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A Hybrid Machine-Learning-Based Method for Analytic Representation of the Vocal Fold Edges during Connected Speech

Investigating the phonatory processes in connected speech from high-speed videoendoscopy (HSV) demands the accurate detection of the vocal fold edges during vibration. The present paper proposes a new spatio-temporal technique to automatically segment vocal fold edges in HSV data during running spee...

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Autores principales: Yousef, Ahmed M., Deliyski, Dimitar D., Zacharias, Stephanie R. C., de Alarcon, Alessandro, Orlikoff, Robert F., Naghibolhosseini, Maryam
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7954580/
https://www.ncbi.nlm.nih.gov/pubmed/33717604
http://dx.doi.org/10.3390/app11031179
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author Yousef, Ahmed M.
Deliyski, Dimitar D.
Zacharias, Stephanie R. C.
de Alarcon, Alessandro
Orlikoff, Robert F.
Naghibolhosseini, Maryam
author_facet Yousef, Ahmed M.
Deliyski, Dimitar D.
Zacharias, Stephanie R. C.
de Alarcon, Alessandro
Orlikoff, Robert F.
Naghibolhosseini, Maryam
author_sort Yousef, Ahmed M.
collection PubMed
description Investigating the phonatory processes in connected speech from high-speed videoendoscopy (HSV) demands the accurate detection of the vocal fold edges during vibration. The present paper proposes a new spatio-temporal technique to automatically segment vocal fold edges in HSV data during running speech. The HSV data were recorded from a vocally normal adult during a reading of the “Rainbow Passage.” The introduced technique was based on an unsupervised machine-learning (ML) approach combined with an active contour modeling (ACM) technique (also known as a hybrid approach). The hybrid method was implemented to capture the edges of vocal folds on different HSV kymograms, extracted at various cross-sections of vocal folds during vibration. The k-means clustering method, an ML approach, was first applied to cluster the kymograms to identify the clustered glottal area and consequently provided an initialized contour for the ACM. The ACM algorithm was then used to precisely detect the glottal edges of the vibrating vocal folds. The developed algorithm was able to accurately track the vocal fold edges across frames with low computational cost and high robustness against image noise. This algorithm offers a fully automated tool for analyzing the vibratory features of vocal folds in connected speech.
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spelling pubmed-79545802021-03-12 A Hybrid Machine-Learning-Based Method for Analytic Representation of the Vocal Fold Edges during Connected Speech Yousef, Ahmed M. Deliyski, Dimitar D. Zacharias, Stephanie R. C. de Alarcon, Alessandro Orlikoff, Robert F. Naghibolhosseini, Maryam Appl Sci (Basel) Article Investigating the phonatory processes in connected speech from high-speed videoendoscopy (HSV) demands the accurate detection of the vocal fold edges during vibration. The present paper proposes a new spatio-temporal technique to automatically segment vocal fold edges in HSV data during running speech. The HSV data were recorded from a vocally normal adult during a reading of the “Rainbow Passage.” The introduced technique was based on an unsupervised machine-learning (ML) approach combined with an active contour modeling (ACM) technique (also known as a hybrid approach). The hybrid method was implemented to capture the edges of vocal folds on different HSV kymograms, extracted at various cross-sections of vocal folds during vibration. The k-means clustering method, an ML approach, was first applied to cluster the kymograms to identify the clustered glottal area and consequently provided an initialized contour for the ACM. The ACM algorithm was then used to precisely detect the glottal edges of the vibrating vocal folds. The developed algorithm was able to accurately track the vocal fold edges across frames with low computational cost and high robustness against image noise. This algorithm offers a fully automated tool for analyzing the vibratory features of vocal folds in connected speech. 2021-01-27 2021-02 /pmc/articles/PMC7954580/ /pubmed/33717604 http://dx.doi.org/10.3390/app11031179 Text en Submitted for possible open access publication under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Yousef, Ahmed M.
Deliyski, Dimitar D.
Zacharias, Stephanie R. C.
de Alarcon, Alessandro
Orlikoff, Robert F.
Naghibolhosseini, Maryam
A Hybrid Machine-Learning-Based Method for Analytic Representation of the Vocal Fold Edges during Connected Speech
title A Hybrid Machine-Learning-Based Method for Analytic Representation of the Vocal Fold Edges during Connected Speech
title_full A Hybrid Machine-Learning-Based Method for Analytic Representation of the Vocal Fold Edges during Connected Speech
title_fullStr A Hybrid Machine-Learning-Based Method for Analytic Representation of the Vocal Fold Edges during Connected Speech
title_full_unstemmed A Hybrid Machine-Learning-Based Method for Analytic Representation of the Vocal Fold Edges during Connected Speech
title_short A Hybrid Machine-Learning-Based Method for Analytic Representation of the Vocal Fold Edges during Connected Speech
title_sort hybrid machine-learning-based method for analytic representation of the vocal fold edges during connected speech
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7954580/
https://www.ncbi.nlm.nih.gov/pubmed/33717604
http://dx.doi.org/10.3390/app11031179
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