On the calibration of powerset speaker diarization models
<|reference_start|>On the calibration of powerset speaker diarization models: End-to-end neural diarization models have usually relied on a multilabel-classification formulation of the speaker diarization problem. Recently, we proposed a powerset multiclass formulation that has beaten the state-of-the-art on multiple datasets. In this paper, we propose to study the calibration of a powerset speaker diarization model, and explore some of its uses. We study the calibration in-domain, as well as out-of-domain, and explore the data in low-confidence regions. The reliability of model confidence is then tested in practice: we use the confidence of the pretrained model to selectively create training and validation subsets out of unannotated data, and compare this to random selection. We find that top-label confidence can be used to reliably predict high-error regions. Moreover, training on low-confidence regions provides a better calibrated model, and validating on low-confidence regions can be more annotation-efficient than random regions.<|reference_end|>
@article{plaquet2024on,
    title={On the calibration of powerset speaker diarization models},
    author={Alexis Plaquet (IRIT-SAMoVA), Herv'e Bredin (IRIT-SAMoVA, CNRS)},
    journal={Interspeech 2024, Sep 2024, Kos, Greece. pp.3764-3768},
    year={2024},
    doi={10.21437/Interspeech.2024-1060},
    archivePrefix={arXiv},
    eprint={2409.15885},
    primaryClass={cs.SD cs.LG eess.AS}
}