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.
The target yi j can represent either time since sleep (regression) or a bi- nary fatigued/non-fatigued state (classification)
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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.