ArticleBiomedical signal processing and control2025
Subject-Specific Modeling by Domain Adaptation for the Estimation of Subglottal Pressure from Neck-Surface Acceleration Signals.
Article in Biomedical signal processing and control, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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
5 citing papers in PubMed.
- Machine learning surrogate forward models for biomechanical laryngeal control.The Journal of the Acoustical Society of America · 2026Article
- Machine learning surrogate forward models for biomechanical laryngeal control.bioRxiv : the preprint server for biology · 2026Article
- Toward Acoustic-Based Normalization of Laryngeal EMG for Improved Interspeaker Consistency in Muscle-to-Acoustic Mapping.Journal of voice : official journal of the Voice Foundation · 2025Article
- Estimation of Physiological Vocal Features from Neck Surface Acceleration Signals Using Probabilistic Bayesian Neural Networks.IEEE transactions on audio, speech, and language processing (2025) · 2025Article
- Floating Ball Voice Therapy: Preliminary Effects on Outcomes and Predicting Individual Patient Differences in Generalization.Journal of speech, language, and hearing research : JSLHR · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Subglottal air pressure is a critical physiologically-based parameter that reveals fundamental pathophysiological processes in patients with voice disorders. However, its assessment in both laboratory and ambulatory settings presents significant challenges due to the necessity for specialized instruments, invasive procedures, and the impracticality of direct measurement in ambulatory contexts. This study expands upon previous efforts to estimate subglottal pressure from portable, lightweight neck-surface acceleration signals using a physiologically relevant model of voice production combined with machine learning techniques. The proposed approach employs a neural network architecture initially trained with numerical simulations from the voice production model, which is subsequently refined through a domain adaptation strategy from synthetic data to
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.