-0.333333
0
0.433013
0.707107
0.829156
0
0
0
4
4
3.6667
3
2
0
3.6667
2.3333
2
3.33325
1.66675
2
Ses01F_impro03_F019
Yeah.
2
neutral
0.430581
8,569
0.302702
-0.333333
0
0.433013
0.5
0.829156
0
0
0
4
4
3.6667
2.3333
2
0
3.3333
2.6667
2
4.16675
0.83325
2
Ses01F_impro03_F022
Fifty bucks. Mmhmm.
2
neutral
0.263881
8,568
0.302702
-0.333333
0
0.5
0.433013
0.829156
0
0
0
4
4
3.3333
2.6667
2
0
3
2.3333
2
3.33325
0
2
Ses01F_impro03_F023
Yeah.
2
neutral
0.208337
9,331
0.283124
-0.333333
0
0.829156
0.5
0.707107
0
0
0
4
4
3
2.3333
2
0
4
3
2.6667
5
2.5
1
Ses01F_impro03_M000
So what's up? What's new?
1
neutral
0.597218
5,025
0.399408
-0.333333
0
0
0.707107
0.5
null
0
0
4
4
4
3
2.6667
0
4.3333
2.6667
2.3333
4.16675
3.33325
1
Ses01F_impro03_M001
Yeah. I heard.
1
neutral
0.986079
8,214
0.307487
-0.333333
0
0.433013
0.433013
0.829156
0
0
0
4
4
4.3333
2.6667
2.3333
0
4
2.6667
2.3333
4.16675
2.5
1
Ses01F_impro03_M006
He turned to you and was like...
1
neutral
0.597234
8,213
0.307487
-0.333333
0
0.433013
0.433013
0.829156
0
0
0
4
4
4
2.6667
2.3333
0
4
2.6667
2
4.16675
2.5
1
Ses01F_impro03_M008
Yeah.
1
neutral
0.708337
4,986
0.40499
-0.333333
0
0
0.433013
0.707107
null
0
0
4
4
4
2.6667
2
0
3.6667
2.3333
2.3333
3.33325
1.66675
1
Ses01F_impro03_M009
Does that mean that you're going to get citizenship, too, in- in England or whatever?
1
neutral
0.319479
8,210
0.31142
-0.333333
0
0.707107
0.5
0.433013
0
0
0
4
4
3.6667
2.3333
2.3333
0
3.6667
2.6667
2.6667
4.16675
1.66675
1
Ses01F_impro03_M016
How much did he win at the slot?
1
neutral
0.252799
8,221
0.306635
-0.333333
0
0.707107
0.5
0.5
0
0
0
4
4
3.6667
2.6667
2.6667
0
3.3333
3
2.6667
5
0.83325
1
Ses01F_impro03_M020
But the pennies always get you because you end up spending like, you know, fifty bucks.
1
neutral
0.152762
6,698
0.34757
null
null
0.5
0.829156
0.5
0
0
0
4
4
3.3333
3
2.6667
0
3
3.3333
2.6667
5.83325
0
1
Ses01F_impro03_M021
like, wait, but it's just pennies. You're like, wait a minute I'm on to you.
1
neutral
0.230534
6,498
0.363323
null
null
0.433013
0.707107
0.5
0
0
0
4
4
3
3.3333
2.6667
0
3.6667
2.6667
2.3333
4.16675
1.66675
1
Ses01F_impro03_M024
That's fair enough.
1
neutral
0.319479
6,501
0.358735
null
null
0.433013
0.433013
0.829156
0
0
0
4
4
3.6667
2.6667
2.3333
0
3
3
3
5
0
2
Ses01F_impro04_F000
Uh, what? Craig's List? Oh the internet thing?
2
neutral
0.075
5,537
0.382149
-0.333333
0
0
0.942809
0.471405
null
0
0
4
3
3
3
3
0
2.5
2.5
3
3.75
-1.25
2
Ses01F_impro04_F001
I don't know. Isn't that-- that's all like escort services and things like that.
2
neutral
0.125
3,395
0.421433
-0.333333
0
0.471405
0.471405
0
0
0
null
4
3
2.5
2.5
3
0
3
2
2.5
2.5
0
2
Ses01F_impro04_F005
He just...
2
neutral
0.125
3,315
0.421433
-0.333333
0
0
0.471405
0.471405
null
0
0
4
3
3
2
2.5
0
3
3.5
3.5
6.25
0
2
Ses01F_impro04_F006
you know what? I heard about that job. I applied for that.
2
neutral
0.35
7,298
0.327556
-0.333333
0
0.471405
0.471405
0.471405
0
0
0
4
3
3
3.5
3.5
0
2.5
2.5
2.5
3.75
-1.25
2
Ses01F_impro04_F009
Yeah.
2
neutral
0.175
9,374
0.278257
-0.333333
0
0.471405
0.816497
0.816497
0
0
0
4
3
2.5
2.5
2.5
0
3
3.5
3.5
6.25
0
2
Ses01F_impro04_F013
I just- I mean I call people up and I tell them what I'm good at.
2
neutral
0.35
7,345
0.327556
-0.333333
0
0.471405
0.471405
0.471405
0
0
0
4
3
3
3.5
3.5
0
2.5
3
2.5
5
-1.25
2
Ses01F_impro04_F014
Yeah.
2
neutral
0.25
8,729
0.293884
-0.333333
0
0.471405
0.942809
0.471405
0
0
0
4
3
2.5
3
2.5
0
3
2.5
2.5
3.75
0
1
Ses01F_impro04_M000
Have you tried, uh, Craig's List?
1
neutral
0.05
2,130
0.505719
null
null
0
0.471405
0.471405
null
0
0
4
3
3
2.5
2.5
0
3
2.5
2
3.75
0
1
Ses01F_impro04_M001
Yeah.
1
neutral
0.2
2,823
0.47121
null
null
0
0.471405
0.816497
null
0
0
4
3
3
2.5
2
0
3
2.5
3
3.75
0
1
Ses01F_impro04_M002
No. There's regular jobs on there.
1
neutral
0
2,160
0.505719
null
null
0
0.471405
0.471405
null
0
0
4
3
3
2.5
3
0
3
2.5
2.5
3.75
0
1
Ses01F_impro04_M003
Uh huh.
1
neutral
0.05
2,161
0.505719
null
null
0
0.471405
0.471405
null
0
0
4
3
3
2.5
2.5
0
3
3
3
5
0
1
Ses01F_impro04_M005
He's like a manager for a theatre.
1
neutral
0.075
78
1
null
null
0
0
0
null
null
null
4
3
3
3
3
0
3
3.5
2.5
6.25
0
1
Ses01F_impro04_M006
He's like a house manager. Yeah. It's a good job.
1
neutral
0.35
6,578
0.353391
null
null
0.471405
0.816497
0.471405
0
0
0
4
3
3
3.5
2.5
0
3.5
3
2.5
5
1.25
1
Ses01F_impro04_M007
You did?
1
neutral
0.25
8,265
0.302907
-0.333333
0
0.471405
0.816497
0.471405
0
0
0
4
3
3.5
3
2.5
0
3
3
2.5
5
0
1
Ses01F_impro04_M008
Yeah.
1
neutral
0.125
2,162
0.505719
null
null
0
0.471405
0.471405
null
0
0
4
3
3
3
2.5
0
3
2.5
2
3.75
0
1
Ses01F_impro04_M010
Hmm.
1
neutral
0.2
2,778
0.47121
null
null
0
0.471405
0.816497
null
0
0
4
3
3
2.5
2
0
3
3
2.5
5
0
1
Ses01F_impro04_M011
I don't know. He had a really good resume I guess and--
1
neutral
0.125
2,864
0.458579
null
null
0
0.942809
0.471405
null
0
0
4
3
3
3
2.5
0
3
3
2.5
5
0
1
Ses01F_impro04_M012
Yeah. Do you have a resume?
1
neutral
0.125
2,863
0.458579
null
null
0
0.942809
0.471405
null
0
0
4
3
3
3
2.5
0
3
2
3
2.5
0
1
Ses01F_impro04_M013
It's kind of helpful when getting a job.
1
neutral
0.075
487
0.691074
null
null
0
0
0.471405
null
null
0
4
3
3
2
3
0
3.5
3.5
3.5
6.25
1.25
1
Ses01F_impro04_M014
Well maybe we should sit down some time and you can just tell me all the stuff that you have done and I'll write it out.
1
neutral
0.475
7,454
0.324634
null
null
0.471405
0.816497
0.816497
0
0
0
4
3
3.5
3.5
3.5
0
2.5
3.5
3
6.25
-1.25
1
Ses01F_impro04_M015
No. You can- or you can just bring it in person. But they need, generally, something to see, you know, what kind of what that you've done, what kind of--
1
neutral
0.425
6,588
0.353391
null
null
0.471405
0.816497
0.471405
0
0
0
4
3
2.5
3.5
3
0
3
2.5
2.5
3.75
0
1
Ses01F_impro04_M016
Yeah. But the piece of paper helps, too.
1
neutral
0.05
2,132
0.505719
null
null
0
0.471405
0.471405
null
0
0
4
3
3
2.5
2.5
0
3
3.5
3
6.25
0
1
Ses01F_impro04_M018
I know. Okay. Well, um. Maybe--
1
neutral
0.3
3,103
0.421433
-0.333333
0
0
0.471405
0.471405
null
0
0
4
3
3
3.5
3
0
3
2.5
2.5
3.75
0
1
Ses01F_impro04_M019
Yeah.
1
neutral
0.05
2,789
0.47121
null
null
0
0.816497
0.471405
null
0
0
4
3
3
2.5
2.5
0
3
2.5
2.5
3.75
0
1
Ses01F_impro04_M020
Okay, yeah. I would imagine.
1
neutral
0.05
3,149
0.421433
-0.333333
0
0
0.471405
0.471405
null
0
0
4
3
3
2.5
2.5
0
3.5
3.5
3
6.25
1.25
1
Ses01F_impro04_M021
Well, sometimes you have to do things that you don't necessarily like to do to have a job. But the job affords you money, you won't have to be taking the bus right now.
1
neutral
0.425
6,579
0.353391
null
null
0.471405
0.816497
0.471405
0
0
0
4
3
3.5
3.5
3
0
3
2.5
2.5
3.75
0
1
Ses01F_impro04_M022
maybe get a car.
1
neutral
0.05
2,131
0.505719
null
null
0
0.471405
0.471405
null
0
0
4
3
3
2.5
2.5
0
2.5
2.5
2
3.75
-1.25
1
Ses01F_impro04_M023
I just, you know, think you should try a little harder maybe.
1
neutral
0.325
2,129
0.505719
null
null
0.471405
0.471405
0
0
0
null
4
3
2.5
2.5
2
0
3.5
3
3
5
1.25
1
Ses01F_impro04_M026
To save the environment, carpool.
1
neutral
0.2
8,696
0.293884
-0.333333
0
0.471405
0.942809
0.471405
0
0
0
4
3
3.5
3
3
0
3.5
3.5
3.5
6.25
1.25
1
Ses01F_impro04_M027
Yes. One less car at a time.
1
neutral
0.475
9,391
0.278257
-0.333333
0
0.471405
0.816497
0.816497
0
0
0
4
3
3.5
3.5
3.5
0
3
2.5
2
3.75
0
1
Ses01F_impro04_M028
Mmhmm.
1
neutral
0.2
490
0.691074
null
null
0
0.471405
0
null
0
null
4
3
3
2.5
2
0
2.5
2
1.5
2.5
-1.25
1
Ses01F_impro04_M032
I- I-I didn't-- okay.
1
neutral
0.65
3,228
0.421433
-0.333333
0
0.471405
0
0.471405
0
null
0
4
3
2.5
2
1.5
0
4
2.5
2
3.75
2.5
1
Ses01F_impro05_M000
Hi. Thanks for waiting.
1
neutral
0.7
5,086
0.392675
-0.333333
0
0
0.471405
0.816497
null
0
0
4
3
4
2.5
2
0
4
2.5
2
3.75
2.5
1
Ses01F_impro05_M003
Okay.
1
neutral
0.7
8,429
0.302907
-0.333333
0
0.471405
0.471405
0.816497
0
0
0
4
3
4
2.5
2
0
3.5
1.5
2
1.25
1.25
1
Ses01F_impro05_M004
Okay.
1
neutral
0.625
9,377
0.278257
-0.333333
0
0.471405
0.816497
0.816497
0
0
0
4
3
3.5
1.5
2
0
3.5
2.5
2
3.75
1.25
1
Ses01F_impro05_M005
Okay. Great. I'm really sorry that you had to do that. What's your last name? Let's start there.
1
neutral
0.325
6,720
0.342865
null
null
0.471405
0.471405
0.942809
0
0
0
4
3
3.5
2.5
2
0
3.5
2.5
2
3.75
1.25
1
Ses01F_impro05_M006
Okay.
1
neutral
0.325
6,721
0.342865
null
null
0.471405
0.471405
0.942809
0
0
0
4
3
3.5
2.5
2
0
3
2.5
2
3.75
0
1
Ses01F_impro05_M008
Uh-huh.
1
neutral
0.2
6,623
0.353391
null
null
0.471405
0.471405
0.816497
0
0
0
4
3
3
2.5
2
0
2.5
2.5
2
3.75
-1.25
1
Ses01F_impro05_M009
Yeah. Yeah, I don't see- I don't see any--
1
neutral
0.325
8,635
0.293884
-0.333333
0
0.471405
0.471405
0.942809
0
0
0
4
3
2.5
2.5
2
0
2.5
2.5
2
3.75
-1.25
1
Ses01F_impro05_M011
we've mistakenly lost your baggage. We're very sorry. On behalf of Jet Blue I would like to apologize for any inconvenience. I am allowed to issue you a fifty dollar gift card which you can use any future flights you want to take with us. I'm very sorry, once again...
1
neutral
0.325
8,632
0.293884
-0.333333
0
0.471405
0.471405
0.942809
0
0
0
4
3
2.5
2.5
2
0
2.5
2
1.5
2.5
-1.25
1
Ses01F_impro05_M012
for such an inconvenience and your bag, but
1
neutral
0.65
7,444
0.324634
null
null
0.816497
0.471405
0.816497
0
0
0
4
3
2.5
2
1.5
0
2.5
2
1.5
2.5
-1.25
1
Ses01F_impro05_M013
Yeah.
1
neutral
0.65
6,621
0.353391
null
null
0.471405
0.471405
0.816497
0
0
0
4
3
2.5
2
1.5
0
2.5
2.5
1.5
3.75
-1.25
1
Ses01F_impro05_M014
No, no.
1
neutral
0.575
8,421
0.302907
-0.333333
0
0.471405
0.471405
0.816497
0
0
0
4
3
2.5
2.5
1.5
0
2.5
2
1.5
2.5
-1.25
1
Ses01F_impro05_M015
No, no. This is real.
1
neutral
0.65
6,620
0.353391
null
null
0.471405
0.471405
0.816497
0
0
0
4
3
2.5
2
1.5
0
2.5
2.5
2
3.75
-1.25
1
Ses01F_impro05_M016
No. I'm just giving you money, the bag is gone. If it's not here, I'm really sorry there is nothing I can do because...
1
neutral
0.325
5,493
0.382149
null
null
0.471405
0.471405
0.471405
0
0
0
4
3
2.5
2.5
2
0
2.5
2.5
2.5
3.75
-1.25
1
Ses01F_impro05_M017
this is the only track record that we have and if it's not listed here then it's probably not anywhere.
1
neutral
0.175
8,363
0.302907
-0.333333
0
0.816497
0.471405
0.471405
0
0
0
4
3
2.5
2.5
2.5
0
2.5
2
2
2.5
-1.25
1
Ses01F_impro05_M019
Yes.
1
neutral
0.4
5,369
0.382149
-0.333333
0
0.471405
0
0.942809
0
null
0
4
3
2.5
2
2
0
2.5
2
2
2.5
-1.25
1
Ses01F_impro05_M020
I know.
1
neutral
0.4
8,628
0.293884
-0.333333
0
0.471405
0.471405
0.942809
0
0
0
4
3
2.5
2
2
0
2.5
2
2
2.5
-1.25
1
Ses01F_impro05_M022
Okay. Calm-
1
neutral
0.4
8,600
0.293884
-0.333333
0
0.471405
0.471405
0.942809
0
0
0
4
3
2.5
2
2
0
2.5
3.5
2.5
6.25
-1.25
1
Ses01F_impro05_M023
Actually, we are not necessarily liable to give you- to even give you this fifty dollars so... it's actually- it's actually really
1
neutral
0.475
7,290
0.327556
-0.333333
0
0.471405
0.471405
0.471405
0
0
0
4
3
2.5
3.5
2.5
0
2.5
3
2.5
5
-1.25
1
Ses01F_impro05_M024
We are actually not even allowed-- you know, like we don't have to give you this fifty dollars. We don't have to do that.
1
neutral
0.25
3,224
0.421433
-0.333333
0
0.471405
0
0.471405
0
null
0
4
3
2.5
3
2.5
0
2.5
2.5
2
3.75
-1.25
1
Ses01F_impro05_M025
It's a gift.
1
neutral
0.325
7,291
0.327556
-0.333333
0
0.471405
0.471405
0.471405
0
0
0
4
3
2.5
2.5
2
0
2.5
2.5
2.5
3.75
-1.25
1
Ses01F_impro05_M026
You know, the airlines can not be held responsible for every bag of luggage that comes through here.
1
neutral
0.175
7,292
0.327556
-0.333333
0
0.471405
0.471405
0.471405
0
0
0
4
3
2.5
2.5
2.5
0
2.5
2.5
2
3.75
-1.25
1
Ses01F_impro05_M027
With the amount of--
1
neutral
0.325
7,293
0.327556
-0.333333
0
0.471405
0.471405
0.471405
0
0
0
4
3
2.5
2.5
2
0
2.5
2.5
2
3.75
-1.25
1
Ses01F_impro05_M028
Um, actually, I don't know. I don't know. Oh really?
1
neutral
0.325
8,320
0.302907
-0.333333
0
0.816497
0.471405
0.471405
0
0
0
4
3
2.5
2.5
2
0
2.5
2.5
2.5
3.75
-1.25
1
Ses01F_impro05_M029
Well then maybe you can just call him and he can give you more money. But right now all I can do--
1
neutral
0.175
7,294
0.327556
-0.333333
0
0.471405
0.471405
0.471405
0
0
0
4
3
2.5
2.5
2.5
0
3
2.5
2
3.75
0
1
Ses01F_impro05_M030
Right now all I can do is just give you this fifty dollars, I'm really sorry.
1
neutral
0.2
7,295
0.327556
-0.333333
0
0.471405
0.471405
0.471405
0
0
0
4
3
3
2.5
2
0
3
2.5
2.5
3.75
0
1
Ses01F_impro05_M031
Oh. My name is Sean. Hi.
1
neutral
0.05
3,225
0.421433
-0.333333
0
0
0.471405
0.471405
null
0
0
4
3
3
2.5
2.5
0
2.5
2.5
2.5
3.75
-1.25
1
Ses01F_impro05_M034
If you want, you can write a letter. I can give you the address. Yes. An angry--
1
neutral
0.175
7,318
0.327556
-0.333333
0
0.471405
0.471405
0.471405
0
0
0
4
3
2.5
2.5
2.5
0
2.5
2
1.5
2.5
-1.25
1
Ses01F_impro05_M035
Or you can make a phone call. But I think a letter might be a little more-- in my impression, letters have more...
1
neutral
0.65
8,434
0.302907
-0.333333
0
0.471405
0.471405
0.816497
0
0
0
4
3
2.5
2
1.5
0
3
2
2.5
2.5
0
2
Ses01F_impro06_F003
That's sweet.
2
neutral
0.125
8,332
0.302907
-0.333333
0
0.816497
0.471405
0.471405
0
0
0
4
3
3
2
2.5
0
3.5
2
3
2.5
1.25
2
Ses01F_impro06_F004
Thank you.
2
neutral
0.2
8,333
0.302907
-0.333333
0
0.816497
0.471405
0.471405
0
0
0
4
3
3.5
2
3
0
2.5
3
3.5
5
-1.25
2
Ses01F_impro06_F009
this woman came up and, you know, it was hers. It was her person that was there.
2
neutral
0.25
3,243
0.421433
-0.333333
0
0.471405
0
0.471405
0
null
0
4
3
2.5
3
3.5
0
4
3
3.5
5
2.5
2
Ses01F_impro06_F016
I brought- I brought Chuck with me. He went to Redlands, yeah, for the day. We ran through the sprinklers.
2
neutral
0.625
946
0.55286
-0.333333
0
0
0
0.471405
null
null
0
4
3
4
3
3.5
0
4
3
3.5
5
2.5
2
Ses01F_impro06_F018
Yeah. It's huge. Redlands is an old city so they have lots and lots of stones.
2
neutral
0.625
5,102
0.392675
-0.333333
0
0.471405
0
0.816497
0
null
0
4
3
4
3
3.5
0
2.5
2
2.5
2.5
-1.25
2
Ses01F_impro06_F019
Yeah.
2
neutral
0.25
5,101
0.392675
-0.333333
0
0.471405
0
0.816497
0
null
0
4
3
2.5
2
2.5
0
2.5
3
3.5
5
-1.25
1
Ses01F_impro06_M001
Is there anything I can get you? Do you want me to take care of anything for you or...?
1
neutral
0.25
5,150
0.392675
-0.333333
0
0.471405
0
0.816497
0
null
0
4
3
2.5
3
3.5
0
2.5
2
3
2.5
-1.25
1
Ses01F_impro06_M002
...pick up anything?
1
neutral
0.2
8,750
0.293884
-0.333333
0
0.471405
0.471405
0.942809
0
0
0
4
3
2.5
2
3
0
3
3
2.5
5
0
1
Ses01F_impro06_M004
Hmm.
1
neutral
0.125
9,437
0.278214
-0.333333
0
0
1.247219
1.414214
null
0
0
4
3
3
3
2.5
0
3
2
2.5
2.5
0
1
Ses01F_impro06_M006
Mmhmm.
1
neutral
0.125
8,462
0.302907
-0.333333
0
0.471405
0.471405
0.816497
0
0
0
4
3
3
2
2.5
0
3
2.5
2.5
3.75
0
1
Ses01F_impro06_M009
Mmhmm.
1
neutral
0.05
3,007
0.436701
null
null
0
0.816497
0.816497
null
0
0
4
3
3
2.5
2.5
0
3
2.5
3
3.75
0
1
Ses01F_impro06_M010
Oh really?
1
neutral
0
6,515
0.356781
-0.333333
0
0
0.471405
1.247219
null
0
0
4
3
3
2.5
3
0
3
2
3
2.5
0
1
Ses01F_impro06_M011
You ran through the sprinklers in the cemetery?
1
neutral
0.075
5,429
0.382149
-0.333333
0
0
0.471405
0.942809
null
0
0
4
3
3
2
3
0
2.5
2.5
2.5
3.75
-1.25
1
Ses01F_impro06_M012
Is it a big cemetery?
1
neutral
0.175
8,475
0.302907
-0.333333
0
0.471405
0.471405
0.816497
0
0
0
4
3
2.5
2.5
2.5
0
3
2.5
2.5
3.75
0
1
Ses01F_impro06_M013
Oh, so they're like really old too, really old ones?
1
neutral
0.05
5,056
0.392675
-0.333333
0
0
0.471405
0.816497
null
0
0
4
3
3
2.5
2.5
0
3
2.5
2
3.75
0
1
Ses01F_impro06_M014
Mmhmm.
1
neutral
0.2
9,544
0.269235
-0.333333
0
0.471405
0.816497
0.942809
0
0
0
4
3
3
2.5
2
0
3
3
2.5
5
0
1
Ses01F_impro06_M015
Mmhmm.
1
neutral
0.125
9,961
0.226541
-0.333333
0
0.471405
0.942809
1.414214
0
0
0
4
3
3
3
2.5
End of preview. Expand in Data Studio
YAML Metadata Warning: empty or missing yaml metadata in repo card (https://huggingface.co/docs/hub/datasets-cards)

IEMOCAP with Curriculum Learning Metrics

This dataset enhances the original IEMO_WAV_Diff_2 dataset with inter-evaluator agreement metrics for curriculum learning following Lotfian & Busso (2019).

Additional Columns

  • curriculum_order: Training order (1=highest agreement, train first)
  • overall_agreement: Combined agreement score (0-1, higher is better)
  • fleiss_kappa: Categorical agreement (-1 to 1, higher is better)
  • krippendorff_alpha: Krippendorff's alpha for categorical reliability
  • valence_std, arousal_std, dominance_std: Standard deviation of dimensional ratings (lower is better)
  • valence_icc, arousal_icc, dominance_icc: Intraclass correlation coefficients (0-1, higher is better)
  • n_categorical_evaluators, n_dimensional_evaluators: Number of evaluators
  • consensus_valence, consensus_arousal, consensus_dominance: Consensus dimensional ratings

Usage for Curriculum Learning

Sort samples by curriculum_order and train on high-agreement samples first:

from datasets import load_dataset

dataset = load_dataset("cairocode/IEMO_WAV_Diff_2_Curriculum")
train_data = dataset["train"].sort("curriculum_order")

# Start with high agreement samples
easy_samples = train_data.filter(lambda x: x["overall_agreement"] > 0.5)
hard_samples = train_data.filter(lambda x: x["overall_agreement"] < 0.5)

Citation

If you use this dataset, please cite:

  • Original IEMOCAP: Busso et al. (2008)
  • Curriculum learning approach: Lotfian & Busso (2019)
  • Original dataset: cairocode/IEMO_WAV_Diff_2
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