Computational Modeling of Voice Production Using Excised Canine Larynx

A combined experimental-numerical work was conducted to comprehensively validate a subject-specific continuum model of voice production in larynx using excised canine laryngeal experiments. The computational model is a coupling of the Navier-Stokes equations for glottal flow dynamics and a finite element model of vocal fold dynamics. The numerical simulations employed a cover-body vocal fold structure with the geometry reconstructed from MRI scans and the material properties determined through an optimization-based inverse process of experimental indentation measurement. The results showed that the simulations predicted key features of the dynamics observed in the experiments, including the skewing of the glottal flow waveform, mucosal wave propagation, continuous increase of the divergent angle and intraglottal swirl strength during glottal closing, and flow recirculation between glottal jet and vocal fold. The simulations also predicted the increase of the divergent angle, glottal jet speed and intraglottal flow swirl strength with the subglottal pressure, same as in the experiments. Quantitatively, the simulations over-predicted the frequency and jet speed and under-predicted the flow rate and divergent angle for the larynx under study. The limitations of the model and their implications were discussed.

Vocal Folds Analysis for Detection and Classification of Voice Disorder

The detection and description of pathological voice are the most important applications of voice profiling. Currently, techniques like laryngostroboscopy or surgical microlarynoscopy are popularly used for the diagnosis of voice pathologies but are invasive in nature. Disorders of vocal folds impact the quality of voice, and therefore, the accuracy of voice profiling is reduced. This paper presents a better solution to differentiate normal and pathological voices based on the glottal, physical, and acoustic and equivalent electrical parameters. These parameters have been correlated using mathematical equations and models. Results reveal that the glottal flow is strongly influenced by physical parameters like stiffness and viscosity of vocal folds in case of pathological voice. However, their direct measurement requires complex invasive medical procedures or costly and complex electronic hardware arrangements in case of non-invasive methods. Glottal parameters, on the other hand, facilitate much simpler estimation of vocal folds disorders. In this work, the authors have presented two non-invasive approaches for better accuracy and least complexity for differentiating normal and pathological voices: 1) by using correlation of glottal and physical parameters, 2)by using acoustic and equivalent electrical parameters.

Effect of Subglottic Stenosis on Vocal Fold Vibration and Voice Production Using Fluid–Structure–Acoustics Interaction Simulation

An in-house 3D fluid–structure–acoustic interaction numerical solver was employed to investigate the effect of subglottic stenosis (SGS) on dynamics of glottal flow, vocal fold vibration and acoustics during voice production. The investigation focused on two SGS properties, including severity defined as the percentage of area reduction and location. The results show that SGS affects voice production only when its severity is beyond a threshold, which is at 75% for the glottal flow rate and acoustics, and at 90% for the vocal fold vibrations. Beyond the threshold, the flow rate, vocal fold vibration amplitude and vocal efficiency decrease rapidly with SGS severity, while the skewness quotient, vibration frequency, signal-to-noise ratio and vocal intensity decrease slightly, and the open quotient increases slightly. Changing the location of SGS shows no effect on the dynamics. Further analysis reveals that the effect of SGS on the dynamics is primarily due to its effect on the flow resistance in the entire airway, which is found to be related to the area ratio of glottis to SGS. Below the SGS severity of 75%, which corresponds to an area ratio of glottis to SGS of 0.1, changing the SGS severity only causes very small changes in the area ratio; therefore, its effect on the flow resistance and dynamics is very small. Beyond the SGS severity of 75%, increasing the SGS severity, leads to rapid increases of the area ratio, resulting in rapid changes in the flow resistance and dynamics.

Finite-element modeling of vocal fold self-oscillations in interaction with vocal tract: Comparison of incompressible and compressible flow model

Finite-element modeling of self-sustained vocal fold oscillations during voice production has mostly considered the air as incompressible, due to numerical complexity. This study overcomes this limitation and studies the influence of air compressibility on phonatory pressures, flow and vocal fold vibratory characteristics. A two-dimensional finite-element model is used, which incorporates layered vocal fold structure, vocal fold collisions, large deformations of the vocal fold tissue, morphing the fluid mesh according to the vocal fold motion by the arbitrary Lagrangian-Eulerian approach and vocal tract model of Czech vowel [i:] based on data from magnetic resonance images. Unsteady viscous compressible or incompressible airflow is described by the Navier-Stokes equations. An explicit coupling scheme with separated solvers for structure and fluid domain was used for modeling the fluid-structure-acoustic interaction. Results of the simulations show clear differences in the glottal flow and vocal fold vibration waveforms between the incompressible and compressible fluid flow. These results provide the evidence on the existence of the coupling between the vocal tract acoustics and the glottal flow (Level 1 interactions), as well as between the vocal tract acoustics and the vocal fold vibrations (Level 2 interactions).

A Methodology to Differentiate Parkinson’s Disease and Aging Speech Based on Glottal Flow Acoustic Analysis

Speech is controlled by axial neuromotor systems, therefore, it is highly sensitive to the effects of neurodegenerative illnesses such as Parkinson’s Disease (PD). Patients suffering from PD present important alterations in speech, which are manifested in phonation, articulation, prosody, and fluency. These alterations may be evaluated using statistical methods on features obtained from glottal, spectral, cepstral, or fractal descriptions of speech. This work introduces an evaluation paradigm based on Information Theory (IT) to differentiate the effects of PD and aging on glottal amplitude distributions. The study is conducted on a database including 48 PD patients (24 males, 24 females), 48 age-matched healthy controls (HC, 24 males, 24 females), and 48 mid-age normative subjects (NS, 24 males, 24 females). It may be concluded from the study that Hierarchical Clustering (HiCl) methods produce a clear separation between the phonation of PD patients from NS subjects (accuracy of 89.6% for both male and female subsets), but the separation between PD patients and HC subjects is less efficient (accuracy of 75.0% for the male subset and 70.8% for the female subset). Conversely, using feature selection and Support Vector Machine (SVM) classification, the differentiation between PD and HC is substantially improved (accuracy of 94.8% for the male subset and 92.8% for the female subset). This improvement was mainly boosted by feature selection, at a cost of information and generalization losses. The results point to the possibility that speech deterioration may affect HC phonation with aging, reducing its difference to PD phonation.