Deliyski, Dimitar D.

Laryngeal high-speed videoendoscopy (HSV) is a powerful tool to study the detailed vibratory characteristics of the vocal folds. The use of HSV in connected speech enables us to study the transitory behaviors of the vocal folds during different vocalizations. The onset and offset of voicing contain critical information about the health and function of the vocal folds. In this work, HSV is used to study how the voicing begins/ends at the laryngeal level at the onset/offset of phonation. HSV data were obtained during production of connected speech from participants with non-pathological voices and patients with different neurogenic voice disorders. The data collection was performed using a Photron FASTCAM mini AX200 high-speed camera coupled with a flexible nasolaryngoscope. The glottal attack time and glottal offset time were measured at the onset and offset of different vocalizations, respectively, from the HSV data. The findings show differences in the onset and offset measures between the two groups of participants. The vocal fold vibratory behaviors will be discussed qualitatively and quantitatively using these measures. The use of HSV in connected speech could help us develop accurate voice assessment procedures to assess the vocal folds function during connected speech.

Adductor spasmodic dysphonia (AdSD) disrupts laryngeal muscle control during speech and, therefore, affects the onset and offset of phonation. In this study, the goal is to use laryngeal high-speed videoendoscopy (HSV) to measure the glottal attack time (GAT) and glottal offset time (GOT) during connected speech for normophonic (vocally normal) and AdSD voices. A monochrome HSV system was used to record readings of six CAPE-V sentences and part of the “Rainbow Passage” from the participants. Three raters visually analyzed the HSV data using a playback software to measure the GAT and GOT. The results show that the GAT was greater in the AdSD group than in the normophonic group; however, the clinical significance of the amount of this difference needs to be studied further. More variability was observed in both GATs and GOTs of the disorder group. Additionally, the GAT and GOT time series were found to be nonstationary for the AdSD group while they were stationary for the normophonic voices. This study shows that the GAT and GOT measures can be potentially used as objective markers to characterize AdSD. The findings will potentially help in the development of standardized measures for voice evaluation and the accurate diagnosis of AdSD.

Purpose:Voice disorders are best assessed by examining vocal fold dynamics in connected speech. This can be achieved using flexible laryngeal high-speed videoendoscopy (HSV), which enables us to study vocal fold mechanics with high temporal details. Analysis of vocal fold vibration using HSV requires accurate segmentation of the vocal fold edges. This article presents an automated deep-learning scheme to segment the glottal area in HSV from which the glottal edges are derived during connected speech.Method:Using a custom-built HSV system, data were obtained from a vocally healthy participant reciting the “Rainbow Passage.” A deep neural network was designed for glottal area segmentation in the HSV data. A recently introduced hybrid approach by the authors was utilized as an automated labeling tool to train the network on a set of HSV frames, where the glottis region was automatically annotated during vocal fold vibrations. The network was then tested against manually segmented frames using different metrics, intersection over union (IoU), and Boundary F1 (BF) score, and its performance was assessed on various phonatory events on the HSV sequence.Results:The designed network was successfully trained using the hybrid approach, without the need for manual labeling, and tested on the manually labeled data. The performance metrics showed a mean IoU of 0.82 and a mean BF score of 0.96. In addition, the evaluation assessment of the network's performance demonstrated an accurate segmentation of the glottal edges/area even during complex nonstationary phonatory events and when vocal folds were not vibrating, thus overcoming the limitations of the previous hybrid approach that could only be applied to the vibrating vocal folds.Conclusions:The introduced automated scheme guarantees accurate glottis representation in challenging color HSV data with lower image quality and excessive laryngeal maneuvers during all instances of connected speech. This facilitates the future development of HSV-based measures to assess the running vibratory characteristics of the vocal folds in speakers with and without voice disorder.Supplemental Material:https://doi.org/10.23641/asha.19798864

Adductor spasmodic dysphonia (AdSD) is a neurological voice disorder, which affects the control of intrinsic muscles of the larynx during speech. Our previous research has shown that high-speed videoendoscopy (HSV) can be used to measure the glottal attack time for patients with AdSD during connected speech. This study builds upon that to analyze the acoustic signal during the phonation onset and prephonatory adjustments. The phonation onset events are quantified using the HSV data that were obtained simultaneously with the acoustic data. We analyze the power and energy waveform of the acoustic signal during vocal folds’ first contact, first oscillation, and transitory behaviors, extracted from high-temporal resolution HSV data. The data were obtained from five patients with AdSD and five vocally normal participants. The data were recorded while the participant produced: 2 productions of vowel /i/ at habitual pitch and loudness, 2 with a soft glottal attack and 2 with a hard glottal attack, six CAPE-V sentences, and the “Rainbow Passage.” The HSV system included a monochrome high-speed camera coupled with a flexible nasolaryngoscope. The results of this study could assist for more accurate clinical management of adductor spasmodic dysphonia.

Investigating the phonatory processes in connected speech from high-speed videoendoscopy (HSV) demands the accurate detection of the vocal fold edges during vibration. The present paper proposes a new spatio-temporal technique to automatically segment vocal fold edges in HSV data during running speech. The HSV data were recorded from a vocally normal adult during a reading of the “Rainbow Passage.” The introduced technique was based on an unsupervised machine-learning (ML) approach combined with an active contour modeling (ACM) technique (also known as a hybrid approach). The hybrid method was implemented to capture the edges of vocal folds on different HSV kymograms, extracted at various cross-sections of vocal folds during vibration. The k-means clustering method, an ML approach, was first applied to cluster the kymograms to identify the clustered glottal area and consequently provided an initialized contour for the ACM. The ACM algorithm was then used to precisely detect the glottal edges of the vibrating vocal folds. The developed algorithm was able to accurately track the vocal fold edges across frames with low computational cost and high robustness against image noise. This algorithm offers a fully automated tool for analyzing the vibratory features of vocal folds in connected speech.

Studying voice production during running speech can provide new knowledge about the mechanisms of voice production with and without disorder. Laryngeal high-speed videoendoscopy (HSV) systems are powerful tools for studying laryngeal function and, if coupled with flexible fiberoptic endoscopes, they can provide unique possibilities to measure vocal fold vibration with high temporal resolution during connected speech. Hence, we can measure the non-stationary behaviors of the vocal folds, such as the glottal attack and offset times in running speech. In this study, a custom-built flexible fiberoptic HSV system was used to record a “Rainbow Passage” production from a vocally normal female. Automated temporal and spatial segmentation algorithms were developed to determine the time stamps of the vibrating vocal folds and the edges of the vocal folds during phonation. The glottal attack time and offset times were then measured from the temporally and spatially segmented HSV images. The amplification ratio was computed during the phonation onset and the damping ratio was calculated at the offset of sustained portion of phonation. These measures can be used to describe the laryngeal mechanisms of voice production in connected speech.