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

AI Article Synopsis

  • A study examined how 18 months of voice training affected acoustic parameters in music majors, using various traditional and nonlinear measures to evaluate changes in their vocal quality.
  • The results indicated significant improvements in the dysphonia severity index (DSI) and vocal range, while certain nonlinear parameters showed changes in voice type component profiles over time.
  • The findings suggest that professional voice training enhances specific vocal metrics, supporting their use for evaluating and screening voice training effectiveness, although many nonlinear measures did not show notable differences.

Article 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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Source
http://dx.doi.org/10.1016/j.jvoice.2021.01.011DOI Listing

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