Summary
Objective
Adductor spasmodic dysphonia (AdSD) is a neurogenic dystonia, which causes spasms
of the laryngeal muscles. This disorder mainly affects production of connected speech.
To understand how AdSD affects vocal fold (VF) movements and hence, the speech signal,
it is necessary to study VF kinematics during the running speech. This paper introduces
an automated method for analysis of VF vibrations in AdSD using laryngeal high-speed
videoendoscopy (HSV) in running speech.
Methods
A monochrome HSV system was used to obtain video recordings from vocally normal individuals
and AdSD patients during production of the six CAPE-V sentences and the “Rainbow Passage.”
A deep neural network was designed based on the UNet architecture. The network was
developed for glottal area segmentation in HSV data providing a tool for quantitative
analysis of VF vibrations in both norm and AdSD. The network was trained and validated
using the manually labeled HSV frames. After training the network, the segmentation
quality was quantitatively evaluated against visual analysis results of a test dataset
including segregated HSV frames and a short sequence of VF vibrations in consecutive
frames.
Results
The developed convolutional network was successfully trained and demonstrated an accurate
segmentation on the testing dataset with a mean Intersection over Union (IoU) of 0.81
and a mean Boundary-F1 score of 0.93. Moreover, the visual assessment of the automated technique showed
an accurate detection of the glottal edges/area in the HSV data even with challenging
image quality and excessive laryngeal maneuvers of AdSD patients during the running
speech.
Conclusion
The introduced automated approach provides an accurate representation of the glottal
edges/area during connected speech in HSV data for norm and AdSD patients. This method
facilitates the development of HSV-based measures to quantify VF dynamics in AdSD.
Using HSV to automatically analyze VF vibrations in AdSD can allow for understanding
AdSD vocal mechanisms and characteristics.
Key Words
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Article info
Publication history
Published online: September 23, 2022
Accepted:
August 17,
2022
Publication stage
In Press Corrected ProofIdentification
Copyright
© 2022 The Voice Foundation. Published by Elsevier Inc. All rights reserved.
