ArticleJournal of speech, language, and hearing research : JSLHR2022
A Deep Learning Approach for Quantifying Vocal Fold Dynamics During Connected Speech Using Laryngeal High-Speed Videoendoscopy.
Article in Journal of speech, language, and hearing research : JSLHR, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 27 citations in OpenAlex.
- A Deep-Learning Approach for Vocal Fold Pose Estimation in Videoendoscopy.Journal of imaging informatics in medicine · 2026Article
- Feasibility of Real-Time Automated Vocal Fold Motion Tracking for In-Office Laryngoscopy.The Laryngoscope · 2026Article
- Standardisation of an AI-based vocal fold assessment tool on a recurrent respiratory papillomatosis model.Acta otorhinolaryngologica Italica : organo ufficiale della Societa italiana di otorinolaringologia e chirurgia cervico-facciale · 2025Article
- Deep-Learning-Based Representation of Vocal Fold Dynamics in Adductor Spasmodic Dysphonia during Connected Speech in High-Speed Videoendoscopy.Journal of voice : official journal of the Voice Foundation · 2025Article
- Machine Learning Classifiers for Voice Health Assessment under Simulated Room Acoustics.Engineering proceedings · 2025Article
- Screening Voice Disorders: Acoustic Voice Quality Index, Cepstral Peak Prominence, and Machine Learning.Folia phoniatrica et logopaedica : official organ of the International Association of Logopedics and Phoniatrics (IALP) · 2025Article
- Male-female specific changes in voice parameters under varying room acoustics.Proceedings of meetings on acoustics. Acoustical Society of America · 2024Article
- Sociodemographic reporting in videomics research: a review of practices in otolaryngology - head and neck surgery.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2024Review
- Supraglottic Laryngeal Maneuvers in Adductor Laryngeal Dystonia During Connected Speech.Journal of voice : official journal of the Voice Foundation · 2024Article
- Detection of Vocal Fold Image Obstructions in High-Speed Videoendoscopy During Connected Speech in Adductor Spasmodic Dysphonia: A Convolutional Neural Networks Approach.Journal of voice : official journal of the Voice Foundation · 2024Article
- Reconstruction of Vocal Fold Medial Surface 3D Trajectories: Effects of Neuromuscular Stimulation and Airflow.The Laryngoscope · 2024Article
- Development and Application of Automated Vocal Fold Tracking Software in a Rat Surgical Model.The Laryngoscope · 2024Article
- Deep Learning Techniques and Imaging in Otorhinolaryngology-A State-of-the-Art Review.Journal of clinical medicine · 2023Review
- Laryngeal Imaging Study of Glottal Attack/Offset Time in Adductor Spasmodic Dysphonia during Connected Speech.Applied sciences (Basel, Switzerland) · 2023Article
- Re-Training of Convolutional Neural Networks for Glottis Segmentation in Endoscopic High-Speed Videos.Applied sciences (Basel, Switzerland) · 2022Article
- Long-term performance assessment of fully automatic biomedical glottis segmentation at the point of care.PloS one · 2022Article
- Mapping 74 years in acoustic analysis of voice disorders: A bibliometric review and future research directions.Journal of communication disordersReview
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
purposeVoice 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.
methodUsing 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 automated labeling tool to train the network on a set of HSV frames, where the glottis region was automatically annotated during vocal fold vibrations. The network was then tested against manually segmented frames using different metrics, intersection over union (IoU), and Boundary F1 (BF) score, and its performance was assessed on various phonatory events on the HSV sequence.
resultsThe designed network was successfully trained using the hybrid approach, without the need for manual labeling, and tested on the manually labeled data. The performance metrics showed a mean IoU of 0.82 and a mean BF score of 0.96. In addition, the evaluation assessment of the network's performance demonstrated an accurate segmentation of the glottal edges/area even during complex nonstationary phonatory events and when vocal folds were not vibrating, thus overcoming the limitations of the previous hybrid approach that could only be applied to the vibrating vocal folds.
conclusionsThe introduced automated scheme guarantees accurate glottis representation in challenging color HSV data with lower image quality and excessive laryngeal maneuvers during all instances of connected speech. This facilitates the future development of HSV-based measures to assess the running vibratory characteristics of the vocal folds in speakers with and without voice disorder. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.19798864.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.