An Euler-Bernoulli-type beam model of the vocal folds for describing curved and incomplete glottal closure patterns.

Incomplete glottal closure is a laryngeal configuration wherein the glottis is not fully obstructed prior to phonation. It has been linked to inefficient voice production and voice disorders. Various incomplete glottal closure patterns can arise and the mechanisms driving them are not well understood. In this work, we introduce an Euler-Bernoulli composite beam vocal fold (VF) model that produces qualitatively similar incomplete glottal closure patterns as those observed in experimental and high-fidelity numerical studies, thus offering insights into the potential underlying physical mechanisms. Refined physiological insights are pursued by incorporating the beam model into a VF posturing model that embeds the five intrinsic laryngeal muscles. Analysis of the combined model shows that co-activating the lateral cricoarytenoid (LCA) and interarytenoid (IA) muscles without activating the thyroarytenoid (TA) muscle results in a bowed (convex) VF geometry with closure at the posterior margin only; this is primarily attributed to the reactive moments at the anterior VF margin. This bowed pattern can also arise during VF compression (due to extrinsic laryngeal muscle activation for example), wherein the internal moment induced passively by the TA muscle tissue is the predominant mechanism. On the other hand, activating the TA muscle without incorporating other adductory muscles results in anterior and mid-membranous glottal closure, a concave VF geometry, and a posterior glottal opening driven by internal moments induced by TA muscle activation. In the case of initial full glottal closure, the posterior cricoarytenoid (PCA) muscle activation cancels the adductory effects of the LCA and IA muscles, resulting in a concave VF geometry and posterior glottal opening. Furthermore, certain maneuvers involving co-activation of all adductory muscles result in an hourglass glottal shape due to a reactive moment at the anterior VF margin and moderate internal moment induced by TA muscle activation. These findings have implications regarding potential laryngeal maneuvers in patients with voice disorders involving imbalances or excessive tension in the laryngeal muscles such as muscle tension dysphonia.

An Euler-Bernoulli-Type Beam Model of the Vocal Folds for Describing Curved and Incomplete Glottal Closure Patterns.

Incomplete glottal closure is a laryngeal configuration wherein the glottis is not fully obstructed prior to phonation. In this work, we introduce an Euler-Bernoulli composite beam vocal fold (VF) model that produces qualitatively similar incomplete glottal closure patterns as those observed in experimental and high-fidelity numerical studies, thus offering insights in to the potential underlying physical mechanisms. Refined physiological insights are pursued by incorporating the beam model into a VF posturing model that embeds the five intrinsic laryngeal muscles. Analysis of the combined model shows that co-activating the lateral cricoarytenoid (LCA) and interarytenoid (IA) muscles without activating the thyroarytenoid (TA) muscle results in a bowed (convex) VF geometry with closure at the posterior margin only; this is primarily attributed to the reactive moments at the anterior VF margin. This bowed pattern can also arise during VF compression (due to extrinsic laryngeal muscle activation for example), wherein the internal moment induced passively by the TA muscle tissue is the predominant mechanism. On the other hand, activating the TA muscle without incorporating other adductory muscles results in anterior and mid-membranous glottal closure, a concave VF geometry, and a posterior glottal opening driven by internal moments induced by TA muscle activation. In the case of initial full glottal closure, the posterior cricoarytenoid (PCA) muscle activation cancels the adductory effects of the LCA and IA muscles, resulting in a concave VF geometry and posterior glottal opening. Furthermore, certain maneuvers involving co-activation of all adductory muscles result in an hourglass glottal shape due to a reactive moment at the anterior VF margin and moderate internal moment induced by TA muscle activation.

Modeling the influence of the extrinsic musculature on phonation.

Neck muscles play important roles in various physiological tasks, including swallowing, head stabilization, and phonation. The mechanisms by which neck muscles influence phonation are not well understood, with conflicting reports on the change in fundamental frequency for ostensibly the same neck muscle activation scenarios. In this work, we introduce a reduced-order muscle-controlled vocal fold model, comprising both intrinsic muscle control and extrinsic muscle effects. The model predicts that when the neck muscles pull the thyroid cartilage in the superior-anterior direction (with a sufficiently large anterior component), inferior direction, or inferior-anterior direction, tension in the vocal folds increases, leading to fundamental frequency rise during sustained phonation. On the other hand, pulling in the superior direction, superior-posterior direction, or inferior-posterior direction (with a sufficiently large posterior component) tends to decrease vocal fold tension and phonation fundamental frequency. Varying the pulling force location alters the posture and phonation biomechanics, depending on the force direction. These findings suggest potential roles of particular neck muscles in modulating phonation fundamental frequency, with implications for vocal hyperfunction.

Kalman Filter Implementation of Subglottal Impedance-Based Inverse Filtering to Estimate Glottal Airflow during Phonation.

Subglottal Impedance-Based Inverse Filtering (IBIF) allows for the continuous, non-invasive estimation of glottal airflow from a surface accelerometer placed over the anterior neck skin below the larynx. It has been shown to be advantageous for the ambulatory monitoring of vocal function, specifically in the use of high-order statistics to understand long-term vocal behavior. However, during long-term ambulatory recordings over several days, conditions may drift from the laboratory environment where the IBIF parameters were initially estimated due to sensor positioning, skin attachment, or temperature, among other factors. Observation uncertainties and model mismatch may result in significant deviations in the glottal airflow estimates; unfortunately, they are very difficult to quantify in ambulatory conditions due to a lack of a reference signal. To address this issue, we propose a Kalman filter implementation of the IBIF filter, which allows for both estimating the model uncertainty and adapting the airflow estimates to correct for signal deviations. One-way analysis of variance (ANOVA) results from laboratory experiments using the Rainbow Passage indicate an improvement using the modified Kalman filter on amplitude-based measures for phonotraumatic vocal hyperfunction (PVH) subjects compared to the standard IBIF; the latter showing a statistically difference (p-value = 0.02, F = 4.1) with respect to a reference glottal volume velocity signal estimated from a single notch filter used here as ground-truth in this work. In contrast, maximum flow declination rates from subjects with vocal phonotrauma exhibit a small but statistically difference between the ground-truth signal and the modified Kalman filter when using one-way ANOVA (p-value = 0.04, F = 3.3). Other measures did not have significant differences with either the modified Kalman filter or IBIF compared to ground-truth, with the exception of H1-H2, whose performance deteriorates for both methods. Overall, both methods (modified Kalman filter and IBIF) show similar glottal airflow measures, with the advantage of the modified Kalman filter to improve amplitude estimation. Moreover, Kalman filter deviations from the IBIF output airflow might suggest a better representation of some fine details in the ground-truth glottal airflow signal. Other applications may take more advantage from the adaptation offered by the modified Kalman filter implementation.

LaDIVA: A neurocomputational model providing laryngeal motor control for speech acquisition and production.

Many voice disorders are the result of intricate neural and/or biomechanical impairments that are poorly understood. The limited knowledge of their etiological and pathophysiological mechanisms hampers effective clinical management. Behavioral studies have been used concurrently with computational models to better understand typical and pathological laryngeal motor control. Thus far, however, a unified computational framework that quantitatively integrates physiologically relevant models of phonation with the neural control of speech has not been developed. Here, we introduce LaDIVA, a novel neurocomputational model with physiologically based laryngeal motor control. We combined the DIVA model (an established neural network model of speech motor control) with the extended body-cover model (a physics-based vocal fold model). The resulting integrated model, LaDIVA, was validated by comparing its model simulations with behavioral responses to perturbations of auditory vocal fundamental frequency (fo) feedback in adults with typical speech. LaDIVA demonstrated capability to simulate different modes of laryngeal motor control, ranging from short-term (i.e., reflexive) and long-term (i.e., adaptive) auditory feedback paradigms, to generating prosodic contours in speech. Simulations showed that LaDIVA's laryngeal motor control displays properties of motor equivalence, i.e., LaDIVA could robustly generate compensatory responses to reflexive vocal fo perturbations with varying initial laryngeal muscle activation levels leading to the same output. The model can also generate prosodic contours for studying laryngeal motor control in running speech. LaDIVA can expand the understanding of the physiology of human phonation to enable, for the first time, the investigation of causal effects of neural motor control in the fine structure of the vocal signal.

Triangular body-cover model of the vocal folds with coordinated activation of the five intrinsic laryngeal muscles.

Poor laryngeal muscle coordination that results in abnormal glottal posturing is believed to be a primary etiologic factor in common voice disorders such as non-phonotraumatic vocal hyperfunction. Abnormal activity of antagonistic laryngeal muscles is hypothesized to play a key role in the alteration of normal vocal fold biomechanics that results in the dysphonia associated with such disorders. Current low-order models of the vocal folds are unsatisfactory to test this hypothesis since they do not capture the co-contraction of antagonist laryngeal muscle pairs. To address this limitation, a self-sustained triangular body-cover model with full intrinsic muscle control is introduced. The proposed scheme shows good agreement with prior studies using finite element models, excised larynges, and clinical studies in sustained and time-varying vocal gestures. Simulations of vocal fold posturing obtained with distinct antagonistic muscle activation yield clear differences in kinematic, aerodynamic, and acoustic measures. The proposed tool is deemed sufficiently accurate and flexible for future comprehensive investigations of non-phonotraumatic vocal hyperfunction and other laryngeal motor control disorders.

Estimation of Subglottal Pressure, Vocal Fold Collision Pressure, and Intrinsic Laryngeal Muscle Activation From Neck-Surface Vibration Using a Neural Network Framework and a Voice Production Model.

The ambulatory assessment of vocal function can be significantly enhanced by having access to physiologically based features that describe underlying pathophysiological mechanisms in individuals with voice disorders. This type of enhancement can improve methods for the prevention, diagnosis, and treatment of behaviorally based voice disorders. Unfortunately, the direct measurement of important vocal features such as subglottal pressure, vocal fold collision pressure, and laryngeal muscle activation is impractical in laboratory and ambulatory settings. In this study, we introduce a method to estimate these features during phonation from a neck-surface vibration signal through a framework that integrates a physiologically relevant model of voice production and machine learning tools. The signal from a neck-surface accelerometer is first processed using subglottal impedance-based inverse filtering to yield an estimate of the unsteady glottal airflow. Seven aerodynamic and acoustic features are extracted from the neck surface accelerometer and an optional microphone signal. A neural network architecture is selected to provide a mapping between the seven input features and subglottal pressure, vocal fold collision pressure, and cricothyroid and thyroarytenoid muscle activation. This non-linear mapping is trained solely with 13,000 Monte Carlo simulations of a voice production model that utilizes a symmetric triangular body-cover model of the vocal folds. The performance of the method was compared against laboratory data from synchronous recordings of oral airflow, intraoral pressure, microphone, and neck-surface vibration in 79 vocally healthy female participants uttering consecutive /pæ/ syllable strings at comfortable, loud, and soft levels. The mean absolute error and root-mean-square error for estimating the mean subglottal pressure were 191 Pa (1.95 cm H2O) and 243 Pa (2.48 cm H2O), respectively, which are comparable with previous studies but with the key advantage of not requiring subject-specific training and yielding more output measures. The validation of vocal fold collision pressure and laryngeal muscle activation was performed with synthetic values as reference. These initial results provide valuable insight for further vocal fold model refinement and constitute a proof of concept that the proposed machine learning method is a feasible option for providing physiologically relevant measures for laboratory and ambulatory assessment of vocal function.

Direct measurement and modeling of intraglottal, subglottal, and vocal fold collision pressures during phonation in an individual with a hemilaryngectomy.

The purpose of this paper is to report on the first in vivo application of a recently developed transoral, dual-sensor pressure probe that directly measures intraglottal, subglottal, and vocal fold collision pressures during phonation. Synchronous measurement of intraglottal and subglottal pressures was accomplished using two miniature pressure sensors mounted on the end of the probe and inserted transorally in a 78-year-old male who had previously undergone surgical removal of his right vocal fold for treatment of laryngeal cancer. The endoscopist used one hand to position the custom probe against the surgically medialized scar band that replaced the right vocal fold and used the other hand to position a transoral endoscope to record laryngeal high-speed videoendoscopy of the vibrating left vocal fold contacting the pressure probe. Visualization of the larynx during sustained phonation allowed the endoscopist to place the dual-sensor pressure probe such that the proximal sensor was positioned intraglottally and the distal sensor subglottally. The proximal pressure sensor was verified to be in the strike zone of vocal fold collision during phonation when the intraglottal pressure signal exhibited three characteristics: an impulsive peak at the start of the closed phase, rounded peak during the open phase, and minimum value around zero immediately preceding the impulsive peak of the subsequent phonatory cycle. Numerical voice production modeling was applied to validate model-based predictions of vocal fold collision pressure using kinematic vocal fold measures. The results successfully demonstrated feasibility of in vivo measurement of vocal fold collision pressure in an individual with a hemilaryngectomy, motivating ongoing data collection that is designed to aid in the development of vocal dose measures that incorporate vocal fold impact collision and stresses.

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

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.

State-Space Approach to Structural Representation of Perturbed Pitch Period Sequences in Voice Signals.

OBJECTIVES: The aim of this study was to propose a state space-based approach to model perturbed pitch period sequences (PPSs), extracted from real sustained vowels, combining the principal features of disturbed real PPSs with structural analysis of stochastic time series and state space methods. METHODS: The PPSs were obtained from a database composed of 53 healthy subjects. State space models were developed taking into account different structures and complexity levels. PPS features such as trend, cycle, and irregular structures were considered. Model parameters were calculated using optimization procedures. For each PPS, state estimates were obtained combining the developed models and diffuse initialization with filtering and smoothing methods. Statistical tests were applied to objectively evaluate the performance of this method. RESULTS: Statistical tests demonstrated that the proposed approach correctly represented more than the 75% of the database with a significance value of 0.05. In the analysis, structural estimates suitably characterized the dynamics of the PPSs. Trend estimates proved to properly represent slow long-term dynamics, whereas cycle estimates captured short-term autoregressive dependencies. CONCLUSIONS: The present study demonstrated that the proposed approach is suitable for representing and analyzing real perturbed PPSs, also allowing to extract further information related to the phonation process.