Purpose
The excised canine larynx provides an advantageous experimental framework in the study of voice physiology. In recent years, signal processing methods have been applied to analyze phonations in excised canine larynx experiments. However, phonations have a highly complex and nonstationary nature corresponding to different proportions of regular and chaotic signal elements. Current nonlinear dynamic methods that are used to assess the degree of irregularity in the voice fail to recognize the distribution of voice type components (VTCs).
Method
Based on measures of intrinsic dimension, this article presents a method to analyze the VTC distribution of phonations in excised canine larynx experiments. Thirty-nine phonation samples from 13 excised canine larynges at three different subglottal pressures were analyzed.
Results
Phonation produced with subglottal pressures above phonation instability pressure (PIP) and below phonation threshold pressure (PTP) resulted in high proportions of Voice Types 3 and 4, characterized by chaotic and noisy signals. Phonation produced with pressure between PTP and PIP contained mostly Type 1 voice, characterized by a regular and nearly periodic signal. Mean proportions of all VTCs varied significantly in comparisons of phonations produced with Sub-PTP and PTP as well as in comparisons of phonations produced with PTP and PIP.
Conclusions
Across all VTCs, the VTC profiles of normal and abnormal phonation differ significantly. Normal phonation is strongly associated with VTC
1
(Voice Type Component 1), whereas abnormal phonation exhibits increased VTC
4
(Voice Type Component 4). The study further demonstrates the ability of intrinsic dimension to successfully detect multiple voice types in an acoustic signal and highlights the need for expanded use of intrinsic dimension in human voice.
Supplemental Material
https://doi.org/10.23641/asha.14417585
Objective: To investigate the value of diffusion tensor imaging (DTI) in the evaluation of vocal fold tissue microstructure after recurrent laryngeal nerve (RLN) injury. Methods: Six canines were divided into 2 groups: a unilateral vocal fold paralysis group (n = 4) and a control group (n = 2). The RLN was cut in the unilateral vocal fold paralysis group, and no intervention was applied in the control group. After 4 months, the canines’ larynges were removed and placed in a small animal magnetic resonance imaging (MRI) system (9.4T BioSpec MRI; Bruker, Germany). After scanning, the vocal folds were isolated, sectioned, and stained. The slides were then analyzed for the cross-sectional area and muscle fiber density through feature extraction technology. Pearson correlation analysis was performed on the DTI scan and histological section extraction results. Results: In the vocal fold muscle layer, the fractional anisotropy (FA) of the unilateral RLN injury group was higher than that of the control group, and the Tensor Trace was lower than that of the control group. This difference was statistically significant, P < .05. In the lamina propria, the FA of the unilateral RLN injury group was lower than that of the control group, P > .05, and the Tensor Trace was lower than that of the control group, P < .05. The muscle fiber cross-sectional area of the RLN injury group was significantly smaller than the control group with statistical significance, P < .05, and the density of muscle fibers was lower, P < .05. The correlation coefficient between FA and the cross-sectional area was −0.838, P = .002, and .726; P = .017 between Tensor Trace and the cross-sectional area. Conclusion: Diffusion tensor imaging is an effective method to assess the changes in the microstructure of atrophic vocal fold muscle tissue after RLN injury.
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