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. 2011 Mar;120(3):155-61.
doi: 10.1177/000348941112000303.

Objective methods of sample selection in acoustic analysis of voice

Aleksandra E Olszewski  1 , Lisa Shen, Jack J Jiang
Affiliations

Objective methods of sample selection in acoustic analysis of voice

Aleksandra E Olszewski et al. Ann Otol Rhinol Laryngol. 2011 Mar.

Abstract

Objectives: In acoustic voice analysis, the fact that reproducible methods of sample selection have not been defined impedes research study generalizability and clinical assessment of treatment efficacy. Because perturbation results differ along a single signal, this study sought to establish objective methods of sample selection by use of a moving window to determine the most stable regions of phonation.

Methods: Voice signals obtained from 21 patients affected by laryngeal conditions associated with Parkinson's disease were analyzed to study jitter, shimmer, signal-to-noise ratio, and correlation dimension parameters when various sample selection procedures were used. Objectively selected voice samples were chosen based upon 5%, 10%, and 20% variance from a signal's minimum perturbation value. The stability of these samples, defined by the standard deviations of the acoustic measurements, was compared to the stability of unselected samples and subjectively selected samples.

Results: A significant decrease in standard deviation values of acoustic parameters was found in comparing the objectively selected samples (particularly those selected with 5% and 10% variance) to the subjectively selected and unselected samples.

Conclusions: These results suggest that the development of an objective sample selection method may have significant effects on the stability and reliability of acoustic voice measurements.

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Figures

Figure 1
Figure 1
Time variance of a voice signal with respect to percent jitter. The different samples 2–4 are marked with arrows to show where the original voice signal was cut. Blue arrows represent sample 2 (5% variance), red arrows represent sample 3 (10% variance), and black arrows represent sample 4 (20% variance). Sample 1 is uncut and represented by the entire length of voice signal.
Figure 2
Figure 2
Time variance of a voice signal with respect to percent shimmer. Arrows define sample selection for samples 2–4, with color designation same as in Figure 1. Sample 1 is uncut and represented by the entire length of the voice signal.
Figure 3
Figure 3
Time variance of a voice signal with respect to SNR. Sample 1 is uncut and represented by the entire length of the voice signal. In the special case of SNR, samples 3 and 4 are also represented by the entire length of the voice signal. Blue arrows represent sample 2 (5% variance).
Figure 4
Figure 4
A box plot distribution comparing the standard deviations of percent jitter from various sample selection methods.
Figure 5
Figure 5
A box plot distribution comparing the standard deviations of percent shimmer from various sample selection methods.
Figure 6
Figure 6
A box plot distribution comparing the standard deviations of SNR from various sample selection methods.
Figure 7
Figure 7
A box plot distribution comparing standard deviations of D2 from various sample selection methods.
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