Spatial Segmentation of Glottal Area in High-Speed Videoendoscopy during Connected Speech скачать в хорошем качестве

Spatial Segmentation of Glottal Area in High-Speed Videoendoscopy during Connected Speech

Ahmed M. Yousef, M.S., Ph.D. Student, Department of Communicative Sciences and Disorders, Michigan State University, Tel: 517-353-8641, email: [email protected] Dimitar D. Deliyski, Ph.D., Professor and Chair, Department of Communicative Sciences and Disorders, Michigan State University, Tel: 517-353-8780, email: [email protected] Stephanie R.C. Zacharias, Ph.D., CCC-SLP, Research Scientist, Laryngotracheal Regeneration Lab, Mayo Clinic – Arizona, Tel: 480-301-4837, email: [email protected] Alessandro de Alarcon, MD, MPH, Professor, Division of Pediatric Otolaryngology, Cincinnati Children’s Hospital Medical Center, and Department of Otolaryngology – Head and Neck Surgery, University of Cincinnati College of Medicine, Tel: 513-636-4355, email: [email protected] Robert F. Orlikoff, Ph.D., CCC-SLP, Professor and Dean, College of Allied Health Sciences, East Carolina University, Tel: 252-744-6010, email: [email protected] Maryam Naghibolhosseini, Ph.D., Assistant Professor, Department of Communicative Sciences and Disorders, Michigan State University, Tel: 517-884-2256, email: [email protected] ----------- Abstract: This study proposes a new computational framework for spatial segmentation of the glottal area in high-speed videoendoscopy (HSV) data during connected speech. This is done to provide an accurate estimation of the glottal area waveform during vocal-fold vibrations in connected speech. HSV data were obtained from a vocally normal adult during production of the “Rainbow Passage.” An algorithm based on the active contour modeling approach was developed for the analysis of HSV data with high noise levels. The noise present in the HSV recordings was modeled in the developed computational framework. The new algorithm was applied on a series of HSV kymograms at different intersections of the vocal folds in order to detect the edges of the vocal folds. This edge detection method follows a set of deformation rules for energy optimization and eventually converges to the vocal fold edges during connected speech. The detected edges in the kymograms were then registered back to the HSV frames. The glottal area waveform was calculated based on the area of the glottis in each frame. The developed algorithm successfully described the edges of vocal folds in the HSV kymograms. This algorithm captured the glottal area across the HSV frames and lead to accurate measurement of the glottal area waveform. The proposed algorithm can serve as an accurate approach for spatial segmentation of the vocal folds in HSV data during connected speech. This study is one of the initial steps toward developing HSV-based measures to study the mechanisms of voice production and voice disorders in the context of connected speech. Acknowledgments: The authors would like to acknowledge the support from the NIH through NIDCD grant K01DC017751, and the Michigan State University Discretionary Funding Initiative.