## 4 Discussion  
The temporal segmentation and motion compensation algorithms were successful in capturing the location of the vibrating vocal folds in a cropped motion window, which prepared the HSV frames for kymogram extraction. The HSV kymograms were generated at different cross sections of the vocal folds during each vocalization. The moment of inertia was used to successfully determine a horizontal line spanning through the center of the vocal folds in each kymogram, which was an important step before applying the hybrid spatial segmentation method to the kymograms. The selection and extraction of the appropriate features were done in order to implement the unsupervised ML technique (i.e., k-means clustering). A different number and combination of features were fed into the ML algorithm to determine the salient subset of features for the development of the method. These features included the intensities of red and green channels and the image gradient. It was found that using these three features was the most appropriate combination of features in terms of obtaining an adequate clustering performance. Given the three considered features, the implemented clustering algorithm was able to precisely cluster the kymograms into two clusters (glottal area and non-glottal area). Subsequently, the edges of the clustered glottal area were spatially segmented, returning the top and bottom initialization contour lines corresponding to the left and right vocal folds, respectively. After obtaining the initial contours from the clustering technique, they were used as inputs to the ACM method to enhance its performance in segmenting the vocal fold edges. The ACM method was successfully applied to the kymograms utilizing the initialized contours. The main weakness of the ACM method is the sensitivity to the contour initialization, which should be selected to be close to the glottal edges. In this study, using the clustering technique to initialize the active contours significantly improved the accuracy of the hybrid ACM in comparison with using the ACM alone, as shown in Figure 5. This hybrid method allowed for the accurate representation of the edges of the vibrating vocal folds in the kymograms at different intersections of the vocal folds. Figure 5 illustrated a comparison between the new machine-learning-based hybrid method against the ACM alone in order to show to what extent the new hybrid technique enhanced the performance of the vocal fold edge representation in comparison with using only the ACM approach. The performance of the hybrid method was compared with that of the ACM by applying the two methods on two decent quality kymograms and two kymograms with dim lighting and degraded qualities. The results of the comparison revealed a significant improvement in edge detection by the hybrid method over using the ACM alone. This enhancement was more noticeable in the lower quality kymograms. This indicated how the proposed hybrid method was less vulnerable to the noise in the image compared to the ACM, which completely failed to detect the edges in the presence of significant noise in the kymograms. In addition, the computational cost of the hybrid method was half of the ACM technique. After applying the hybrid method, the segmented edges in the kymograms, which were extracted at different vocal fold cross sections, were registered back to the HSV spatial frames to detect the vocal fold edges in each individual HSV frame. The performance of the proposed hybrid method was tested through visual inspection of the detected vocal fold edges in the HSV kymograms of different vocalization segments of the \"Rainbow Passage.\" Out of 76 vocalizations, the visual inspection of the detected vocal fold edges in the extracted kymograms demonstrated that the developed hybrid technique successfully captured the glottal edges for 74 vocalizations with an error less than \(\pm 1\) pixel. This yields a high accuracy of 97.4% in vocal fold edge representation using the hybrid method for HSV data during connected speech.