Bayesian inference has been previously demonstrated as a viable inverse analysis tool for estimating subject-specific reduced-order model parameters and uncertainties. However, previous studies have relied upon simulated glottal area waveforms with superimposed random noise as the measurement. In practice, high-speed videoendoscopy is used to measure glottal area, which introduces practical imaging effects not captured in simulated data, such as viewing angle, frame rate, and camera resolution. Herein, high-speed videos of the vocal folds were approximated by recording the trajectories of physical vocal fold models controlled by a symmetric body-cover model. Twenty videos were recorded, varying subglottal pressure, cricothyroid activation, and viewing angle, with frame rate and video resolution varied by digital video manipulation. Bayesian inference was used to estimate subglottal pressure and cricothyroid activation from glottal area waveforms extracted from the videos. The resulting estimates show off-axis viewing of 10° can lead to a 10% bias in the estimated subglottal pressure. A viewing model is introduced such that viewing angle can be included as an estimated parameter, which alleviates estimate bias. Frame rate and pixel resolution were found to primarily affect uncertainty of parameter estimates up to a limit where spatial and temporal resolutions were too poor to resolve the glottal area. Since many high-speed cameras have the ability to sacrifice spatial for temporal resolution, the findings herein suggest that Bayesian inference studies employing high-speed video should increase temporal resolutions at the expense of spatial resolution for reduced estimate uncertainties.

1.
J. C.
Lucero
,
K. G.
Lourenço
,
N.
Hermant
,
A.
Van Hirtum
, and
X.
Pelorson
, “
Effect of source–tract acoustical coupling on the oscillation onset of the vocal folds
,”
J. Acoust. Soc. Am.
132
,
403
411
(
2012
).
2.
N.
Ruty
,
X.
Pelorson
,
A.
Van Hirtum
,
I.
Lopez-Arteaga
, and
A.
Hirschberg
, “
An in vitro setup to test the relevance and the accuracy of low-order vocal folds models
,”
J. Acoust. Soc. Am.
121
,
479
490
(
2007
).
3.
M.
Zañartu
,
G. E.
Galindo
,
B. D.
Erath
,
S. D.
Peterson
,
G. R.
Wodicka
, and
R. E.
Hillman
, “
Modeling the effects of a posterior glottal opening on vocal fold dynamics with implications for vocal hyperfunction
,”
J. Acoust. Soc. Am.
136
,
3262
3271
(
2014
).
4.
B. D.
Erath
,
S. D.
Peterson
,
M.
Zañartu
,
G. R.
Wodicka
, and
M. W.
Plesniak
, “
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
).
5.
B. D.
Erath
,
D. E.
Sommer
,
S. D.
Peterson
, and
M.
Zañartu
, “
Nonlinearities in block-type reduced-order vocal fold models with asymmetric tissue properties
,”
Proc. Mtg. Acoust.
19
,
060243
(
2013
).
6.
I.
Steinecke
and
H.
Herzel
, “
Bifurcations in an asymmetric vocal-fold model
,”
J. Acoust. Soc. Am.
97
,
1874
1884
(
1995
).
7.
I. R.
Titze
and
B. H.
Story
, “
Rules for controlling low-dimensional vocal fold models with muscle activation
,”
J. Acoust. Soc. Am.
112
,
1064
1076
(
2002
).
8.
R. A.
Lester
and
B. H.
Story
, “
The effects of physiological adjustments on the perceptual and acoustical characteristics of simulated laryngeal vocal tremor
,”
J. Acoust. Soc. Am.
138
,
953
963
(
2015
).
9.
I. R.
Titze
, “
Bi-stable vocal fold adduction: A mechanism of modal-falsetto register shifts and mixed registration
,”
J. Acoust. Soc. Am.
135
,
2091
2101
(
2014
).
10.
D. D.
Mehta
,
M.
Zañartu
,
T. F.
Quatieri
,
D. D.
Deliyski
, and
R. E.
Hillman
, “
Investigating acoustic correlates of human vocal fold vibratory phase asymmetry through modeling and laryngeal high-speed videoendoscopy
,”
J. Acoust. Soc. Am.
130
,
3999
4009
(
2011
).
11.
D.
Robertson
,
M.
Zañartu
, and
D.
Cook
, “
Comprehensive, population-based sensitivity analysis of a two-mass vocal fold model
,”
PloS One
11
,
e0148309
(
2016
).
12.
E.
Cataldo
,
C.
Soize
, and
R.
Sampaio
, “
Uncertainty quantification of voice signal production mechanical model and experimental updating
,”
Mech. Syst. Signal Process.
40
,
718
726
(
2013
).
13.
M.
Döllinger
,
P.
Gómez
,
R. R.
Patel
,
C.
Alexiou
,
C.
Bohr
, and
A.
Schützenberger
, “
Biomechanical simulation of vocal fold dynamics in adults based on laryngeal high-speed videoendoscopy
,”
PLoS One
12
,
e0187486
(
2017
).
14.
M.
Döllinger
,
U.
Hoppe
,
F.
Hettlich
,
J.
Lohscheller
,
S.
Schuberth
, and
U.
Eysholdt
, “
Vibration parameter extraction from endoscopic image series of the vocal folds
,”
IEEE Trans. Biomed. Eng.
49
,
773
781
(
2002
).
15.
P. J.
Hadwin
,
G. E.
Galindo
,
K. J.
Daun
,
M.
Zañartu
,
B. D.
Erath
,
E.
Cataldo
, and
S. D.
Peterson
, “
Non-stationary Bayesian estimation of parameters from a body cover model of the vocal folds
,”
J. Acoust. Soc. Am.
139
,
2683
2696
(
2016
).
16.
P. J.
Hadwin
and
S. D.
Peterson
, “
An extended Kalman filter approach to non-stationary Bayesian estimation of reduced-order vocal fold model parameters
,”
J. Acoust. Soc. Am.
141
,
2909
2920
(
2017
).
17.
J.
Kaipio
and
E.
Somersalo
,
Statistical and Computational Inverse Problems, Vol. 160
(
Springer-Verlag
,
New York
,
2005
), pp.
1
340
.
18.
R.
Schwarz
,
M.
Döllinger
,
T.
Wurzbacher
,
U.
Eysholdt
, and
J.
Lohscheller
, “
Spatio-temporal quantification of vocal fold vibrations using high-speed videoendoscopy and a biomechanical model
,”
J. Acoust. Soc. Am.
123
,
2717
2732
(
2008
).
19.
T.
Wurzbacher
,
M.
Döllinger
,
R.
Schwarz
,
U.
Hoppe
,
U.
Eysholdt
, and
J.
Lohscheller
, “
Spatiotemporal classification of vocal fold dynamics by a multimass model comprising time-dependent parameters
,”
J. Acoust. Soc. Am.
123
,
2324
2334
(
2008
).
20.
T.
Wurzbacher
,
R.
Schwarz
,
M.
Döllinger
,
U.
Hoppe
,
U.
Eysholdt
, and
J.
Lohscheller
, “
Model-based classification of nonstationary vocal fold vibrations
,”
J. Acoust. Soc. Am.
120
,
1012
1027
(
2006
).
21.
A.
Yang
,
M.
Stingl
,
D. A.
Berry
,
J.
Lohscheller
,
D.
Voigt
,
U.
Eysholdt
, and
M.
Döllinger
, “
Computation of physiological human vocal fold parameters by mathematical optimization of a biomechanical model
,”
J. Acoust. Soc. Am.
130
,
948
964
(
2011
).
22.
P.
Gómez
,
A.
Schützenberger
,
M.
Semmler
, and
M.
Döllinger
, “
Laryngeal pressure estimation with a recurrent neural network
,”
IEEE J. Transl. Eng. Health Med.
7
,
1
11
(
2019
).
23.
B.
Sudret
, “
Uncertainty propagation and sensitivity analysis in mechanical models—Contributions to structural reliability and stochastic spectral methods
,” Habilitationa Diriger des Recherches, Université Blaise Pascal, Clermont-Ferrand, France (
2007
).
24.
E.
Cataldo
,
C.
Soize
,
R.
Sampaio
, and
C.
Desceliers
, “
Probabilistic modeling of a nonlinear dynamical system used for producing voice
,”
Comput. Mech.
43
,
265
275
(
2009
).
25.
R. R.
Patel
,
S. N.
Awan
,
J.
Barkmeier-Kraemer
,
M.
Courey
,
D.
Deliyski
,
T.
Eadie
,
D.
Paul
,
J. G.
Švec
, and
R.
Hillman
, “
Recommended protocols for instrumental assessment of voice: American speech-language-hearing association expert panel to develop a protocol for instrumental assessment of vocal function
,”
Am. J. Speech. Lang. Pathol.
27
,
887
905
(
2018
).
26.
J.
Lohscheller
,
H.
Toy
,
F.
Rosanowski
,
U.
Eysholdt
, and
M.
Döllinger
, “
Clinically evaluated procedure for the reconstruction of vocal fold vibrations from endoscopic digital high-speed videos
,”
Med. Image Anal.
11
,
400
413
(
2007
).
27.
D. D.
Deliyski
and
R. E.
Hillman
, “
State of the art laryngeal imaging: Research and clinical implications
,”
Curr. Opin. Otolaryngol. Head Neck Surg.
18
,
147
152
(
2010
).
28.
B. H.
Story
and
I. R.
Titze
, “
Voice simulation with a body cover model of the vocal folds
,”
J. Acoust. Soc. Am.
97
,
1249
1260
(
1995
).
29.
D. E.
Sommer
, “
Development of a coupled numerical-experimental facility to model the fluid-structure interactions of the human vocal folds
,” MASc, University of Waterloo, Waterloo, ON,
2014
.
30.
R. C.
Scherer
,
D.
Shinwari
,
K. J.
De Witt
,
C.
Zhang
,
B. R.
Kucinschi
, and
A. A.
Afjeh
, “
Intraglottal pressure profiles for a symmetric and oblique glottis with a divergence angle of 10 degrees
,”
J. Acoust. Soc. Am.
109
,
1616
1630
(
2001
).
31.
D. D.
Deliyski
,
M. E. G.
Powell
,
S. R. C.
Zacharias
,
T. T.
Gerlach
, and
A.
De Alarcon
, “
Experimental investigation on minimum frame rate requirements of high-speed videoendoscopy for clinical voice assessment
,”
Biomed. Signal Process. Control
17
,
21
28
(
2015
).
32.
J. N.
Carlson
,
S.
Das
,
F.
de la Torre
,
C. W.
Callaway
,
P. E.
Phrampus
, and
J.
Hodgins
, “
Motion capture measures variability in laryngoscopic movement during endotracheal intubation
,”
Simul. Healthc.
7
,
255
260
(
2012
).
33.
Y.
Zhang
,
E.
Bieging
,
H.
Tsui
, and
J. J.
Jiang
, “
Efficient and effective extraction of vocal fold vibratory patterns from High-Speed Digital Imaging
,”
J. Voice
24
,
21
29
(
2010
).
34.
M. K.
Mobashir
,
A. E. R. S.
Mohamed
,
A. S.
Quriba
,
A. M.
Anany
, and
E. M.
Hassan
, “
Linear measurements of vocal folds and laryngeal dimensions in freshly excised human larynges
,”
J. Voice
32
,
525
528
(
2017
).
35.
See supplementary material at http://dx.doi.org/10.1121/1.5124256 for a sample video and tabulated results for all imaging configurations.
36.
R. E.
Kass
and
A. E.
Raftery
, “
Bayes factors
,”
J. Am. Stat. Assoc.
90
,
773
795
(
1995
).

Supplementary Material

You do not currently have access to this content.