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Meta-Learning Approaches for Speaker-Dependent Voice Fatigue Models

Primary research

#803

T1new
Topic
unassigned (set during synthesis)
First seen
2026-07-27 13:13:52
Last seen
2026-07-27 13:13:52

Source raw items (1)

  • arXiv2026-07-27 13:13:12
    Meta-Learning Approaches for Speaker-Dependent Voice Fatigue Models

    Speaker-dependent modelling can substantially improve performance in speech-based health monitoring applications. While mixed-effect models are commonly used for such speaker adaptation, they require computationally expensive retraining for each new observation, making them impractical in a production environment. We reformulate this task as a meta-learning problem and explore three approaches of increasing complexity: ensemble-based distance models, prototypical networks, and transformer-based sequence models. Using pre-trained speech embeddings, we evaluate these methods on a large longitudinal dataset of shift workers (N=1,185, 10,286 recordings), predicting time since sleep from speech as a function of fatigue, a symptom commonly associated with ill-health. Our results demonstrate that all meta-learning approaches tested outperformed both cross-sectional and conventional mixed-effects models, with a transformer-based method achieving the strongest performance.