Summary
Objectives
The purpose of this paper is to introduce an iterative nonlinear weighted method based
on the variation in spectral energy distribution present in a voice signal to differentiate
between four voice types: type 1 voice signals are nearly periodic, type 2 voice signals
have strong modulations and subharmonics, type 3 signals are chaotic, and type 4 signals
are dominated by stochastic noise.
Study Design
A total of 135 voice signal samples of the sustained vowel /a/ were obtained from
the Disordered Voice Database and then individually categorized into the appropriate
voice types based on the classification system described in Sprecher et al (2010).
Voice samples were analyzed using the nonlinear methods of spectrum convergence ratio,
rate of divergence, and nonlinear energy difference ratio (NEDR) to investigate classifier
efficacy.
Methods
An iterative nonlinear weighted method based on the derivative of instantaneous frequency
and Fourier transformations is applied to calculate spectral energy distributions.
The distribution is then used to calculate the NEDR to classify voice signal types.
Results
Statistical analysis revealed that NEDR effectively differentiated between all four
voice types (P < 0.001). Subsequent multiclass receiver operating characteristic analysis demonstrated
that NEDR (area under the curve [95% CI] = 0.99 [0.96–1.0]) possessed the greatest
classification accuracy relative to spectrum convergence ratio and rate of divergence.
Conclusion
NEDR was shown to be an effective metric for objective differentiation between all
four voice signal types. NEDR calculations occurred approximately instantaneously,
constituting a substantial improvement over the tedious computational time required
for calculation of previous nonlinear parameters. This metric could assist clinicians
in the diagnosis of voice disorders and monitor the efficacy of treatment through
observation of voice acoustical improvement over time.
Key Words
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Article info
Publication history
Published online: May 18, 2018
Accepted:
February 14,
2018
Footnotes
Conflict of interest: None.
Identification
Copyright
© 2018 The Voice Foundation. Published by Elsevier Inc. All rights reserved.
