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Electrical and Computer Engineering, University of Southern California, Los |
Angeles, California; ‡Keck School of Medicine, University of Southern California, |
Los Angeles, California; and the §Department of Otolaryngology—Head & Neck |
Surgery, University of Southern California, Los Angeles, California |
Address correspondence and reprint requests to Benjamin van der Woerd, |
Department of Surgery, Division of Otolaryngology—Head and Neck Surgery, |
McMaster University, 50 Charlton Avenue East, Office G839, Hamilton, Ontario |
L8N 1Y3, Canada |
1Nikolaos Flemotomos is now working at Apple Inc |
Downloaded for Anonymous User (n/a) at McMaster University from ClinicalKey.com by Elsevier on September 07, |
2023 |
For personal use only |
No other uses without permission |
Copyright ©2023 |
Elsevier Inc |
All rights reserved |
within otolaryngology—head and neck surgery.15-17 With |
respect to voice assessments, researchers have developed |
tools to categorize samples according to gender and eval- |
uate dysphonia using sustained vowel samples.15 However, |
the traditional auditory-perceptual assessment includes |
both sustained vowels and connected speech to evaluate |
voice parameters across a variety of laryngeal beha - |
viors.18,19 To date, there are no machine-learning global |
assessments of dysphonia severity using both sustained |
vowels and sentence samples calibrated to known expert |
ratings |
In this study, the authors seek to develop a ma- |
chine-learning algorithm for evaluating dysphonia severity |
on a 100-point scale using previously collected and expertly |
rated voice samples of sustained vowels and connected |
speech |
METHODS |
This study was designed using a previously labeled data set |
to train a machine-learning model on |
The Perceptual |
Voice Qualities Database (PVQD) includes audio samples |
(n = 295) which were professionally captured at partici - |
pating voice centers |
These samples include sustained vo- |
wels and connected speech (CAPE-V sentences) segments |
Furthermore, each sample was previously rated by three |
experts on a 0–100 scale according to the standards of the |
CAPE-V |
The labeled data set allows the computer to |
know how the samples are supposed to be rated and to fit |
different criteria for the prediction model result in similar |
estimates |
The primary goal was to teach the model to |
categorize voice samples according to the same scale |
To do this, we used the OpenSMILE open-source toolkit |
with the emobase2010 configuration.20 This was used to |
extract acoustic and prosodic features, as well as pitch |
onsets and recording duration |
More explicitly, we ex- |
tracted a base of 34 low-level descriptor (LLD) features |
(including Mel-Frequency Cepstral Coefficient features, |
logarithmic power of Mel-frequency bands, normalized |
intensity, etc) with 34 corresponding delta coefficients ap- |
pended and applied 21 different statistical functions (such |
as standard deviation, arithmetic mean, skewness, kurtosis, |
etc) to these, which resulted in 1428 features |
Next, 19 |
functionals were applied to four LLD features based on the |
pitch (F0final, jitterLocal, jitterDDP, and shimmerLocal) |
as well as their corresponding four delta coefficients, re- |
sulting in 152 features |
Finally, we appended pitch onsets |
and recording duration features |
All LLD features are ex- |
tracted based on a frame-by-frame analysis, using windows |
of 25 ms with 10 ms frameshifts |
In consideration of the nature and size of our dataset, we |
adopted a support vector machine (SVM) instead of a re- |
cursive neural network |
An SVM is a method often |
adopted for categorization problems, looking to draw lines |
between categories that maximize the margin between the |
line and the closest data points in each category |
Within the |
context of the SVM, different features were tried to achieve |
the highest correlation and lowest root mean square error (RMSE) to the expert raters’ data |
The features were |
analyzed separately for different parts of the voice re- |
cordings, and then combined to make the final predictions |
Our feature set, extracted through OpenSmile, consists |
of descriptive statistics related to low-level characteristics |
These are widely utilized in various speech-related tasks |
such as emotion recognition |
The SVM model was im- |
plemented with Gaussian kernel, and we tried different |
configurations of the penalty parameter |
C[10,1,10 ]1 1 |
and kernel coefficient |
[10,10,10]4 3 2 |
The optimal |
parameters were selected based on the five-fold cross-vali - |
dation |
This entails splitting the data set into five parts |
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