## 3 Results
The top figure illustrates the clustered regions using the two intensities as features and the bottom figure shows the result when using both the gradient  
Figure 4: Detected glottal edges based on the results of the k-means clustering algorithm using (**a**) the green and red channel intensities as features and (**b**) using the gradient along with both red and green channel intensities as three features. Figure 3: Clustered kymogram (from Figure 1h) by employing the k-means clustering algorithm using (**a**) the red and green channel intensities as features and (**b**) the gradient along with the red and green channel intensities as three features. and the intensities (green and red channel intensities) as three features. Figure 3 illustrates the clustered areas on the binary labeled kymogram so that only two distinct colors are shown representing the two clusters obtained. As seen, using the gradient in addition to the intensity features allows us to capture more information about the glottal area, which well aligns with the results obtained from the Figure 2.  
Figure 4 shows the detected edges of the glottal area based on the results of clustering. In this figure, only the glottal cluster region is shown with a white line in the original kymogram (to have a better visual representation of the performance of the clustering method) using the intensity features (Panel (a)) and the gradient and intensity features (Panel (b)). The comparison of Panel (a) and (b) shows the improvement in clustering after adding the gradient feature to the intensity features. As can be seen in this figure, using only intensity features results in missing some spatial information about the glottal area, particularly during the sustained vibration of the vocal folds. On the other hand, the glottal edges were detected more accurately when the gradient feature was used along with both the red and green channel intensities. This improvement is more noticeable during the sustained oscillation of the vocal folds than during the voicing onsets and offsets. The preliminary segmented glottal edges by applying the clustering technique were used as inputs to the ACM method. Figure 5 shows how using k-means clustering as an initialization step for the ACM impacts the accuracy of the method. The results are presented in four kymograms extracted at four different vocalizations. The detected glottal edges using the ACM alone and the developed machine-learning-based hybrid method are shown for two decent quality kymograms (between Frames 40,505 to 41,255 in Panel (b), and 103,942 to 104,577 in Panel (d)) and for two challenging kymograms (between Frames 18,975 to 19,803 in Panel (a), and 98,105 to 98,651 in Panel (c)). The figure depicts the result of applying the ACM method alone (the top figure in each panel) and the hybrid method at each vocalization (the bottom figure in each panel). Although the ACM performed better for the top kymograms in Panel (b) and (d) in comparison with the (more challenging) kymograms at the top of Panel (a) and (c), this method missed the glottal edges for several cycles as seen in the top figures in Panel (b) and (d). The ACM was not able to capture the glottal edges for many glottal cycles in the dim kymograms as seen in the top figures in Panel (a) and (c). In contrast, the hybrid method showed a considerable enhancement in the performance and high accuracy as it detected the glottal edges precisely for all the kymograms, as seen in the bottom kymograms in Panel (b) and (d), also in Panel (a) and (c) with an inferior quality and challenging kymograms. In Figure 6, five HSV frames are presented from each of the four different vocalizations in Figure 5 along with the detected glottal edges by the hybrid method. This figure shows the captured edges after registering the glottal edges from the kymograms back to the HSV frames. For each vocalization, the five frames are chosen to show several frames from a different phase of a vibrating cycle of the vocal folds.