A Deep Learning Approach for Quantifying Vocal Fold Dynamics During Connected Speech Using Laryngeal High-Speed Videoendoscopy.

ABSTRACT:

Purpose

Voice disorders are best assessed by examining vocal fold dynamics in connected speech. This can be achieved using flexible laryngeal high-speed videoendoscopy (HSV), which enables us to study vocal fold mechanics with high temporal details. Analysis of vocal fold vibration using HSV requires accurate segmentation of the vocal fold edges. This article presents an automated deep-learning scheme to segment the glottal area in HSV from which the glottal edges are derived during connected speech.

Method

Using a custom-built HSV system, data were obtained from a vocally healthy participant reciting the "Rainbow Passage." A deep neural network was designed for glottal area segmentation in the HSV data. A recently introduced hybrid approach by the authors was utilized as an

SUBMITTER: Yousef AM 

PROVIDER: S-EPMC9567340 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature