## 5 Voice Research and Clinical Applications  
In voice research, several works make use of glottal segmentation and FP to investigate the correlation between vocal folds vibratory patterns, speaker voice quality, voice onset and offset. For instance, the authors in [3,131,132] make use of glottal segmentation to obtain the afforementioned GAW, which identifies new quantitative measures to evaluate the regularity of vocal folds vibration during phonation. In [133], GAW has been used to compute the Voice Onset Time (VOT) which is a clinical indicator for correct laryngeal functionality. The authors in [134] computed 20 parameters from the acoustic signal and GAW. The aim was to identify mathematical dependencies between parameters and suggest which parameters may best describe the properties of the GAW and corresponding acoustical signal. Subsequent works [7,39,48,71,135,136] pointed out the advantages of studying the vocal folds vibration function to detect asymmetries, transients, breaks, opening phase events, closing phase events and irregularities. The literature also reports a combined use of laryngeal image processing of the vocal folds motion with biomechanical models (so-called Lumpsed Mass Models (LMMs)) to investigate the parameter effects such as applied subglottal air pressure, vibrating masses, tissue stiffness and elongation characteristics with respect to the dynamic vocal folds behavior. In [6,137,138], the Two Spring-Coupled Masses Models (2 MMs) proposed in [139] was combined with glottal segmentation to describe the vocal folds oscillations as a function of time. The authors found physiological parameters such as vocal folds tensions and vocal folds masses. In [140], an automatic optimization procedure was developed to fit a multi-mass model [141] to the observed vocal folds oscillations obtained via segmentation, with the aim of inferring an approximation of the stiffness and mass distribution along the entire vocal folds. In [142], a numerical 2 MMs was adapted to the VFT playback by varying model masses, stiffness and subglottal pressure. The obtained model was used to quantify gender and age-related differences. Most recent work makes use of a recurrent neural network to train a 2 MMs using VFT to estimate laryngeal pressure [143]. The authors claim that the methodology proposed is transferable to estimate other laryngeal parameters that will aid in diagnosis and treatment selection in voice disorders. The image-processing of vocal folds motion has been used to highlight the importance of visualizing MW propagation for an accurate diagnosis and optimal treatment of voice disorders. In [144,145] the MW was detected and quantified by combining glottal segmentation with physiological knowledge of mucosal lateral movements. An important conclusion was the finding that 2000 fps is insufficient to assess features of MW when the frequency of vocal fold vibration is high. The authors in [146] discussed the benefits, the disadvantages, and the clinical applicability of different MW measurement techniques. They found the necessity of additional research to broaden the use of laryngeal image-processing to accurately and objectively diagnose voice disorders. The authors in [147] analyzed the OQ from glottal-gap segmentation and Electroglottography (EGG). They observe that the measure of the OQ from three parts of the glottis helps to diagnose and localize glottal vocal-fold lesions. They also found that OQ varies depending on the type of organic dysphonia. In [148,149] MW analysis is used for the diagnosis of different vocal fold lesions and for evaluating the success of treatment procedures, hydration effects,or mucosal healing after phonosurgery. Besides human vocal folds, mucosal waves were documented on the vocal folds of many mammals and also in birds [150,151,152,153]. They observed that the fundamental frequencies of many mammals are similar to those seen in humans, and consists of flow-induced self-sustaining oscillations of laryngeal tissues.