Learning from Annotation Uncertainty: Entropy-Aware Curriculum for Speech Emotion Recognition
Speech emotion recognition (SER) often relies on hard consensus labels that collapse annotator disagreement. We study distribution-based supervision for 9-class SER on MSP-Podcast 2.0 using a WavLM-Base multitask model for categorical emotion and dimensional VAD. Hard-label training is compared with targets from primary and merged primary--secondary annotator vote distributions. Distributional objectives improve alignment with human vote distributions, reducing JSD/KLD relative to hard-label training. Analysis shows that hard supervision partly benefits from assigning ambiguous utterances to the residual Other class, whereas distributional supervision redistributes uncertainty across emot...
| Authors | Zahra Omidi, John H. L. Hansen |
|---|---|
| Primary category | cs.SD |
| Published | 25 June 2026 |
| Last updated at source | 25 June 2026 |
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