School of Medicine and Public Health, University of Wisconsin-Madison.
Am J Speech Lang Pathol. 2018 Nov 21;27(4):1426-1433. doi: 10.1044/2018_AJSLP-17-0103.
The purpose of this study is to develop a program to concatenate acoustic vowel segments that were selected with the moving window technique, a previously developed technique used to segment and select the least perturbed segment from a sustained vowel segment. The concatenated acoustic segments were compared with the nonconcatenated, short, individual acoustic segments for their ability to differentiate normal and pathological voices. The concatenation process sometimes created a clicking noise or beat, which was also analyzed to determine any confounding effects.
A program was developed to concatenate the moving window segments. Listeners with no previous rating experience were trained and, then, rated 20 normal and 20 pathological voice segments, both concatenated (2 s) and short (0.2 s) for a total of 80 segments. Listeners evaluated these segments on both the Grade, Roughness, Breathiness, Asthenia, and Strain scale (GRBAS; 8 listeners) and the Consensus Auditory-Perceptual Evaluation of Voice (Kempster, Gerratt, Abbott, Barkmeier-Kraemer, & Hillman, 2009) scale (7 listeners). The sensitivity and specificity of these ratings were analyzed using a receiver-operating characteristic curve. To evaluate if there were increases in particular criteria due to the beat, differences between beat and nonbeat ratings were compared using a 2-tailed analysis of variance.
Concatenated segments had a higher sensitivity and specificity for distinguishing pathological and normal voices than short segments. Compared with nonbeat segments, the beat had statistically similar increases for all criteria across Consensus Auditory-Perceptual Evaluation of Voice and GRBAS scales, except pitch and loudness.
The concatenated moving window method showed improved sensitivity and specificity for detecting voice disorders using auditory-perceptual analysis, compared with the short moving window segment. It is a helpful tool for perceptual analytic protocols, allowing for voice evaluation using standardized and automated voice-segmenting procedures.
本研究旨在开发一种程序，将使用移动窗口技术选择的声学元音段进行拼接，这是一种先前开发的技术，用于从持续元音段中分割和选择受干扰最小的段。将拼接的声学段与未拼接的、短的、单个声学段进行比较，以确定它们区分正常和病理嗓音的能力。拼接过程有时会产生咔嗒声或拍击声，也对其进行了分析以确定任何干扰效应。
开发了一个程序来拼接移动窗口段。具有先前评分经验的听众接受了培训，然后对 20 个正常和 20 个病理嗓音段进行评分，包括拼接（2 秒）和短（0.2 秒）段，总共 80 个段。听众使用等级、粗糙度、呼吸声、乏力度和紧张度量表（GRBAS；8 位听众）和共识听觉感知嗓音评估（Kempster、Gerratt、Abbott、Barkmeier-Kraemer 和 Hillman，2009 年）量表（7 位听众）对这些段进行评分。使用受试者工作特征曲线分析这些评分的敏感性和特异性。为了评估由于拍击声而导致特定标准是否增加，使用双尾方差分析比较了拍击声和非拍击声评分之间的差异。
与短段相比，拼接段在区分病理和正常嗓音方面具有更高的敏感性和特异性。与非拍击声段相比，在共识听觉感知嗓音评估和 GRBAS 量表的所有标准中，除了音高和响度外，拍击声都具有统计学上相似的增加。
与短移动窗口段相比，拼接移动窗口方法在使用听觉感知分析检测嗓音障碍方面显示出更高的敏感性和特异性。它是感知分析协议的有用工具，允许使用标准化和自动化的嗓音分段程序进行嗓音评估。
