Ikuma Takeshi, Kunduk Melda, Fink Daniel, McWhorter Andrew J
Department of Otolaryngology-Head and Neck Surgery, Louisiana State University Health Sciences Center, 533 Bolivar Street, New Orleans, Louisiana 70112.
Department of Otolaryngology-Head and Neck Surgery, Louisiana State University Health Sciences Center, 533 Bolivar Street, New Orleans, Louisiana 70112; Department of Communication Sciences and Disorders, Louisiana State University, 64 Hatcher Hall, Baton Rouge, Louisiana 70803; Our Lady of the Lake Regional Medical Center-Voice Center, 4950 Essen Lane, Suite B, Baton Rouge, Louisiana 70808.
J Voice. 2016 Nov;30(6):756.e21-756.e30. doi: 10.1016/j.jvoice.2015.09.007. Epub 2015 Nov 30.
High-speed videoendoscopy excels in the ability to observe the vocal-fold oscillatory patterns during voice initiation and termination. The initial and most critical step in the analysis of these transient regions is to identify the locations of these transient periods, that is, determining when the vocal-fold oscillation is absent and when the oscillation has reached its steady-state behavior. The latter is more challenging as the "steady" oscillation during sustained phonation is not truly steady and is expected to vary over time. This variation may cause unreliable identification of the transient periods, possibly resulting in less accurate or less reliable parameter measurements. An oscillation feature that is relatively consistent in the steady state is the glottal length, that is, the extent of the oscillation along vocal folds. This paper proposes an autonomous algorithm to estimate the vocal-fold oscillation length and its use to detect four transient events: oscillation onset and offset, and attainment and loss of full-length oscillation. The detected event markers are intended to be used to improve the transient parameter measurements. The autonomous algorithm manipulates the set of glottal width waveforms spatiotemporally to estimate the oscillation length. Examples with in vivo high-speed videoendoscopy recordings of both normal and pathological cases are included to show the efficacy of the proposed algorithm to identify the transient markers.
高速视频内镜在观察发声起始和终止过程中声带振荡模式方面表现出色。分析这些瞬态区域的首要且关键步骤是确定这些瞬态时期的位置,即确定声带振荡何时不存在以及振荡何时达到其稳态行为。后者更具挑战性,因为持续发声期间的“稳定”振荡并非真正稳定,且预计会随时间变化。这种变化可能导致瞬态时期的识别不可靠,可能导致参数测量不够准确或可靠。在稳态下相对一致的振荡特征是声门长度,即沿声带的振荡范围。本文提出一种自主算法来估计声带振荡长度,并利用它来检测四个瞬态事件:振荡起始和偏移,以及全长振荡的达到和丧失。检测到的事件标记旨在用于改进瞬态参数测量。自主算法通过时空操作声门宽度波形集来估计振荡长度。文中包含了正常和病理病例的体内高速视频内镜记录示例,以展示所提算法识别瞬态标记的有效性。