Meta-learning, especially a transformer-based sequence model, outperforms cross-sectional and mixed-effects baselines for predicting time since sleep from speech, though the evaluation protocol may overstate deployment performance.
Model Performance We observed that, overall, prediction accuracy increases with the number of available observations per speaker (Figure 2)
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Meta-Learning Approaches for Speaker-Dependent Voice Fatigue Models
Meta-learning, especially a transformer-based sequence model, outperforms cross-sectional and mixed-effects baselines for predicting time since sleep from speech, though the evaluation protocol may overstate deployment performance.