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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doaj-73e0aecc3dfa4678a2c64b32aa58a3412021-01-28T00:05:47ZengMDPI AGApplied Sciences2076-34172021-01-01111179117910.3390/app11031179A Hybrid Machine-Learning-Based Method for Analytic Representation of the Vocal Fold Edges during Connected SpeechAhmed M. Yousef0Dimitar D. Deliyski1Stephanie R. C. Zacharias2Alessandro de Alarcon3Robert F. Orlikoff4Maryam Naghibolhosseini5Department of Communicative Sciences and Disorders, Michigan State University, East Lansing, MI 48824, USADepartment of Communicative Sciences and Disorders, Michigan State University, East Lansing, MI 48824, USAMayo Clinic, Scottsdale, AZ 85259, USADivision of Pediatric Otolaryngology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229, USACollege of Allied Health Sciences, East Carolina University, Greenville, NC 27834, USADepartment of Communicative Sciences and Disorders, Michigan State University, East Lansing, MI 48824, USAInvestigating 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.https://www.mdpi.com/2076-3417/11/3/1179high-speed videoendoscopyconnected speechautomated machine-learning-based edge detection |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ahmed M. Yousef Dimitar D. Deliyski Stephanie R. C. Zacharias Alessandro de Alarcon Robert F. Orlikoff Maryam Naghibolhosseini |
spellingShingle |
Ahmed M. Yousef Dimitar D. Deliyski Stephanie R. C. Zacharias Alessandro de Alarcon Robert F. Orlikoff Maryam Naghibolhosseini A Hybrid Machine-Learning-Based Method for Analytic Representation of the Vocal Fold Edges during Connected Speech Applied Sciences high-speed videoendoscopy connected speech automated machine-learning-based edge detection |
author_facet |
Ahmed M. Yousef Dimitar D. Deliyski Stephanie R. C. Zacharias Alessandro de Alarcon Robert F. Orlikoff Maryam Naghibolhosseini |
author_sort |
Ahmed M. Yousef |
title |
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_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_sort |
hybrid machine-learning-based method for analytic representation of the vocal fold edges during connected speech |
publisher |
MDPI AG |
series |
Applied Sciences |
issn |
2076-3417 |
publishDate |
2021-01-01 |
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. |
topic |
high-speed videoendoscopy connected speech automated machine-learning-based edge detection |
url |
https://www.mdpi.com/2076-3417/11/3/1179 |
work_keys_str_mv |
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