Summary
Objective
Adductor spasmodic dysphonia (AdSD) is a neurogenic voice disorder, affecting the
intrinsic laryngeal muscle control. AdSD leads to involuntary laryngeal spasms and
only reveals during connected speech. Laryngeal high-speed videoendoscopy (HSV) coupled
with a flexible fiberoptic endoscope provides a unique opportunity to study voice
production and visualize the vocal fold vibrations in AdSD during speech. The goal
of this study is to automatically detect instances during which the image of the vocal
folds is optically obstructed in HSV recordings obtained during connected speech.
Methods
HSV data were recorded from vocally normal adults and patients with AdSD during reading
of the “Rainbow Passage”, six CAPE-V sentences, and production of the vowel /i/. A
convolutional neural network was developed and trained as a classifier to detect obstructed/unobstructed
vocal folds in HSV frames. Manually labelled data were used for training, validating,
and testing of the network. Moreover, a comprehensive robustness evaluation was conducted
to compare the performance of the developed classifier and visual analysis of HSV
data.
Results
The developed convolutional neural network was able to automatically detect the vocal
fold obstructions in HSV data in vocally normal participants and AdSD patients. The
trained network was tested successfully and showed an overall classification accuracy
of 94.18% on the testing dataset. The robustness evaluation showed an average overall
accuracy of 94.81% on a massive number of HSV frames demonstrating the high robustness
of the introduced technique while keeping a high level of accuracy.
Conclusions
The proposed approach can be used for efficient analysis of HSV data to study laryngeal
maneuvers in patients with AdSD during connected speech. Additionally, this method
will facilitate development of vocal fold vibratory measures for HSV frames with an
unobstructed view of the vocal folds. Indicating parts of connected speech that provide
an unobstructed view of the vocal folds can be used for developing optimal passages
for precise HSV examination during connected speech and subject-specific clinical
voice assessment protocols.
Key Words
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Article info
Publication history
Published online: March 15, 2022
Accepted:
January 30,
2022
Publication stage
In Press Corrected ProofFootnotes
We acknowledge the support from NIH/NIDCD, K01DC017751 “Studying the Laryngeal Mechanisms Underlying Dysphonia in Connected Speech” and Michigan State University Discretionary Funding Initiative.
Identification
Copyright
© 2022 The Voice Foundation. Published by Elsevier Inc. All rights reserved.
