Physical simulation of laryngeal disorders using a multiple-mass vocal fold model

Neural network-based estimation of biomechanical vocal fold parameters

Vocal fold (VF) vibrations are the primary source of human phonation. High-speed video (HSV) endoscopy enables the computation of descriptive VF parameters for assessment of physiological properties of laryngeal dynamics, i.e., the vibration of the VFs. However, underlying biomechanical factors responsible for physiological and disordered VF vibrations cannot be accessed. In contrast, physically based numerical VF models reveal insights into the organ's oscillations, which remain inaccessible through endoscopy. To estimate biomechanical properties, previous research has fitted subglottal pressure-driven mass-spring-damper systems, as inverse problem to the HSV-recorded VF trajectories, by global optimization of the numerical model. A neural network trained on the numerical model may be used as a substitute for computationally expensive optimization, yielding a fast evaluating surrogate of the biomechanical inverse problem. This paper proposes a convolutional recurrent neural network (CRNN)-based architecture trained on regression of a physiological-based biomechanical six-mass model (6 MM). To compare with previous research, the underlying biomechanical factor "subglottal pressure" prediction was tested against 288 HSV ex vivo porcine recordings. The contributions of this work are two-fold: first, the presented CRNN with the 6 MM handles multiple trajectories along the VFs, which allows for investigations on local changes in VF characteristics. Second, the network was trained to reproduce further important biomechanical model parameters like VF mass and stiffness on synthetic data. Unlike in a previous work, the network in this study is therefore an entire surrogate of the inverse problem, which allowed for explicit computation of the fitted model using our approach. The presented approach achieves a best-case mean absolute error (MAE) of 133 Pa (13.9%) in subglottal pressure prediction with 76.6% correlation on experimental data and a re-estimated fundamental frequency MAE of 15.9 Hz (9.9%). In-detail training analysis revealed subglottal pressure as the most learnable parameter. With the physiological-based model design and advances in fast parameter prediction, this work is a next step in biomechanical VF model fitting and the estimation of laryngeal kinematics.

Investigating blunt force trauma to the larynx: The role of inferior-superior vocal fold displacement on phonation

Blunt force trauma to the larynx, which may result from motor vehicle collisions, sports activities, etc., can cause significant damage, often leading to displaced fractures of the laryngeal cartilages, thereby disrupting vocal function. Current surgical interventions primarily focus on airway restoration to stabilize the patient, with restoration of vocal function usually being a secondary consideration. Due to laryngeal fracture, asymmetric vertical misalignment of the left or right vocal fold (VF) in the inferior-superior direction often occurs. This affects VF closure and can lead to a weak, breathy voice requiring increased vocal effort. It is unclear, however, how much vertical VF misalignment can be tolerated before voice quality degrades significantly. To address this need, the influence of inferior-superior VF displacement on phonation is investigated in 1.0mm increments using synthetic, self-oscillating VF models in a physiologically-representative facility. Acoustic (SPL, frequency, H1-H2, jitter, and shimmer), kinematic (amplitude and phase differences), and aerodynamic parameters (flow rate and subglottal pressure) are investigated as a function of inferior-superior vertical displacement. Significant findings include that once the inferior-superior medial length of the VF is surpassed, sustained phonation degrades precipitously, becoming severely pathological. If laryngeal reconstruction approaches can ensure VF contact is maintained during phonation (i.e., vertical displacement doesn't surpass VF medial length), improved vocal outcomes are expected.

Analysis of Measured and Simulated Supraglottal Acoustic Waves

To date, although much attention has been paid to the estimation and modeling of the voice source (ie, the glottal airflow volume velocity), the measurement and characterization of the supraglottal pressure wave have been much less studied. Some previous results have unveiled that the supraglottal pressure wave has some spectral resonances similar to those of the voice pressure wave. This makes the supraglottal wave partially intelligible. Although the explanation for such effect seems to be clearly related to the reflected pressure wave traveling upstream along the vocal tract, the influence that nonlinear source-filter interaction has on it is not as clear. This article provides an insight into this issue by comparing the acoustic analyses of measured and simulated supraglottal and voice waves. Simulations have been performed using a high-dimensional discrete vocal fold model. Results of such comparative analysis indicate that spectral resonances in the supraglottal wave are mainly caused by the regressive pressure wave that travels upstream along the vocal tract and not by source-tract interaction. On the contrary and according to simulation results, source-tract interaction has a role in the loss of intelligibility that happens in the supraglottal wave with respect to the voice wave. This loss of intelligibility mainly corresponds to spectral differences for frequencies above 1500 Hz.

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.