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Review Article| Volume 37, ISSUE 3, P465.e1-465.e18, May 2023

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Biomechanical Models to Represent Vocal Physiology: A Systematic Review

      SUMMARY

      Biomechanical modeling allows obtaining information on physical phenomena that cannot be directly observed. This study aims to review models that represent voice production. A systematic review of the literature was conducted using PubMed/Medline, SCOPUS, and IEEE Xplore databases. To select the papers, we used the protocol PRISMA Statement. A total of 53 publications were included in this review. This article considers a taxonomic classification of models found in the literature. We propose four categories in the taxonomy: (1) Models representing the Source (Vocal folds); (2) Models representing the Filter (Vocal Tract); (3) Models representing the Source - Filter Interaction; and (4) Models representing the Airflow - Source Interaction. We include a bibliographic analysis with the evolution of the publications per category. We provide an analysis of the number as well of publications in journals per year. Moreover, we present an analysis of the term occurrence and its frequency of usage, as found in the literature. In each category, different types of vocal production models are mentioned and analyzed. The models account for the analysis of evidence about aerodynamic, biomechanical, and acoustic phenomena and their correlation with the physiological processes involved in the production of the human voice. This review gives an insight into the state of the art related to the mathematical modeling of voice production, analyzed from the viewpoint of vocal physiology.

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      References

        • Zhang Z.
        Mechanics of human voice production and control.
        J Acoust Soc Am. 2016; 140: 2614-2635https://doi.org/10.1121/1.4964509
        • Becker S.
        • Kniesburges S.
        • Müller S.
        • et al.
        Flow-structure-acoustic interaction in a human voice model.
        J Acoust Soc Am. 2009; 125: 1351-1361https://doi.org/10.1121/1.3068444
        • Kniesburges S.
        • Hesselmann C.
        • Becker S.
        • et al.
        Influence of vortical flow structures on the glottal jet location in the supraglottal region.
        J Voice. 2013; 27: 531-544https://doi.org/10.1016/j.jvoice.2013.04.005
        • Link G.
        • Kaltenbacher M.
        • Breuer M.
        • et al.
        A 2D finite-element scheme for fluid-solid-acoustic interactions and its application to human phonation.
        Comput Methods Appl Mech Eng. 2009; 198: 3321-3334https://doi.org/10.1016/j.cma.2009.06.009
        • Calvache C.
        • Guzman M.
        • Bobadilla M.
        • et al.
        Variation on vocal economy after different semioccluded vocal tract exercises in subjects with normal voice and dysphonia.
        J Voice. 2019; https://doi.org/10.1016/j.jvoice.2019.01.007
        • Alipour F.
        • Brücker C.
        • Cook D.D.
        • et al.
        Mathematical models and numerical schemes for the simulation of human phonation.
        . 2011; 43: 323-343
        • Calawerts W.M.
        • Lin L.
        • Sprott J.
        • et al.
        Using rate of divergence as an objective measure to differentiate between voice signal types based on the amount of disorder in the signal.
        J Voice. 2017; 31: 16-23https://doi.org/10.1016/j.jvoice.2016.01.005
        • Galindo G.E.
        • Peterson S.D.
        • Erath B.D.
        • et al.
        Modeling the pathophysiology of phonotraumatic vocal hyperfunction with a triangular glottal model of the vocal folds.
        J Speech Lang Hearing Res. 2017; 60: 2452-2471https://doi.org/10.1044/2017_jslhr-s-16-0412
        • Cortés J.P.
        • Espinoza V.M.
        • Ghassemi M.
        • et al.
        Ambulatory assessment of phonotraumatic vocal hyperfunction using glottal airflow measures estimated from neck-surface acceleration.
        PLoS ONE. 2018; 13https://doi.org/10.1371/journal.pone.0209017
        • Palaparthi A.
        • Smith S.
        • Mau T.
        • et al.
        A computational study of depth of vibration into vocal fold tissues.
        J Acoust Soc Am. 2019; 145: 881-891https://doi.org/10.1121/1.5091099
        • Liu B.
        • Polce E.
        • Raj H.
        • et al.
        Quantification of voice type components present in human phonation using a modified diffusive chaos technique.
        Ann Otol Rhinol Laryngol. 2019; 128: 921-931https://doi.org/10.1177/0003489419848451
        • Valero-Cuevas F.J.
        • Anand V.V.
        • Saxena A.
        • et al.
        Beyond parameter estimation: extending biomechanical modeling by the explicit exploration of model topology.
        IEEE Trans Biomed Eng. 2007; 54: 1951-1964https://doi.org/10.1109/tbme.2007.906494
        • Zañartu M.
        • Mongeau L.
        • Wodicka G.R.
        Influence of acoustic loading on an effective single mass model of the vocal folds.
        J Acoust Soc Am. 2007; 121: 1119-1129https://doi.org/10.1121/1.2409491
      1. January
        • Zhang Z.
        Regulation of glottal closure and airflow in a three-dimensional phonation model: implications for vocal intensity control.
        J Acoust Soc Am. 2015; 137: 898-910https://doi.org/10.1121/1.4906272
        • Berry D.A.
        • Herzel H.
        • Titze I.R.
        • et al.
        Interpretation of biomechanical simulations of normal and chaotic vocal fold oscillations with empirical eigenfunctions.
        J Acoust Soc Am. 1994; 95: 3595-3604https://doi.org/10.1121/1.409875
        • Titze I.R.
        Nonlinear source filter coupling in phonation: theory.
        J Acoust Soc Am. 2008; 123: 2733-2749https://doi.org/10.1121/1.2832337
        • Story B.H.
        • Titze I.R.
        Voice simulation with a body-cover model of the vocal folds.
        J Acoust Soc Am. 1995; 97: 1249-1260https://doi.org/10.1121/1.412234
        • Story B.H.
        An overview of the physiology, physics and modeling of the sound source for vowels.
        Acoust Sci Technol. 2002; 23: 195-206https://doi.org/10.1250/ast.23.195
        • Hunter E.J.
        • Alipour F.
        • Titze I.R.
        Sensitivity of elastic properties to measurement uncertainties in laryngeal muscles with implications for voice fundamental frequency prediction.
        J Voice. 2007; 21: 641-650https://doi.org/10.1016/j.jvoice.2006.06.004
        • Story B.H.
        Mechanisms of voice production.
        The Handbook of Speech Production. John Wiley & Sons, Inc, 2015: 34-58https://doi.org/10.1002/9781118584156.ch3
        • Horáček J.
        • Šidlof P.
        • Švec J.
        Numerical simulation of self-oscillations of human vocal folds with hertz model of impact forces.
        J Fluids Struct. 2005; 20: 853-869https://doi.org/10.1016/j.jfluidstructs.2005.05.003
        • Doellinger M.
        The Next Step in Voice Assessment: High-Speed Digital Endoscopy and Objective Evaluation, Current Bioinformatics. 2009; 42: 101-111https://doi.org/10.2174/157489309788184774
        • Woo P.
        Objective measures of laryngeal imaging: what have we learned since dr. paul moore.
        J Voice. 2014; 28: 69-81https://doi.org/10.1016/j.jvoice.2013.02.001
        • Herbst C.T.
        Performance evaluation of subharmonic-to-Harmonic ratio (SHR) computation.
        J Voice. 2020; https://doi.org/10.1016/j.jvoice.2019.11.005
        • Krishnamurthy R.
        • Ramani S.A.
        Aerodynamic and acoustic characteristics of voice in children with down syndrome-a systematic review.
        Int J Pediatr Otorhinolaryngol. 2020; 133: 109946https://doi.org/10.1016/j.ijporl.2020.109946
        • Popolo P.S.
        • Johnson A.M.
        Relating cepstral peak prominence to cyclical parameters of vocal fold vibration from high-Speed videoendoscopy using machine learning: A Pilot study.
        J Voice. 2020; https://doi.org/10.1016/j.jvoice.2020.01.026
        • Calvache C.
        • Guzman M.
        • Romero L.
        • et al.
        Do different semi-Occluded voice exercises affect differently vocal fold adduction in subjects diagnosed with functional dysphonia?.
        Pan European Voice Conference Abstract Book. 2015: 136
        • Park M.C.
        Understanding the multi-mass model and sound generation of vocal fold oscillation.
        AIP Adv. 2019; 9https://doi.org/10.1063/1.5113911
        • Zhang Y.
        • Zheng X.
        • Xue Q.
        A deep neural network based glottal flow model for predicting fluid-Structure interactions during voice production.
        Appl Sci. 2020; 10: 705https://doi.org/10.3390/app10020705
        • Tao C.
        • Jiang J.J.
        Mechanical stress during phonation in a self-oscillating finite-element vocal fold model.
        J Biomech. 2007; 40: 2191-2198https://doi.org/10.1016/j.jbiomech.2006.10.030
        • Tao C.
        • Jiang J.J.
        • Zhang Y.
        Simulation of vocal fold impact pressures with a self-oscillating finite-element model.
        J Acoust Soc Am. 2006; 119: 3987-3994https://doi.org/10.1121/1.2197798
        • Zhang X.
        • Gu L.
        • Wei W.
        Pathological voice source analysis system using a flow waveform-matched biomechanical model.
        Appl Bionics Biomech. 2018; 2018https://doi.org/10.1155/2018/3158439
        • Horáček J.
        • Laukkanen A.M.
        • Šidlof P.
        Estimation of impact stress using an aeroelastic model of voice production.
        Logopedics Phoniatr Vocol. 2007; 32: 185-192https://doi.org/10.1080/14015430600628039
        • Kniesburges S.
        • Thomson S.L.
        • Barney A.
        • et al.
        In vitro experimental investigation of voice production.
        Curr Bioinform. 2011; 6: 305-322https://doi.org/10.2174/157489311796904637
        • Horáek J.
        • Laukkanen A.M.
        • Šidlof P.
        • et al.
        Comparison of acceleration and impact stress as possible loading factors in phonation: a computer modeling study.
        Folia Phoniatrica et Logopaedica. 2009; 61: 137-145https://doi.org/10.1159/000219949
        • Tao C.
        • Jiang J.J.
        Chaotic component obscured by strong periodicity in voice production system.
        Phys Rev E - Stat NonlinSoft Matter Phys. 2008; 77: 1-8https://doi.org/10.1103/PhysRevE.77.061922
        • Šidlof P.
        • Zörner S.
        • Hüppe A.
        A hybrid approach to the computational aeroacoustics of human voice production.
        Biomech Model Mechanobiol. 2015; 14: 473-488https://doi.org/10.1007/s10237-014-0617-1
        • Döllinger M.
        • Kniesburges S.
        • Kaltenbacher M.
        • et al.
        Aktuelle methoden zur modellierung des stimmgebungsprozesses.
        HNO. 2016; 64: 82-90https://doi.org/10.1007/s00106-015-0110-x
        • Barney A.
        • Kob M.
        Advanced voice function assessment: editorial introduction to this special issue.
        Logopedics Phoniatr Vocol. 2015; 40: 1-4https://doi.org/10.3109/14015439.2015.1006425
        • Liberati A.
        • Altman D.G.
        • Tetzlaff J.
        • et al.
        The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration.
        Journal of Clinical Epidemiology. 62. Pergamon, 2009: e1-e34https://doi.org/10.1016/j.jclinepi.2009.06.006
        • Cveticanin L.
        Review on mathematical and mechanical models of the vocal cord.
        J Appl Math. 2012; 2012https://doi.org/10.1155/2012/928591
        • Ishizaka K.
        • Flanagan J.L.
        Synthesis of voiced sounds from a two mass model of the vocal cords.
        Bell Syst Tech J. 1972; 51: 1233-1268https://doi.org/10.1002/j.1538-7305.1972.tb02651.x
        • Flanagan J.L.
        • Ishizaka K.
        Computer model to characterize the air volume displaced by the vibrating vocal cords.
        J Acoust Soc Am. 1978; 63: 1559-1565
        • Pelorson X.
        • Hirschberg A.
        • van Hassel R.R.
        • et al.
        Theoretical and experimental study of quasisteady-flow separation within the glottis during phonation. application to a modified two-mass modela.
        J Acoust Soc Am. 1994; 96: 3416-3431https://doi.org/10.1121/1.411449
        • Childers D.G.
        • Wong C.F.
        Measuring and modeling vocal source-Tract interaction.
        IEEE Trans Biomed Eng. 1994; 41: 663-671https://doi.org/10.1109/10.301733
        • Farley G.R.
        A quantitative model of voice F0 control a locus cartilage.
        . 1994; 95: 1017-1029
        • Zhang Z.
        Compensation strategies in voice production with glottal insufficiency.
        J Voice. 2019; 33: 96-102https://doi.org/10.1016/j.jvoice.2017.10.002
        • Zhang Z.
        Effect of vocal fold stiffness on voice production in a three-dimensional body-cover phonation model.
        J Acoust Soc Am. 2017; 142: 2311-2321https://doi.org/10.1121/1.5008497
        • Jones C.L.
        • Achuthan A.
        • Erath B.D.
        Modal response of a computational vocal fold model with a substrate layer of adipose tissue.
        J Acoust Soc Am. 2015; 137: EL158-EL164https://doi.org/10.1121/1.4905892
        • Gunter H.E.
        A mechanical model of vocal-fold collision with high spatial and temporal resolution.
        J Acoust Soc Am. 2003; 113: 994-1000https://doi.org/10.1121/1.1534100
        • Samlan R.A.
        • Story B.H.
        • Bunton K.
        Relation of perceived breathiness to laryngeal kinematics and acoustic measures based on computational modeling.
        J Speech Lang Hear Res. 2013; 56: 1209-1223https://doi.org/10.1044/1092-4388(2012/12-0194)
        • Šidlof P.
        • Švec J.G.
        • Horáček J.
        • et al.
        Geometry of human vocal folds and glottal channel for mathematical and biomechanical modeling of voice production.
        J Biomech. 2008; 41: 985-995https://doi.org/10.1016/j.jbiomech.2007.12.016
        • Tao C.
        • Jiang J.J.
        A self-oscillating biophysical computer model of the elongated vocal fold.
        Comput Biol Med. 2008; 38: 1211-1217https://doi.org/10.1016/j.compbiomed.2008.10.001
        • Manriquez R.
        • Peterson S.D.
        • Prado P.
        • et al.
        Neurophysiological muscle activation scheme for controlling vocal fold models.
        IEEE Trans Neural Syst Rehabil Eng. 2019; 27: 1043-1052https://doi.org/10.1109/TNSRE.2019.2906030
        • Cataldo E.
        • Soize C.
        Stochastic mechanical model of vocal folds for producing jitter and for identifying pathologies through real voices.
        J Biomech. 2018; 74: 126-133https://doi.org/10.1016/j.jbiomech.2018.04.031
        • Samlan R.A.
        • Story B.H.
        Influence of left right asymmetries on voice quality in simulated paramedian vocal fold paralysis.
        J Speech Lang Hearing Res. 2017; 60: 306-321https://doi.org/10.1044/2016_JSLHR-S-16-0076
        • Cataldo E.
        • Soize C.
        Voice signals produced with jitter through a stochastic one-mass mechanical model.
        J Voice. 2017; 31: 111.e9-111.e18https://doi.org/10.1016/j.jvoice.2016.01.001
        • Erath B.D.
        • Zañartu M.
        • Peterson S.D.
        Modeling viscous dissipation during vocal fold contact: the influence of tissue viscosity and thickness with implications for hydration.
        Biomech Model Mechanobiol. 2017; 16: 947-960https://doi.org/10.1007/s10237-016-0863-5
        • Pinheiro A.P.
        • Kerschen G.
        Vibrational dynamics of vocal folds using nonlinear normal modes.
        Med Eng Phys. 2013; 35: 1079-1088https://doi.org/10.1016/j.medengphy.2012.11.002
        • Gunter H.E.
        Modeling mechanical stresses as a factor in the etiology of benign vocal fold lesions.
        J Biomech. 2004; 37: 1119-1124https://doi.org/10.1016/j.jbiomech.2003.11.007
        • Erath B.D.
        • Peterson S.D.
        • Weiland K.S.
        • et al.
        An acoustic source model for asymmetric intraglottal flow with application to reduced-order models of the vocal folds.
        PLoS One. 2019; 14: e0219914https://doi.org/10.1371/journal.pone.0219914
        • Assaneo M.F.
        • Trevisan M.A.
        Revisiting the two-mass model of the vocal folds.
        Pap Phys. 2013; 5050004–1–050004–7https://doi.org/10.4279/PIP.050004
        • Bailly L.
        • Henrich N.
        • Pelorson X.
        Vocal fold and ventricular fold vibration in period-doubling phonation: physiological description and aerodynamic modeling.
        J Acoust Soc Am. 2010; 127: 3212-3222https://doi.org/10.1121/1.3365220
        • Adachi S.
        • Yu J.
        Two-dimensional model of vocal fold vibration for sound synthesis of voice and soprano singing.
        J Acoust Soc Am. 2005; 117: 3213-3224https://doi.org/10.1121/1.1861592
        • Zhang Y.
        • Jiang J.
        • Rahn D.A.
        Studying vocal fold vibrations in Parkinson’s disease with a nonlinear model.
        Chaos. 2005; 15https://doi.org/10.1063/1.1916186
        • Lucero J.C.
        • Koenig L.L.
        Simulations of temporal patterns of oral airflow in men and women using a two-mass model of the vocal folds under dynamic control.
        J Acoust Soc Am. 2005; 117: 1362-1372https://doi.org/10.1121/1.1853235
        • LaMar M.D.
        • Qi Y.
        • Xin J.
        Modeling vocal fold motion with a hydrodynamic semicontinuum model.
        J Acoust Soc Am. 2003; 114: 455-464https://doi.org/10.1121/1.1577547
        • Laje R.
        • Gardner T.
        • Mindlin G.B.
        Continuous model for vocal fold oscillations to study the effect of feedback.
        Phys Rev E - Stat Phys PlasmasFluids Rel Interdiscip Top. 2001; 64: 7https://doi.org/10.1103/PhysRevE.64.056201
        • Zhang K.
        • Siegmund T.
        • Chan R.W.
        • Fu M.
        Predictions of fundamental frequency changes during phonation based on a biomechanical model of the vocal fold lamina propria.
        J Voice. 2009; 23: 277-282https://doi.org/10.1016/j.jvoice.2007.09.010
        • Pickup B.A.
        • Thomson S.L.
        Influence of asymmetric stiffness on the structural and aerodynamic response of synthetic vocal fold models.
        J Biomech. 2009; 42: 2219-2225https://doi.org/10.1016/j.jbiomech.2009.06.039
        • Fleischer M.
        • Mainka A.
        • Kürbis S.
        • Birkholz P.
        How to precisely measure the volume velocity transfer function of physical vocal tract models by external excitation.
        PLoS ONE. 2018; 13: 1-16https://doi.org/10.1371/journal.pone.0193708
        • Vampola T.
        • Horáček J.
        • Laukkanen A.M.
        • et al.
        Human vocal tract resonances and the corresponding mode shapes investigated by three-dimensional finite-element modelling based on CT measurement.
        Logoped Phoniatr Vocol. 2015; 40: 14-23https://doi.org/10.3109/14015439.2013.775333
        • Vampola T.
        • Laukkanen A.-M.
        • Horáček J.
        • et al.
        Vocal tract changes caused by phonation into a tube: a case study using computer tomography and finite-element modeling.
        J Acoust Soc Am. 2011; 129: 310-315https://doi.org/10.1121/1.3506347
        • Švancara P.
        • Horácek J.
        • Vokrál J.
        • et al.
        Computational modelling of effect of tonsillectomy on voice production.
        Logoped Phoniatr Vocol. 2006; 31: 117-125https://doi.org/10.1080/14015430500342277
        • Yokota K.
        • Ishikawa S.
        • Koba Y.
        • et al.
        Inverse analysis of vocal sound source using an analytical model of the vocal tract.
        Appl Acoust. 2019; 150: 89-103https://doi.org/10.1016/j.apacoust.2019.02.005
        • Delebecque L.
        • Pelorson X.
        • Beautemps D.
        Modeling of aerodynamic interaction between vocal folds and vocal tract during production of a vowel voiceless plosive vowel sequence.
        J Acoust Soc Am. 2016; 139: 350-360https://doi.org/10.1121/1.4939115
        • Smith B.L.
        • Nemcek S.P.
        • Swinarski K.A.
        • et al.
        Nonlinear source-filter coupling due to the addition of a simplified vocal tract model for excised larynx experiments.
        J Voice. 2013; 27: 261-266https://doi.org/10.1016/j.jvoice.2012.12.012
        • Chen L.-J.
        • Mongeau L.
        Verification of two minimally invasive methods for the estimation of the contact pressure in human vocal folds during phonation.
        J Acoust Soc Am. 2011; 130: 1618-1627https://doi.org/10.1121/1.3613708
        • Van Der Plaats A.
        • Schutte H.K.
        • Van Der Eerden F.J.
        • et al.
        An in-vitro test set-up for evaluation of a voice-producing element under physiologic acoustic conditions.
        Ann Biomed Eng. 2006; 34: 893-900https://doi.org/10.1007/s10439-006-9083-y
        • Kaburagi T.
        Voice production model integrating boundary-layer analysis of glottal flow and source-filter coupling.
        J Acoust Soc Am. 2011; 129: 1554-1567https://doi.org/10.1121/1.3533732
        • Hirschberg A.
        Comments on “a theoretical model of the pressure field arising from asymmetric intraglottal flows applied to a two-mass model of the vocal folds” [j. acoust. soc. am. 130, 389–403 (2011)].
        J Acoust Soc Am. 2013; 134: 9-12https://doi.org/10.1121/1.4807816
        • Tokuda I.T.
        • Shimamura R.
        Effect of level difference between left and right vocal folds on phonation: physical experiment and theoretical study.
        J Acoust Soc Am. 2017; 142: 482-492https://doi.org/10.1121/1.4996105
        • Robertson D.
        • Zañartu M.
        • Cook D.
        Comprehensive, population-Based sensitivity analysis of a two-Mass vocal fold model.
        PLoS ONE. 2016; 11: 1-19https://doi.org/10.1371/journal.pone.0148309
        • Horáček J.
        • Laukkanen A.M.
        • Šidlof P.
        Estimation of impact stress using an aeroelastic model of voice production.
        Logoped Phoniatr Vocol. 2007; 32: 185-192https://doi.org/10.1080/14015430600628039
        • Kucinschi B.R.
        • Scherer R.C.
        • DeWitt K.J.
        • et al.
        An experimental analysis of the pressures and flows within a driven mechanical model of phonation.
        J Acoust Soc Am. 2006; 119: 3011-3021https://doi.org/10.1121/1.2186429
        • Triep M.
        • Brücker C.
        • Stingl M.
        • et al.
        Optimized transformation of the glottal motion into a mechanical model.
        Med Eng Phys. 2011; 33: 210-217https://doi.org/10.1016/j.medengphy.2010.09.019
        • Sadeghi H.
        • Kniesburges S.
        • Kaltenbacher M.
        • et al.
        Computational models of laryngeal aerodynamics: potentials and numerical costs.
        Journal of Voice. 2019; 33: 385-400https://doi.org/10.1016/j.jvoice.2018.01.001
        • Jiang W.
        • Xue Q.
        • Zheng X.
        Effect of longitudinal variation of vocal fold inner layer thickness on fluid-structure interaction during voice production.
        J Biomech Eng. 2018; 140: 1-9https://doi.org/10.1115/1.4041045
        • Zhang Z.
        Cause-effect relationship between vocal fold physiology and voice production in a three-dimensional phonation model.
        J Acoust Soc Am. 2016; 139: 1493-1507https://doi.org/10.1121/1.4944754
        • Mendelsohn A.H.
        • Xuan Y.
        • Zhang Z.
        Voice outcomes following laser cordectomy for early glottic cancer: a physical model investigation.
        Laryngoscope. 2014; 124: 1882-1886https://doi.org/10.1002/lary.24563
        • Triep M.
        • Brücker C.
        Three-dimensional nature of the glottal jet.
        J Acoust Soc Am. 2010; 127: 1537-1547https://doi.org/10.1121/1.3299202
        • Li S.
        • Wan M.X.
        • Wang S.P.
        The effects of the false vocal fold gaps on intralaryngeal pressure distributions and their effects on phonation.
        Sci China, Ser C Life Sci. 2008; 51: 1045-1051https://doi.org/10.1007/s11427-008-0128-3
        • Nicollas R.
        • Giordano J.
        • Perrier P.
        • et al.
        Modelling sound production from an aerodynamical model of the human newborn larynx.
        Biomed Signal Process Control. 2006; 1: 102-106https://doi.org/10.1016/j.bspc.2006.08.003
        • Hunter E.J.
        • Titze I.R.
        • Alipour F.
        A three-dimensional model of vocal fold abduction/adduction.
        J Acoust Soc Am. 2004; 115: 1747-1759https://doi.org/10.1121/1.1652033
        • Howe M.S.
        • McGowan R.S.
        Aerodynamic sound of a body in arbitrary, deformable motion, with application to phonation.
        J Sound Vib. 2013; 332: 3909-3923https://doi.org/10.1016/j.jsv.2012.11.009
        • Howe M.S.
        • McGowan R.S.
        On the single-mass model of the vocal folds.
        Fluid Dyn Res. 2010; 42https://doi.org/10.1088/0169-5983/42/1/015001
        • Gömmel A.
        • Butenweg C.
        • Bolender K.
        • et al.
        A muscle controlled finite-element model of laryngeal abduction and adduction.
        Comput Methods Biomech Biomed Engin. 2007; 10: 377-388https://doi.org/10.1080/10255840701550923
        • Chhetri D.K.
        • Berke G.S.
        • Lotfizadeh A.
        • et al.
        Control of vocal fold cover stiffness by laryngeal muscles: a preliminary study.
        Laryngoscope. 2009; 119: 222-227https://doi.org/10.1002/lary.20031
        • Berry D.A.
        • Zhang Z.
        • Neubauer J.
        • et al.
        Mechanisms of irregular vibration in a physical model of the vocal folds.
        J Acoust Soc Am. 2006; 120: EL36-EL42https://doi.org/10.1121/1.2234519
        • Haji T.
        • Mori K.
        • Omori K.
        • et al.
        Experimental studies on the viscoelasticity of the vocal fold.
        Acta Otolaryngol. 1992; 112: 151-159https://doi.org/10.3109/00016489209100797
        • Chan R.W.
        • Titze I.R.
        Viscoelastic shear properties of human vocal fold mucosa: measurement methodology and empirical results.
        J Acoust Soc Am. 1999; 106: 2008-2021https://doi.org/10.1121/1.427947
        • Chan R.W.
        • Rodriguez M.L.
        A simple-shear rheometer for linear viscoelastic characterization of vocal fold tissues at phonatory frequencies.
        J Acoust Soc Am. 2008; 124: 1207-1219https://doi.org/10.1121/1.2946715
        • de Vries M.P.
        • Hamburg M.C.
        • Schutte H.K.
        • et al.
        Numerical simulation of self-sustained oscillation of a voice-producing element based on navier stokes equations and the finite element method.
        J Acoust Soc Am. 2003; 113: 2077-2083https://doi.org/10.1121/1.1560163
        • Alipour F.
        • Scherer R.C.
        Flow separation in a computational oscillating vocal fold model.
        J Acoust Soc Am. 2004; 116: 1710-1719https://doi.org/10.1121/1.1779274
        • Harnisch W.
        • Brosch S.
        • Schmidt M.
        • et al.
        Breathing and voice quality after surgical treatment for bilateral vocal cord paralysis.
        Archives of Otolaryngology - Head and Neck Surgery. 2008; 134: 278-284https://doi.org/10.1001/archoto.2007.44
        • Nandamudi S.
        • Scherer R.C.
        Airflow vibrato: dependence on pitch and loudness.
        Journal of Voice. 2019; 33: 815-830https://doi.org/10.1016/j.jvoice.2018.05.007
        • Gilman M.
        • Maira C.
        • Hapner E.R.
        Airflow patterns of running speech in patients with voice disorders.
        Journal of Voice. 2019; 33: 277-283https://doi.org/10.1016/j.jvoice.2017.12.004
        • Graham E.
        • Angadi V.
        • Sloggy J.
        • et al.
        Contribution of glottic insufficiency to perceived breathiness in classically trained singers.
        Med Probl Perform Art. 2016; 31: 179-184https://doi.org/10.21091/mppa.2016.3032
        • Titze I.R.
        Sensitivity of odd-harmonic amplitudes to open quotient and skewing quotient in glottal airflow.
        J Acoust Soc Am. 2015; 137: 502-504https://doi.org/10.1121/1.4904539
        • Mehta D.D.
        • Espinoza V.M.
        • Van Stan J.H.
        • et al.
        The difference between first and second harmonic amplitudes correlates between glottal airflow and neck-surface accelerometer signals during phonation.
        J Acoust Soc Am. 2019; 145: EL386-EL392https://doi.org/10.1121/1.5100909
        • Gilman M.
        • Petty B.
        • Maira C.
        • et al.
        Aerodynamic patterns in patients with voice disorders: a retrospective study.
        J Voice. 2017; 31: 545-549https://doi.org/10.1016/j.jvoice.2016.11.001
        • Grillo E.U.
        A phrase captures aerodynamic and acoustic data in healthy voice users and in patients with voice disorders.
        Logoped Phoniatr Vocol. 2020; 45: 24-29https://doi.org/10.1080/14015439.2018.1545866
        • Mehta D.D.
        • Stan J.H.V.
        • Zañartu M.
        • et al.
        Using ambulatory voice monitoring to investigate common voice disorders: research update.
        Front Bioeng Biotechnol. 2015; 3https://doi.org/10.3389/fbioe.2015.00155
        • Dastolfo C.
        • Gartner-Schmidt J.
        • Yu L.
        • et al.
        Aerodynamic outcomes of four common voice disorders: moving toward disorder-Specific assessment.
        J Voice. 2016; 30: 301-307https://doi.org/10.1016/j.jvoice.2015.03.017
      2. B.S. Zörner, Numerical simulation method for a precise calculation of the human phonation under realistic conditions(2014) 7–8.

        • Sidlof P.
        • Horacek J.
        • Ridky V.
        Parallel CFD simulation of flow in a 3D model of vibrating human vocal folds.
        Comput Fluids. 2013; 80: 290-300https://doi.org/10.1016/j.compfluid.2012.02.005
        • Alipour F.
        • Scherer R.C.
        Vocal fold bulging effects on phonation using a biophysical computer model.
        Journal of Voice. 2000; 14: 470-483https://doi.org/10.1016/S0892-1997(00)80004-1
        • Erath B.D.
        • Peterson S.D.
        • Zañartu M.
        • et al.
        A theoretical model of the pressure field arising from asymmetric intraglottal flows applied to a two-mass model of the vocal folds.
        J Acoust Soc Am. 2011; 130: 389-403https://doi.org/10.1121/1.3586785
        • Alipour F.
        • Vigmostad S.
        Measurement of vocal folds elastic properties for continuum modeling.
        J Voice. 2012; 26: 816.e21-816.e29https://doi.org/10.1016/j.jvoice.2012.04.010
        • Fraile R.
        • Evdokimova V.V.
        • Evgrafova K.V.
        • et al.
        Analysis of measured and simulated supraglottal acoustic waves.
        Journal of Voice. 2016; 30: 518-528https://doi.org/10.1016/j.jvoice.2015.08.006
      3. D.M. Boudreaux, Using the Ambulatory Phonation Monitor to measure the vocal parameters of older people with and without Parkinson’s disease(May) (2011).

        • Lin J.Z.
        • Espinoza V.M.
        • Marks K.L.
        • et al.
        Improved subglottal pressure estimation from neck-Surface vibration in healthy speakers producing non-Modal phonation.
        IEEE J Sel Top Signal Process. 2020; 14: 449-460https://doi.org/10.1109/JSTSP.2019.2959267
        • Zanartu M.
        • Ho J.C.
        • Mehta D.D.
        • et al.
        Subglottal impedance-Based inverse filtering of voiced sounds using neck surface acceleration.
        IEEE Trans Audio Speech LangProcess. 2013; 21: 1929-1939https://doi.org/10.1109/TASL.2013.2263138
        • Fang S.H.
        • Tsao Y.
        • Hsiao M.J.
        • et al.
        Detection of pathological voice using cepstrum vectors: a deep learning approach.
        J Voice. 2019; 33: 634-641https://doi.org/10.1016/j.jvoice.2018.02.003
      4. S. Hegde, S. Shetty, S. Rai, et al., A survey on machine learning approaches for automatic detection of voice disorders, 2019. 10.1016/j.jvoice.2018.07.014

        • Pang Y.F.
        • Huang J.
        • Xu B.Z.
        • et al.
        Quantitative analysis of pathological voice and identification with artificial neural network.
        Lin Chung Er Bi Yan Hou Tou Jing Wai Ke Za Zhi. 2016; 31: 100-102https://doi.org/10.13201/j.issn.1001-1781.2017.02.005