Bayesian estimation of vocal function measures using laryngeal high-speed videoendoscopy and glottal airflow estimates: An in vivo case study

Abstract

This study introduces the in vivo application of a Bayesian framework to estimate subglottal pressure, laryngeal muscle activation, and vocal fold contact pressure from calibrated transnasal high-speed videoendoscopy and oral airflow data. A subject-specific, lumped-element vocal fold model is estimated using an extended Kalman filter and two observation models involving glottal area and glottal airflow. Model-based inferences using data from a vocally healthy male individual are compared with empirical estimates of subglottal pressure and reference values for muscle activation and contact pressure in the literature, thus providing baseline error metrics for future clinical investigations.

Fig. 1.

(Color online) Clinical signals for a repetitive /pa/ gesture of the subject. Top: Intraoral pressure. Center: Glottal area waveform. Bottom: Oral (thick line) and inverse-filtered glottal (thin line) volume velocity. Differences in signal dynamics indicate voiced and plosive segments.

Fig. 2.

(Color online) Observed data (solid line), model approximations, and estimates extracted from EKF and sustained vowel data for case I (dashed line) and case II (dash-dotted line). Shaded regions represent 95% confidence bounds.

Fig. 3.

(Color online) Comparison of IOP signal (solid line) for a repetitive /pa/ phonation, and estimated P_s obtained through EKF (case I: dashed line, case II: dash-dotted line). Shaded regions represent 95% confidence bounds.