ArticleJASA express letters2025
Toward ambulatory monitoring of vocal behavior at the physiological level using deep ensembles and Bayesian neural networks.
Article in JASA express letters, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
2 citing papers in PubMed.
- Diagnostic Accuracy of Artificial Intelligence in Laryngeal Disorders: An Integrative Review.Journal of personalized medicine · 2026Review
- Perceiving Physiology from the Voice: Evidence for Physiological Coupling Between Laryngeal and Epilaryngeal Adjustments.Journal of voice : official journal of the Voice Foundation · 2026Article
Corrections and comments
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Authors and funding
1 author.
Funding
Abstract
Currently, diagnosis of voice disorders is often made when patients visit the clinic, by which time speakers already experience vocal difficulties. The goal of this study was to develop a voice inversion system that predicts how speakers modulate vocal physiology from the produced voice, toward early detection of unhealthy vocal behavior. Two neural networks, a Bayesian neural network and a deep ensemble of neural networks, were developed that predict changes in vocal physiological parameters and their confidence intervals. Comparison to human data showed that both networks were able to predict meaningful differences in vocal behavior across subjects, demonstrating their potential toward ambulatory monitoring of vocal behavior at the physiological level.
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Registered trials
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