Summary
Objectives
Computerized detection of voice disorders has attracted considerable academic and
clinical interest in the hope of providing an effective screening method for voice
diseases before endoscopic confirmation. This study proposes a deep-learning-based
approach to detect pathological voice and examines its performance and utility compared
with other automatic classification algorithms.
Methods
This study retrospectively collected 60 normal voice samples and 402 pathological
voice samples of 8 common clinical voice disorders in a voice clinic of a tertiary
teaching hospital. We extracted Mel frequency cepstral coefficients from 3-second
samples of a sustained vowel. The performances of three machine learning algorithms,
namely, deep neural network (DNN), support vector machine, and Gaussian mixture model,
were evaluated based on a fivefold cross-validation. Collective cases from the voice
disorder database of MEEI (Massachusetts Eye and Ear Infirmary) were used to verify
the performance of the classification mechanisms.
Results
The experimental results demonstrated that DNN outperforms Gaussian mixture model
and support vector machine. Its accuracy in detecting voice pathologies reached 94.26%
and 90.52% in male and female subjects, based on three representative Mel frequency
cepstral coefficient features. When applied to the MEEI database for validation, the
DNN also achieved a higher accuracy (99.32%) than the other two classification algorithms.
Conclusions
By stacking several layers of neurons with optimized weights, the proposed DNN algorithm
can fully utilize the acoustic features and efficiently differentiate between normal
and pathological voice samples. Based on this pilot study, future research may proceed
to explore more application of DNN from laboratory and clinical perspectives.
Key Words
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Article info
Publication history
Published online: March 19, 2018
Accepted:
February 6,
2018
Identification
Copyright
© 2018 The Voice Foundation. Published by Elsevier Inc. All rights reserved.
