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Voice pathology classification based on High-Speed Videoendoscopy

机译:基于高速视频内窥镜的语音病理分类

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This work presents a method for automatical and objective classification of patients with healthy and pathological vocal fold vibration impairments using High-Speed Videoendoscopy of the larynx. We used an image segmentation and extraction of a novel set of numerical parameters describing the spatio-temporal dynamics of vocal folds to classification according to the normal and pathological cases and achieved 73,3% cross-validation classification accuracy. This approach is promising to develop an automatic diagnosis tool of voice disorders.
机译:这项工作提出了一种使用高速喉内窥镜对患有健康和病理性声带振动障碍的患者进行自动和客观分类的方法。我们使用图像分割和一组新的数值参数来描述人声折叠的时空动态,以根据正常和病理情况进行分类,并获得了73,3%的交叉验证分类准确率。这种方法有望开发语音障碍的自动诊断工具。

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