Non-Invasive Voice Disorder Detection

Lim Kee Boon

Temasek Polytechnic
Singapore

Gina Tan Ci En

Muhammad Adam

Temasek Polytechnic
Singapore

1906371H@student.tp.edu.sg 1906315E@student.tp.edu.sg
1. INTRODUCTION

Dysphonia is an alteration of voice production due to amorphological or functional alteration of the pneumo-articulatory
apparatus. Moreover, dysphonia affects great number of people,
with about one third of adults suffering from this disorder at least
once in their lifetime [1].
The conventional method used to diagnose dysphonia is via the useof laryngoscopy. However, the laryngoscopy examination is aninvasive procedure performed by an experienced laryngologist.
Furthermore, the equipment required is costly and not commonly
found in primary care units [2].

Temasek PolytechnicSingapore

muhdadam@tp.edu.sg

Table 1. The studies on voice disorder detection using voices.
Author/YearMethod

Best Result

Ji-Yeoun Leeet al., 2021
[3]
Wu et al.,2018 [4]CNN with linear prediction
cepstrum coefficients(LPCCs)82.69% accuracy

CNN

66.2% accuracy

The aim of this study is to provide an alternative approach, whichis efficient and non-invasive. The project proposed to develop acomputer aided diagnosis (CAD) system to classify pathologicaland healthy voices from voice signal using Multi-layer
Perceptron (MLP) and Convolutional neural network (CNN).

1.1 Literature Review

The studies on voice disorder detection using voices as listed inTable 1.

2. MATERIALS AND METHODS
The following are the materials used:VOICED (VOice ICar fEDerico II) database [1]

58 healthy and 150 pathological voices

The following are the methods used:Noise removal and downsamplingMulti-Layer Perceptron (MLP)1-Dimensional Convolutional Neural Network (1D CNN)

3. RESULTS

The healthy and pathological voice signals initially undergone
noise removal and downsampled from 8000 Hz to 1000 Hz. This
study experimented on both raw and preprocessed voice signals toseparately train and test the baseline MLP and 1D CNN models of
various layers. Further, the models employed model improvement
methods, such as the callbacks, dropout and weight regularization,
together with K-fold cross validation and confusion matrix.
Overall, 1D CNN model of one layer with callbacks of patience=50
and weight regularization yielded the best validation accuracy of
82.76% and training accuracy of 68.97% at epoch 121 as shown inFigure 1.

Figure 1. The classification performance of 1D CNN
one-layer model with callbacks and weight
regularization using denoised voices.

ACKNOWLEDGMENTS

We deeply appreciate for the sponsors of HPC Asia 2022.

REFERENCES[1] Cesari, U., Pietro, G.D., Marciano, E., Niri, C., Sannino, G.,& Verde, L. (2018). A new database of healthy andpathological voices. Comput. Electr. Eng., 68, 310-321.
[2] Fang, S., Tsao, Y., Hsiao, M., Chen, J., Lai, Y., Lin, F., &Wang, C. (2018). Detection of Pathological Voice Using
Cepstrum Vectors: A Deep Learning Approach. Journal ofvoice: official journal of the Voice Foundation.
[3] Lee, Ji-Yeoun. "Experimental Evaluation of Deep Learning
Methods for an Intelligent Pathological Voice Detection
System Using the Saarbruecken Voice Database." AppliedSciences 11.15 (2021): 7149.
[4] Wu, H., Soraghan, J., Lowit, A., & Di Caterina, G. (2018,July). Convolutional neural networks for pathological voicedetection. In 2018 40th annual international conference of theieee engineering.