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A Machine-Learning Algorithm for the Automated Perceptual |
Evaluation of Dysphonia Severity |
#,⁎Benjamin van der Woerd, †Zhuohao Chen, †,1Nikolaos Flemotomos, ‡Maria Oljaca, §Lauren Timmons Sund, |
†,§Shrikanth Narayanan, and §Michael M |
Johns, *Hamilton, Canada, and †‡§Los Angeles, California |
Summary: Objectives |
Auditory-perceptual assessments are the gold standard for assessing voice quality |
This project aims to develop a machine-learning model for measuring perceptual dysphonia severity of audio |
samples consistent with assessments by expert raters |
Methods |
The Perceptual Voice Qualities Database samples were used, including sustained vowel and |
Consensus Auditory-Perceptual Evaluation of Voice sentences, which were previously expertly rated on a 0–100 |
scale |
The OpenSMILE (audEERING GmbH, Gilching, Germany) toolkit was used to extract acoustic (Mel- |
Frequency Cepstral Coefficient-based, n = 1428) and prosodic (n = 152) features, pitch onsets, and recording |
duration |
We utilized a support vector machine and these features (n = 1582) for automated assessment of |
dysphonia severity |
Recordings were separated into vowels (V) and sentences (S) and features were extracted |
separately from each |
Final voice quality predictions were made by combining the features extracted from the |
individual components with the whole audio (WA) sample (three file sets: S, V, WA) |
Results |
This algorithm has a high correlation (r = 0.847) with estimates of expert raters |
The root mean square |
error was 13.36 |
Increasing signal complexity resulted in better estimation of dysphonia, whereby combining the |
features outperformed WA, S, and V sets individually |
Conclusion |
A novel machine-learning algorithm was able to perform perceptual estimates of dysphonia se- |
verity using standardized audio samples on a 100-point scale |
This was highly correlated to expert raters |
This |
suggests that ML algorithms could offer an objective method for evaluating voice samples for dysphonia se- |
verity |
Level of Evidence |
4 |
Key Words: Machine learning–Voice evaluation–Perceptual voice evaluation–Automation–Artificial in- |
telligence |
BACKGROUND |
Structured voice evaluation is a critical component of as- |
sessing patients with dysphonia |
Comprehensive assess - |
ment typically includes both perceptual and instrumental |
assessments |
Auditory-perceptual analysis represents the |
gold standard for the assessment of dysphonia severity |
It is |
inexpensive and robust.1,2 This method of voice assessment |
is widely accepted in clinical applications as well as research |
purposes.2-4 |
Despite the widespread use of perceptual evaluations, it |
remains a subjective assessment and raters will develop |
their own internal reference standards with inherent biases, |
which impact the judgment of future voice samples.5 |
These internal standards can vary across time and between different raters, highlighting one critique of this form of |
voice assessment |
, namely reliability |
Through standardized |
scales, such as the Consensus Auditory-Perceptual Eva- |
luation of Voice (CAPE-V) tool, high levels of consistency |
within and across raters can be achieved.6,7 With tools such |
as this, small-scale changes from sample to sample can be |
reliably detected.4,6,8 Reliability of auditory-perceptual |
evaluations has been extensively researched and, when |
confounding variables are controlled, they have been |
proven a robust form of voice assessment.2,4,6,8,9 |
Expert raters are important in th |
e reliability of these |
assessments.2,10-12 Speech pathology assessment is a time- |
limited resource and voice evaluations are limited to the |
times patients can provide voice samples |
Furthermore, |
these assessments typically rely on in-person voice samples, |
though some research suggests that remote sample collec - |
tion from non-optimized settings may be adequate for |
clinical assessment.13,14 These restrictions indicate a re- |
source bottleneck in these evaluations |
A computer-auto - |
mated perceptual evaluation tool may provide an |
opportunity to relieve the resource limitations and objec - |
tively measure voice samples |
This might allow for interval |
evaluations between in-person visits, which could increase |
the total number of assessments, and ultimately could |
allow for within-person normative values as targets for |
tracking therapeutic improvement or decline |
Recent advancements in machine learning methods have |
led to many medical applications, including applications Accepted for publication June 7, 2023 |
Journal of Voice, Vol xx, No xx, pp |
xxx–xxx |
0892-1997 |
© 2023 The Voice Foundation |
Published by Elsevier Inc |
All rights reserved |
https ://doi.org/10.1016/j.jvoice.2023.06.006 Presented as podium presentations at the Canadian Society of Otolaryngology |
Annual General Meeting 2022 in Vancouver, British Columbia, Canada and Fall |
Voice Conference 2022 in San Francisco, California, USA |
From the #Department of Surgery, Division of Otolaryngology—Head & Neck |
Surgery, McMaster University, Hamilton, Ontario, Canada; †Department of |
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