## 3 Results  
The following results demonstrate the implementation of the proposed method for the HSV data recorded from a vocally normal individual while reading the \"Rainbow passage\". An example of five cropped HSV frames extracted from a vocalization after applying the temporal segmentation and motion compensation techniques is illustrated at the top of Figure 1. This vocalization was extracted between frames 32,709 and 35,061. The frame numbers are shown at the top of Panel (b)-(f). As seen, the motion window captures the size and the spatial location of the vocal folds during different phases of the vibratory cycle. After applying the motion window, the HSV kymograms were extracted at various cross sections of the vocal folds during each vocalized segment. Four kymograms, extracted at four different cross sections of the vocal folds during the same vocalization, are shown in Figure 1g-j. The \(y\)-axis of the kymograms represents the left-right dimension of the HSV frame, while the \(x\)-axis refers to the time (number of frames). Each kymogram in the figure displays the voicing onset and offset along with the vibration of the vocal folds. The k-means clustering technique was implemented for each kymogram. Different subsets of features were fed into the ML algorithm to determine the proper number and combination of features leading to an accurate vocal fold edge representation. Figures 2-4 illustrate a comparison between the results of applying two different combinations of the features for glottal area/edge detection: (i) red and green channel intensities as two features (Panel (a) in the figures) versus (ii) the image gradient along with the red and green channel intensities as three features (Panel (b) in the figures). The results of utilizing the other subsets of the aforementioned features to perform the clustering showed poorer performance of the method in comparison with using the selected feature combinations in Figures 2-4. Figure 2 shows the result of applying the clustering technique to the kymogram shown in Figure 1h between Frame 32,709 and 35,061 (for a total of 143,167 data points). The scatter plot in Figure 2a is generated by feeding the clustering algorithm the two intensity features: the green channel intensity and red channel intensity. The scatter plot in Figure 2b is generated using the gradient feature along with both red and green channel intensity features. The glottal area cluster in the kymogram is shown by red diamonds and the non-glottal cluster is shown by blue circles. As seen, after adding the gradient feature to the intensity features in Figure 2b, the two clusters can be distinguished in the scatter plot; in contrast, depending only on the intensity as a feature, it is relatively hard to divide the data points into two different clusters. The better performance of the ML method using the three features is more prevalent in Figure 3.  
Figure 1: Panels **(b–f)**: five HSV cropped frames for Frame 32,974, 32,979, 32,984, 32,992, and 32,997 after applying the motion window to five different HSV frames (one HSV frame is shown in Panel **(a)**). Panels **(g-j)**: four extracted kymograms at different cross-sections of the vocal folds. The R and L on the \(y\)-axis indicate the right and left vocal folds in the HSV frames, respectively. Figure 2: Scatter plots of the two clusters when applying the clustering method to the kymogram in Figure 1h between Frame 32,659 and 35,111: **(a)** using the green and red channel intensities as the features; and **(b)** using both green and red channel intensities along with the gradient as features. Figure 3 shows the two clusters after applying the k-means clustering technique to the kymogram in Figure 1h.