###### Abstract
Since these approaches utilized a supervised learning framework (i.e., deep neural networks), they required manual annotation of the vocal fold edges in HSV frames in order to be used as a training dataset. Recently, we introduced a fully automated method to segment the glottal area using HSV during connected speech for the first time [20]. This method applied a spatio-temporal approach based on active-contour modeling (ACM), which enabled us to detect the glottal edges during vocal fold vibration. ACM is an iterative energy minimization technique for edge detection, and it requires initializing a deformable contour near the edges of interest in an image [50]. One of the benefits of ACM is that it is not noise sensitive [50; 51; 52], unlike some of the other spatial segmentation methods, which are more vulnerable to noise and intensity homogeneity in the image [39; 43; 45; 53; 54]. In our proposed algorithm, the segmentation was applied to HSV kymograms at different vocal fold cross-sections in individual vocalizations to capture the glottal edges in each kymogram. The detected edges in the kymograms were registered back to the HSV frames [20]. The method we developed was able to detect the glottal edges in 88% of the vocalizations. The algorithm was not successful when the kymograms had very dim lighting. Although ACM is less vulnerable to noise in the image, it is sensitive to the initialization of the contour. The initial contour should be close to the glottal edge in order to have the best performance of ACM, which is a limitation of this method. Moreover, since ACM is an iterative method, it required a relatively long time for convergence because the analysis is done at all cross-sections of the vocal folds for each vocalization. ML may overcome the limitation of ACM on its dependency to the contour initialization and the high computational cost. In this work, we propose a hybrid method based on an unsupervised ML (clustering) technique and ACM for vocal fold edge representation in HSV data during connected speech. This hybrid approach can provide a more robust spatial segmentation performance with less computational costs than the ACM method alone. Data clustering is a way of grouping data with similar characteristics, which is used extensively in image analysis [55]. Clustering is based on partitioning the data into clusters of data points that have similar features within each cluster and dissimilarity with other clusters [56]. In this project, the clustering was used to group the pixels belonging to the glottal area into one cluster and the rest of the pixels into another cluster. Clustering is considered an unsupervised ML method since the data points are not required to be labeled, meaning we do not need to visually label the pixels to indicate whether they belong to the glottal area or not, a time-consuming process involving manual analysis of the data. Therefore, our developed method in this work is fully automated and does not require the user intervention in the data analysis. The clustering technique can perform efficiently in detecting the glottal area in HSV data. This is because the glottal area is relatively dark and can be silhouetted against the brighter surrounding tissues of the vocal folds [32]. In the present study, the k-means clustering method is combined with ACM as a shape-based image segmentation method to improve our previously introduced method (i.e., ACM) [20]. As such, the clustering technique is utilized to obtain an accurate initialization contour close to the glottal area, which was given as an input to the ACM method. The ACM algorithm is used to capture the accurate glottal edges during the vocal fold vibrations. The goals of this study are: (i) to present the developed theoretical framework for the proposed method, (ii) to exhibit its feasibility in vocal fold edge representation in HSV data during connected speech, and (iii) to show its robustness for challenging color HSV images.