Journal article
A Hybrid Machine-Learning-Based Method for Analytic Representation of the Vocal Fold Edges during Connected Speech
Applied sciences, Vol.11(3), pp.1-15
02/01/2021
DOI: 10.3390/app11031179
PMCID: PMC7954580
PMID: 33717604
Abstract
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.
Details
- Title: Subtitle
- A Hybrid Machine-Learning-Based Method for Analytic Representation of the Vocal Fold Edges during Connected Speech
- Creators
- Ahmed M. Yousef - Michigan State UniversityDimitar D. Deliyski - Michigan State UniversityStephanie R. C. Zacharias - Mayo Clinic in ArizonaAlessandro de Alarcon - University of Cincinnati Medical CenterRobert F. Orlikoff - East Carolina UniversityMaryam Naghibolhosseini - Michigan State University
- Resource Type
- Journal article
- Publication Details
- Applied sciences, Vol.11(3), pp.1-15
- DOI
- 10.3390/app11031179
- PMID
- 33717604
- PMCID
- PMC7954580
- NLM abbreviation
- Appl Sci (Basel)
- ISSN
- 2076-3417
- eISSN
- 2076-3417
- Publisher
- Mdpi
- Number of pages
- 15
- Grant note
- K01DC017751; R01-DC007640 / National Institutes of Health, NIDCD; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA; NIH National Institute on Deafness & Other Communication Disorders (NIDCD)
- Language
- English
- Date published
- 02/01/2021
- Academic Unit
- Communication Sciences and Disorders
- Record Identifier
- 9984721123702771
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