cairocode/MSPI_WAV_Diff_Curriculum

IEMOCAP with Curriculum Learning Metrics

This dataset enhances the original IEMOWAVDiff_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/MSPI_WAV_Diff_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/IEMOWAVDiff_2