## 4 Discussion  
In this study, the detection of vocal fry based on characterizing the variation observed in the vibration patterns of the vocal folds is proposed. The results of the classification accuracies suggest that the proposed model enables the distinction of normal and vocal fry GAWs. The obtained classification accuracies of detection were found to be competitive with the accuracies reported for past detection techniques proposed in [3, 12, 13, 14]. In these past techniques, the detection rates of vocal fry were less than 90% and the models were based mainly on audio signals, whereas in our study, the high-speed videos of vocal folds enable a more detailed study of voice production. This may help in studying the cause-effect relationship between voice production and perception, and aid the diagnosis of voice disorders. [PERSON] et al. investigated the ventricular fold dynamics in human phonation [25, 26]. A correlation between the vibration of the ventricular and vocal folds was demonstrated using time derivative of EGG signals (DEEG) [25]. A periodic contact between the ventricular folds was observed for every second glottal cycle which resulted in a period-doubling pattern. In [26], ventricular motion was studied using synchronized high-speed cinematographic and audio recordings. The recorded audio sequences consist of glides with transition between different voice qualities. In most of vocal fry sequences, the ventricular folds were observed to follow a slow nonoscillatory motion with a partial ventricular contact, or a total contact. However, in our study, no significant ventricular motion was observed in the high-speed videos. The audio sequences were also recorded for sustained phonation of vowels without any glides. The cyclic modulation of the vibration patterns of vocal fry which we observed using high-speed videos were only due to distinct vocal fold vibration behavior. For CAMP = 2 and CAMP = 4 type GAWs, in each modulator cycle, the peak area of the glottis increased after every subsequent opening of the vocal folds. It is suggested that the myoelastic aerodynamic cause of this phenomenon is to be studied in the future. A central aspect of our study is the analysis of noise contained in the GAWs. The GAWs extracted from the videos contain modulation noise (i.e., jitter and shimmer) and quantization noise. We modelled the modulation noise on a pulse-to-pulse time scale by modulating the quasi unit pulse train that the model uses. The additive quantization noise arises from the finite pixel resolution of the videos and is part of the modelling error levels E. In particular, depending on the relative size of the glottis in the video, a GAW is quantized to only a few hundred available values, i.e., a discrete number of pixels. We focused in this study on modelling modulation noise instead of additive quantization noise, because (i) the modulations and their properties were used to directly describe the voice types that we report, and (ii) the amount of modulation noise in voices labeled as vocal fry exceeded the amount of additive quantization noise by an order of a magnitude. Added values of the presented approach include the following. First, analysis-by-synthesis combined with parameter distribution fitting provides a means of data augmentation. That is particularly relevant because not much natural data is available yet. The selection of the synthesis parameter distributions that we fitted to create the synthetic, i.e., augmented corpus appears to be reasonable since the scatter plots of modelling errors as well as the boxplots of the entropy of the modulator's spectrum and the modulator-to-carrier frequency ratio reflect comparable distributions for natural and synthetic data. Second, we opted for a detailed signal modelling based approach for classification instead of using a black box approach. This has the advantages that the classification appears to be better explainable, and that qualitative knowledge regarding voice production kinematics is obtained.