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The Influence of Voice Training on Vocal Learner's Objective Acoustic Voice Components.

作者信息

Wu Pinhong, Klein Logan, Rozema Zoe, Haderlein Nicole, Cai Jie, Scholp Austin, Xu Xinlin, Jiang Jack J, Zhuang Peiyun

机构信息

Barnard College of Columbia University, New York, NY, USA.

Division of Otolaryngology - Head and Neck Surgery, Department of Surgery, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin.

出版信息

J Voice. 2023 May;37(3):355-361. doi: 10.1016/j.jvoice.2021.01.011. Epub 2021 Feb 27.

Abstract

OBJECTIVE

Acoustic parameters of voice were studied in music majors throughout 18 months of training to understand the influence of voice training on voice.

METHODS

Twenty-three students from Xiamen Music School between 12 and 15 years old were enrolled. Acoustic examination was performed three times- every 6 months for 18 months. Various traditional acoustic parameters were measured, including dysphonia severity index (DSI), jitter, and D-value of vocal range. Nonlinear dynamic measures were also measured, including diffusive chaos to construct voice type component profiles (VTCPs), spectrum convergence ratio, and nonlinear energy difference ratio. The results were analyzed by multivariate analysis of variance.

RESULTS

Over the study duration, there was an improvement of DSI (P = 0.002), and D-value of vocal range (P = 0.000). Among nonlinear parameters, only voice type component data demonstrated significant changes during the study duration. Both Voice Type Component 1(VTC1) and VTC3 values differed from Time 1 to Time 2 as well as from Time 1 to Time 3. The proportion of VTC1 in samples generally decreased, while VTC3, representative of aperiodicity, increased. Both nonlinear energy difference ratio and spectrum convergence ratio exhibited no significant changes throughout the study.

CONCLUSION

Professional voice training can improve DSI and D-value of vocal range in singers' voices. These parameters have potential to be used for voice training evaluation and screening. Many nonlinear parameters did not detect differences in the healthy voices studied, but VTCPs created using intrinsic dimension present a valuable new method, visualizing increases in aperiodicity of the speaking voices after professional voice training.

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