Dataset Information

Temporal Segmentation for Laryngeal High-Speed Videoendoscopy in Connected Speech.

ABSTRACT: OBJECTIVE:This study proposes a gradient-based method for temporal segmentation of laryngeal high-speed videoendoscopy (HSV) data obtained during connected speech. METHODS:A custom-developed HSV system coupled with a flexible fiberoptic nasolaryngoscope was used to record one vocally normal female participant during reading of the "Rainbow Passage." A gradient-based algorithm was developed to generate a motion window. When applied to the HSV data, the motion window acted as a filter tracking the location of the vibrating vocal folds. The glottal area waveform was estimated using a statistical-based image-processing approach. The vocal fold vibratory frequency was computed by an autocorrelation-based extraction of the fundamental frequency (f0) from the glottal area waveform. Temporal segmentation was then performed based on the f0 contour and automatic detection of the epiglottic obstructions. Additionally, visual temporal segmentation was performed by viewing the HSV images frame by frame to determine the time points of the vocalization onsets and offsets, and the epiglottic obstructions of the glottis. RESULTS:The time points resulting from the automatic and visual temporal segmentation methods were cross-validated. The f0-contour patterns of rise and fall resulting from the automatic algorithm were found to be in agreement with the visual inspection of the vibratory frequency change in the HSV data. CONCLUSIONS:This study demonstrated the feasibility of automatic temporal segmentation of HSV imaging of connected speech, which allows for mapping the video content into onsets, offsets, and epiglottic obstructions for each vocalization. Automated analysis of HSV imaging of connected speech has significant clinical potential for advancing instrumental voice assessment protocols.

SUBMITTER: Naghibolhosseini M

PROVIDER: S-EPMC5740029 | biostudies-other | 2018 Mar

REPOSITORIES: biostudies-other

ACCESS DATA

Publications

Temporal Segmentation for Laryngeal High-Speed Videoendoscopy in Connected Speech.

Naghibolhosseini Maryam M Deliyski Dimitar D DD Zacharias Stephanie R C SRC de Alarcon Alessandro A Orlikoff Robert F RF

Journal of voice : official journal of the Voice Foundation 20170621 2

<h4>Objective</h4>This study proposes a gradient-based method for temporal segmentation of laryngeal high-speed videoendoscopy (HSV) data obtained during connected speech.<h4>Methods</h4>A custom-developed HSV system coupled with a flexible fiberoptic nasolaryngoscope was used to record one vocally normal female participant during reading of the "Rainbow Passage." A gradient-based algorithm was developed to generate a motion window. When applied to the HSV data, the motion window acted as a filt ...[more]

PMID: 28647431

Similar Datasets

Project description:The objective investigation of the dynamic properties of vocal fold vibrations demands the recording and further quantitative analysis of laryngeal high-speed video (HSV). Quantification of the vocal fold vibration patterns requires as a first step the segmentation of the glottal area within each video frame from which the vibrating edges of the vocal folds are usually derived. Consequently, the outcome of any further vibration analysis depends on the quality of this initial segmentation process. In this work we propose for the first time a procedure to fully automatically segment not only the time-varying glottal area but also the vocal fold tissue directly from laryngeal high-speed video (HSV) using a deep Convolutional Neural Network (CNN) approach. Eighteen different Convolutional Neural Network (CNN) network configurations were trained and evaluated on totally 13,000 high-speed video (HSV) frames obtained from 56 healthy and 74 pathologic subjects. The segmentation quality of the best performing Convolutional Neural Network (CNN) model, which uses Long Short-Term Memory (LSTM) cells to take also the temporal context into account, was intensely investigated on 15 test video sequences comprising 100 consecutive images each. As performance measures the Dice Coefficient (DC) as well as the precisions of four anatomical landmark positions were used. Over all test data a mean Dice Coefficient (DC) of 0.85 was obtained for the glottis and 0.91 and 0.90 for the right and left vocal fold (VF) respectively. The grand average precision of the identified landmarks amounts 2.2 pixels and is in the same range as comparable manual expert segmentations which can be regarded as Gold Standard. The method proposed here requires no user interaction and overcomes the limitations of current semiautomatic or computational expensive approaches. Thus, it allows also for the analysis of long high-speed video (HSV)-sequences and holds the promise to facilitate the objective analysis of vocal fold vibrations in clinical routine. The here used dataset including the ground truth will be provided freely for all scientific groups to allow a quantitative benchmarking of segmentation approaches in future.

Project description:In laryngeal high-speed videoendoscopy (HSV) the area between the vibrating vocal folds during phonation is of interest, being referred to as glottal area waveform (GAW). Varying camera resolution may influence parameters computed on the GAW and hence hinder the comparability between examinations. This study investigates the influence of spatial camera resolution on quantitative vocal fold vibratory function parameters obtained from the GAW. In total 40 HSV recordings during sustained phonation (20 healthy males and 20 healthy females) were investigated. A clinically used Photron Fastcam MC2 camera with a frame rate of 4000 fps and a spatial resolution of 512×256 pixels was applied. This initial resolution was reduced by pixel averaging to (1) a resolution of 256×128 and (2) to a resolution of 128×64 pixels, yielding three sets of recordings. The GAW was extracted and in total 50 vocal fold vibratory parameters representing different features of the GAW were computed. Statistical analyses using SPSS Statistics, version 21, was performed. 15 Parameters showing strong mathematical dependencies with other parameters were excluded from the main analysis but are given in the Supporting Information. Data analysis revealed clear influence of spatial resolution on GAW parameters. Fundamental period measures and period perturbation measures were the least affected. Amplitude perturbation measures and mechanical measures were most strongly influenced. Most glottal dynamic characteristics and symmetry measures deviated significantly. Most energy perturbation measures changed significantly in males but were mostly unaffected in females. In females 18 of 35 remaining parameters (51%) and in males 22 parameters (63%) changed significantly between spatial resolutions. This work represents the first step in studying the impact of video resolution on quantitative HSV parameters. Clear influences of spatial camera resolution on computed parameters were found. The study results suggest avoiding the use of the most strongly affected parameters. Further, the use of cameras with high resolution is recommended to analyze GAW measures in HSV data.

Project description:MotivationHuman voice is generated in the larynx by the two oscillating vocal folds. Owing to the limited space and accessibility of the larynx, endoscopic investigation of the actual phonatory process in detail is challenging. Hence the biomechanics of the human phonatory process are still not yet fully understood. Therefore, we adapt a mathematical model of the vocal folds towards vocal fold oscillations to quantify gender and age related differences expressed by computed biomechanical model parameters.MethodsThe vocal fold dynamics are visualized by laryngeal high-speed videoendoscopy (4000 fps). A total of 33 healthy young subjects (16 females, 17 males) and 11 elderly subjects (5 females, 6 males) were recorded. A numerical two-mass model is adapted to the recorded vocal fold oscillations by varying model masses, stiffness and subglottal pressure. For adapting the model towards the recorded vocal fold dynamics, three different optimization algorithms (Nelder-Mead, Particle Swarm Optimization and Simulated Bee Colony) in combination with three cost functions were considered for applicability. Gender differences and age-related kinematic differences reflected by the model parameters were analyzed.Results and conclusionThe biomechanical model in combination with numerical optimization techniques allowed phonatory behavior to be simulated and laryngeal parameters involved to be quantified. All three optimization algorithms showed promising results. However, only one cost function seems to be suitable for this optimization task. The gained model parameters reflect the phonatory biomechanics for men and women well and show quantitative age- and gender-specific differences. The model parameters for younger females and males showed lower subglottal pressures, lower stiffness and higher masses than the corresponding elderly groups. Females exhibited higher subglottal pressures, smaller oscillation masses and larger stiffness than the corresponding similar aged male groups. Optimizing numerical models towards vocal fold oscillations is useful to identify underlying laryngeal components controlling the phonatory process.

Dataset Information

Temporal Segmentation for Laryngeal High-Speed Videoendoscopy in Connected Speech.

Publications

Temporal Segmentation for Laryngeal High-Speed Videoendoscopy in Connected Speech.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets