Center for Biomedical Technology, Universidad Politécnica de Madrid, Campus de Montegancedo, Crta. M40 km, 38, Madrid, Spain.
Med Biol Eng Comput. 2017 Dec;55(12):2123-2141. doi: 10.1007/s11517-017-1652-8. Epub 2017 May 27.
The visual examination of the vibration patterns of the vocal folds is an essential method to understand the phonation process and diagnose voice disorders. However, a detailed analysis of the phonation based on this technique requires a manual or a semi-automatic segmentation of the glottal area, which is difficult and time consuming. The present work presents a cuasi-automatic framework to accurately segment the glottal area introducing several techniques not explored before in the state of the art. The method takes advantage of the possibility of a minimal user intervention for those cases where the automatic computation fails. The presented method shows a reliable delimitation of the glottal gap, achieving an average improvement of 13 and 18% with respect to two other approaches found in the literature, while reducing the error of wrong detection of total closure instants. Additionally, the results suggest that the set of validation guidelines proposed can be used to standardize the criteria of accuracy and efficiency of the segmentation algorithms.
声带振动模式的目视检查是理解发音过程和诊断嗓音障碍的基本方法。然而，基于该技术对发音进行详细分析需要手动或半自动分割声门区域，这既困难又耗时。本工作提出了一种准自动框架，通过引入一些以前在该领域中尚未探索的技术，实现了声门区域的精确分割。该方法利用了在自动计算失败的情况下，用户进行最小干预的可能性。所提出的方法能够可靠地划定声门间隙，与文献中发现的另外两种方法相比，平均提高了 13%和 18%，同时减少了总闭合时刻误检的错误。此外，结果表明，所提出的验证准则集可用于对分割算法的准确性和效率标准进行标准化。
