## 4 Discussion
The only other study performing the same task that we can compare our work with was our previously developed ACM method [20], which detected the glottal edges accurately in 88% of the vocalizations in the same HSV sample. There are no other known studies of automated vocal fold segmentation of HSV recordings during connected speech. The current study presented several of the vocalizations, where the ACM method failed. The higher accuracy and performance of the hybrid method, as were shownin this study, reveals its superiority over the ACM method. The extracted kymograms of the two vocalizations in which the hybrid method did not perform accurately had extremely dim lighting across most of the frames, which also made the visual detection of the glottal boundaries impossible, making it challenging to create an accurate reference manually. The hybrid method in this study is the first ML-based approach developed for vocal fold segmentation during connected speech. The recently developed deep learning approaches for vocal fold segmentation were all employed for HSV analysis during sustained vocalization with higher image quality [32; 33; 34; 35]. The developed hybrid method is fully automated, while the deep learning techniques required manual labelling of a part of the dataset in order to train the deep neural networks. Moreover, the deep learning methods are all spatial segmentation techniques; however, the hybrid method in this study is a spatiotemporal method that would potentially lead to a higher robustness in case of irregular vocal fold closure. The hybrid method in this study relies on the accurate performance of the developed motion compensation method; however, this is not an issue with the HSV analysis during sustained vocalization due to the little change in the vocal fold location across frames. Since there is no known gold-standard accurate method to fully capture the vocal fold edges from HSV data during connected speech, visual inspection was performed to serve as reference for validating the performance of the developed technique. It should be noted that this study showed the feasibility of the hybrid method for vocal fold edge representation (in HSV data) during connected speech in one participant with no history of voice disorder. This method needs to be tested on more vocally normal participants, also on participants with voice disorders in order to be generalized. Although the promising performance of the hybrid method was shown during vocal fold oscillation, the algorithm did not perform accurately before and after the onset and offset of vocal fold vibration. This was due to the deviation of the motion window from the vocal fold location before and after the oscillation. However, this did not contradict the purpose of the present study, which was to track the edges of the vocal folds during vocalization. In future, the development of an algorithm to automatically detect the edges of the vocal folds when adducted and not vibrating would be valuable in studying laryngeal maneuvers during connected speech. In addition, the proposed approach showed a promising performance for HSV data with the most challenging images, obtained by a color HSV system. This facilitates the future implementation of the proposed method on less challenging monochromatic images since a monochrome camera provides a higher sensitivity and dynamic range with better pixel representation. This will potentially lead to a higher accuracy and faster performance of the hybrid method for monochromatic HSV data. This study aimed to show the feasibility of this approach for color HSV images, which is preferred over monochromatic images by many voice specialists since color images allow them to better evaluate the health of the tissues.