Non-stationary Bayesian estimation of parameters from a body cover model of the vocal folds

• Published in: The Journal of the Acoustical Society of America Vol. 139; no. 5; pp. 2683 - 2696
• Main Authors: Hadwin, Paul J., Galindo, Gabriel E., Daun, Kyle J., Zañartu, Matías, Erath, Byron D., Cataldo, Edson, Peterson, Sean D.
• Format: Journal Article
• Language: English
• Published: United States Acoustical Society of America 01-05-2016
• Subjects: Bayes Theorem; Biomechanical Phenomena; Humans; Least-Squares Analysis; Models, Anatomic; Models, Biological; Numerical Analysis, Computer-Assisted; Patient-Specific Modeling; Phonation; Speech; Speech Acoustics; Speech Communication; Time Factors; Vocal Cords - anatomy & histology; Vocal Cords - physiology; Voice; Voice Quality
• ISSN: 0001-4966; 1520-8524
• DOI: 10.1121/1.4948755

The evolution of reduced-order vocal fold models into clinically useful tools for subject-specific diagnosis and treatment hinges upon successfully and accurately representing an individual patient in the modeling framework. This, in turn, requires inference of model parameters from clinical measurements in order to tune a model to the given individual. Bayesian analysis is a powerful tool for estimating model parameter probabilities based upon a set of observed data. In this work, a Bayesian particle filter sampling technique capable of estimating time-varying model parameters, as occur in complex vocal gestures, is introduced. The technique is compared with time-invariant Bayesian estimation and least squares methods for determining both stationary and non-stationary parameters. The current technique accurately estimates the time-varying unknown model parameter and maintains tight credibility bounds. The credibility bounds are particularly relevant from a clinical perspective, as they provide insight into the confidence a clinician should have in the model predictions.