###### 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. high-speed videoendoscopy; connected speech; automated machine-learning-based edge detection +
Footnote †: journal: Applied Sciences  
latter limitation by recording the intra-cycle vibratory characteristics of the vocal folds at high frame rates without depending on the periodicity of the acoustic voice signal [12; 13; 14]. Moreover, through a recent advancement, HSV can be used to objectively measure vocal fold vibratory characteristics with high temporal resolution not only in sustained phonation but also in running speech [15; 16; 17; 18; 19; 20]. Hence, HSV is an effective tool that can improve our understanding of complex physiological and phonatory mechanisms of voice production in ways that are not feasible when using videostroboscopy [12; 13; 21; 22]. Since voice disorders are often revealed in connected speech [23; 24; 25; 26; 27; 28], using HSV in voice assessment can serve as a powerful tool in studying the cycle-to-cycle and intra-cycle vibratory details besides the non-stationary events (e.g., voice breaks, and voicing onsets and offsets) during phonation in connected speech [15; 19; 29; 30; 31]. However, in clinical settings, it is not feasible to navigate through the huge amount of data obtained using HSV without the aid of automated analysis techniques. The development of automated methods for extracting HSV-based measures of vocal fold vibration enables us to acquire clinically relevant vocal fold vibratory characteristics during running speech [32; 33]. The mining of massive HSV datasets requires specialized machine-learning (ML) methods. Using ML, we can classify similar and dissimilar structures and/or discover hidden patterns in the data efficiently with a low computational cost. To study the vibratory characteristics of the vocal folds in connected speech, it is essential to develop algorithms for the detection of the vibrating vocal fold edges during phonation. Developing fully automated spatial segmentation algorithms for such an edge-detection task will facilitate extraction of HSV-based measures during connected speech. Various spatial segmentation methods have been implemented to detect the glottal edges in HSV data in isolated sustained vowels [34; 35; 36; 37; 38; 39; 40], e.g., histogram thresholding [38; 41; 42], seeded region growing procedures [37; 43; 44], level set methods [45; 46], watershed transform [47], and active contour models [39; 40; 48; 49]. Four recent ML techniques based on deep learning have successfully segmented the glottis/vocal fold edges in HSV recordings with high accuracy [32; 33; 34; 35]. The HSV datasets in these studies were recorded during the production of sustained phonation, and not running speech.