Deep-Learning-Based Representation of Vocal Fold Dynamics in Adductor Spasmodic Dysphonia during Connected Speech in High-Speed Videoendoscopy.

Journal: Journal of voice : official journal of the Voice Foundation

PMID: 36154973

Abstract

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.

Authors

Ahmed M Yousef

Department of Communicative Sciences and Disorders, Michigan State University, East Lansing.
Dimitar D Deliyski

Department of Communicative Sciences and Disorders, Michigan State University, East Lansing.
Stephanie R C Zacharias

Head and Neck Regenerative Medicine Program, Mayo Clinic, Scottsdale, AZ.
Maryam Naghibolhosseini

Department of Communicative Sciences and Disorders, Michigan State University, East Lansing.

Keywords

Adult Biomechanical Phenomena Case-Control Studies Deep Learning Dysphonia Female Humans Image Interpretation, Computer-Assisted Laryngoscopy Male Middle Aged Phonation Predictive Value of Tests Reproducibility of Results Speech Acoustics Speech Production Measurement Vibration Video Recording Vocal Cords Voice Quality

External Resources

View on PubMed Access via DOI PubMed (36154973)

Deep-Learning-Based Representation of Vocal Fold Dynamics in Adductor Spasmodic Dysphonia during Connected Speech in High-Speed Videoendoscopy.

Abstract

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