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This research reframes speaker adaptation for health monitoring as a meta-learning challenge to eliminate the computational cost of retraining models for every new user. By comparing ensemble, prototypical network, and transformer-based methods on longitudinal shift worker data, the study demonstrates that these advanced approaches significantly outperform conventional mixed-effects models in predicting fatigue indicators from speech embeddings. The findings offer a practical pathway toward scalable, production-ready voice analytics without sacrificing personalization accuracy.
Meta-Learning Approaches for Speaker-Dependent Voice Fatigue Models
arXiv:2505.23378v3 Announce Type: replace Abstract: 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.
Abstract: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.
5 pages, 3 figures. Accepted at Interspeech 2025
Machine Learning (cs.LG)
arXiv:2505.23378 [cs.LG]
(or arXiv:2505.23378v3 [cs.LG] for this version)
Proc. Interspeech 2025, 2038-2042
https://doi.org/10.21437/Interspeech.2025-2055 DOI(s) linking to related resources
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