Laryngeal videoendoscopy is one of the main tools in clinical examinations for voice disorders and voice research. Using high-speed videoendoscopy, it is possible to fully capture the vocal fold oscillations, however, processing the recordings typically involves a time-consuming segmentation of the glottal area by trained experts. Even though automatic methods have been proposed and the task is particularly suited for deep learning methods, there are no public datasets and benchmarks available to compare methods and to allow training of generalizing deep learning models. In an international collaboration of researchers from seven institutions from the EU and USa, we have created BaGLS, a large, multihospital dataset of 59,250 high-speed videoendoscopy frames with individually annotated segmentation masks. The frames are based on 640 recordings of healthy and disordered subjects that were recorded with varying technical equipment by numerous clinicians. the BaGLS dataset will allow an objective comparison of glottis segmentation methods and will enable interested researchers to train their own models and compare their methods.

Quantitative analysis of phonatory characteristics of rabbits has been widely neglected. However, preliminary studies established the rabbit larynx as a potential model of human phonation. This study reports quantitative data on phonation using ex vivo rabbit larynx models to achieve more insight into dependencies of three main components of the phonation process, including airflow, vocal fold dynamics, and the acoustic output. Sustained phonation was induced in 11 ex vivo rabbit larynges. For 414 phonatory conditions, vocal fold vibrations, acoustic, and aerodynamic parameters were analyzed as functions of longitudinal vocal fold pre-stress, applied air flow, and glottal closure insufficiency. Dimensions of the vocal folds were measured and histological data were analyzed. Glottal closure characteristics improved for increasing longitudinal pre-stress and applied airflow. For the subglottal pressure signal only the cepstral peak prominence showed dependency on glottal closure. In contrast, vibrational, acoustic, and aerodynamic parameters were found to be highly dependent on the degree of glottal closure: The more complete the glottal closure during phonation, the better the aerodynamic and acoustic characteristics. Hence, complete or at least partial glottal closure appears to enhance acoustic signal quality. Finally, results validate the ex vivo rabbit larynx as an effective model for analyzing the phonatory process.

Purpose High-speed videoendoscopy (HSV) is an emerging, but barely used, endoscopy technique in the clinic to assess and diagnose voice disorders because of the lack of dedicated software to analyze the data. HSV allows to quantify the vocal fold oscillations by segmenting the glottal area. This challenging task has been tackled by various studies; however, the proposed approaches are mostly limited and not suitable for daily clinical routine. Method We developed a user-friendly software in C# that allows the editing, motion correction, segmentation, and quantitative analysis of HSV data. We further provide pretrained deep neural networks for fully automatic glottis segmentation. Results We freely provide our software Glottis Analysis Tools (GAT). Using GAT, we provide a general threshold-based region growing platform that enables the user to analyze data from various sources, such as in vivo recordings, ex vivo recordings, and high-speed footage of artificial vocal folds. Additionally, especially for in vivo recordings, we provide three robust neural networks at various speed and quality settings to allow a fully automatic glottis segmentation needed for application by untrained personnel. GAT further evaluates video and audio data in parallel and is able to extract various features from the video data, among others the glottal area waveform, that is, the changing glottal area over time. In total, GAT provides 79 unique quantitative analysis parameters for video- and audio-based signals. Many of these parameters have already been shown to reflect voice disorders, highlighting the clinical importance and usefulness of the GAT software. Conclusion GAT is a unique tool to process HSV and audio data to determine quantitative, clinically relevant parameters for research, diagnosis, and treatment of laryngeal disorders. Supplemental Material https://doi.org/10.23641/asha.14575533

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