Detection of Vocal Fold Image Obstructions in High-Speed Videoendoscopy During Connected Speech in Adductor Spasmodic Dysphonia: A Convolutional Neural Networks Approach.
Detection of Vocal Fold Image Obstructions in High-Speed Videoendoscopy During Connected Speech in Adductor Spasmodic Dysphonia: A Convolutional Neural Networks Approach.
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DOI：
10.1016/j.jvoice.2022.01.028
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发表时间：
2024-07
期刊：
影响因子：
2.2
通讯作者：
Naghibolhosseini, Maryam
中科院分区：
文献类型：
--
作者：
Yousef, Ahmed M.;Deliyski, Dimitar D.;Zacharias, Stephanie R. C.;Naghibolhosseini, Maryam
关键词：
内收肌痉挛性发声障碍（AdSD）是一种神经源性发声障碍，影响喉内肌的控制。AdSD会导致不自主的喉痉挛，并且只在连接讲话时才会出现。喉高速视频内窥镜（HSV）加上灵活的光纤内窥镜提供了一个独特的机会，研究语音生产和可视化声带振动AdSD在讲话。本研究的目标是自动检测的情况下，在连接语音过程中获得的HSV录音声带的图像是光学阻碍。HSV数据记录从发音正常的成年人和AdSD患者在阅读的“彩虹通道”，六个CAPE-V的句子，和生产的元音/i/。卷积神经网络的开发和训练作为一个分类器，以检测HSV帧中的阻塞/通畅声带。手动标记的数据用于网络的训练、验证和测试。此外，进行了全面的鲁棒性评估，比较开发的分类器和HSV数据的视觉分析的性能。所开发的卷积神经网络能够自动检测声音正常参与者和AdSD患者的HSV数据中的声带阻塞。经过训练的网络成功地进行了测试，在测试数据集上显示出94.18%的整体分类准确率。鲁棒性评估显示，在大量HSV帧上的平均总体准确度为94.81%，这表明所引入的技术具有高鲁棒性，同时保持了高水平的准确度。该方法可用于HSV数据的成本效益分析，以研究AdSD患者在连接语音期间的喉部动作。此外，这种方法将有助于发展声带振动措施的HSV帧与声带的通畅的看法。指示提供声带的无障碍视图的连接语音的部分可以用于开发用于在连接语音和受试者特定的临床语音评估协议期间进行精确HSV检查的最佳通道。
Adductor spasmodic dysphonia (AdSD) is a neurogenic voice disorder, affecting the intrinsic laryngeal muscle control. AdSD leads to involuntary laryngeal spasms and only reveals during connected speech. Laryngeal high-speed videoendoscopy (HSV) coupled with a flexible fiberoptic endoscope provides a unique opportunity to study voice production and visualize the vocal fold vibrations in AdSD during speech. The goal of this study is to automatically detect instances during which the image of the vocal folds is optically obstructed in HSV recordings obtained during connected speech. HSV data were recorded from vocally normal adults and patients with AdSD during reading of the “Rainbow Passage”, six CAPE-V sentences, and production of the vowel /i/. A convolutional neural network was developed and trained as a classifier to detect obstructed/unobstructed vocal folds in HSV frames. Manually labelled data were used for training, validating, and testing of the network. Moreover, a comprehensive robustness evaluation was conducted to compare the performance of the developed classifier and visual analysis of HSV data. The developed convolutional neural network was able to automatically detect the vocal fold obstructions in HSV data in vocally normal participants and AdSD patients. The trained network was tested successfully and showed an overall classification accuracy of 94.18% on the testing dataset. The robustness evaluation showed an average overall accuracy of 94.81% on a massive number of HSV frames demonstrating the high robustness of the introduced technique while keeping a high level of accuracy. The proposed approach can be used for a cost-effective analysis of HSV data to study laryngeal maneuvers in patients with AdSD during connected speech. Additionally, this method will facilitate development of vocal fold vibratory measures for HSV frames with an unobstructed view of the vocal folds. Indicating parts of connected speech that provide an unobstructed view of the vocal folds can be used for developing optimal passages for precise HSV examination during connected speech and subject-specific clinical voice assessment protocols.
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DOI：
10.1016/j.jvoice.2017.05.014
发表时间：
2018-03
期刊：
Journal of voice : official journal of the Voice Foundation
影响因子：
--
作者：
Naghibolhosseini M;Deliyski DD;Zacharias SRC;de Alarcon A;Orlikoff RF
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DOI：
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影响因子：
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