Purpose: 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. Method: 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. Results: 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. Conclusions: 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. Supplemental Material: https://doi.org/10.23641/asha.7100951.
Purpose: 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. Method: 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. Results: 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. Conclusions: 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. Supplemental Material: https://doi.org/10.23641/asha.7100951.