Department of Phoniatrics and Pediatric Audiology, University Hospital Erlangen, Bohlenplatz 21, D-91054 Erlangen, Germany.
Comput Methods Programs Biomed. 2010 Sep;99(3):275-88. doi: 10.1016/j.cmpb.2010.01.004.
The clinical diagnosis of voice disorders is based on examination of the rapidly moving vocal folds during phonation (f0: 80-300Hz) with state-of-the-art endoscopic high-speed cameras. Commonly, analysis is performed in a subjective and time-consuming manner via slow-motion video playback and exhibits low inter- and intra-rater reliability. In this study an objective method to overcome this drawback is presented being based on Phonovibrography, a novel image analysis technique. For a collective of 45 normophonic and paralytic voices the laryngeal dynamics were captured by specialized Phonovibrogram features and analyzed with different machine learning algorithms. Classification accuracies reached 93% for 2-class and 73% for 3-class discrimination. The results were validated by subjective expert ratings given the same diagnostic criteria. The automatic Phonovibrogram analysis approach exceeded the experienced raters' classifications by 9%. The presented method holds a lot of potential for providing reliable vocal fold diagnosis support in the future.
嗓音障碍的临床诊断基于对发声时快速运动的声带（f0：80-300Hz）进行检查，使用最先进的内窥镜高速摄像机。通常，通过慢动作视频回放以主观和耗时的方式进行分析，并且表现出低的组内和组间可靠性。在这项研究中，提出了一种基于声振图的客观方法来克服这一缺点，这是一种新颖的图像分析技术。对于 45 个正常音和瘫痪音的集合，通过专门的声振图特征捕获了喉部动力学，并使用不同的机器学习算法进行了分析。对于 2 类和 3 类的区分，分类准确率分别达到 93%和 73%。通过使用相同的诊断标准给予主观专家评分来验证结果。自动声振图分析方法比经验丰富的评估者的分类高出 9%。该方法在未来为提供可靠的声带诊断支持方面具有很大的潜力。
