Meta-Learning Approaches for Speaker-Dependent Voice Fatigue Models
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866908389270880256 |
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| author | Polle, Roseline Norbury, Agnes Georgescu, Alexandra Livia Cummins, Nicholas Goria, Stefano |
| author_facet | Polle, Roseline Norbury, Agnes Georgescu, Alexandra Livia Cummins, Nicholas Goria, Stefano |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_23378 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Meta-Learning Approaches for Speaker-Dependent Voice Fatigue Models Polle, Roseline Norbury, Agnes Georgescu, Alexandra Livia Cummins, Nicholas Goria, Stefano Machine Learning 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. |
| title | Meta-Learning Approaches for Speaker-Dependent Voice Fatigue Models |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2505.23378 |