Summary
Objectives
The purposes of this study were (1) to analyze the usefulness of self-report questionnaires,
acoustic analysis, and auditory perceptual assessment for screening voice problems;
and (2) to develop a new model for predicting a comprehensive voice severity using
multi-assessment.
Methods
A total of 306 voice samples were analyzed in this study (typical group, n = 72; dysphonia
group, n = 234). We performed a receiver operating characteristic analysis to determine
the cutoff values of auditory perceptual assessments (visual analog scale), acoustic
parameters (spectral- and cepstral-based analyses), and self-report questionnaires
for screening voice disorders. We also performed a stepwise multiple regression analysis
to verify which combination of parameters (acoustic parameters, and self-report questionnaires)
could best predict perceived voice severity.
Results
We verified that most of the variables analyzed were useful for voice evaluation,
and found to be useful for screening voice problems. Of these, a five-variable model
was a useful to predict perceived voice severity (mean R2 = .807). The five-variable model consisted of acoustic parameters based on cepstral
analysis (cepstral peak prominences in connected speech and sustained vowel task,
and low versus high-frequency spectral energy ratio in connected speech task) and
self-report questionnaires (total score of the Voice Handicap Index, and rumination
score of the Voice Catastrophization Index).
Conclusion
We verified that most of the variables were useful for screening dysphonia and five-variable
model was a useful to predict perceived voice severity. The five-variable model could
be used as an objective criterion for predicting voice severity.
Key Words
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Article info
Publication history
Published online: November 13, 2020
Accepted:
October 30,
2020
Footnotes
This study was supported by Biomedical Research Institute Grant (202000240001), Pusan National University Hospital.
Identification
Copyright
© 2020 The Voice Foundation. Published by Elsevier Inc. All rights reserved.
