An Efficient SMOTE-Based Deep Learning Model for Voice Pathology Detection

被引:8
作者
Lee, Ji-Na [1 ]
Lee, Ji-Yeoun [2 ]
机构
[1] Seokyeong Univ, Div Global Business Languages, Seoul 02173, South Korea
[2] Eulji Univ, Dept Bigdata Med Convergence, 553 Sanseong daero, Seongnam Si 13135, South Korea
来源
APPLIED SCIENCES-BASEL | 2023年 / 13卷 / 06期
基金
新加坡国家研究基金会;
关键词
pathological voice; disordered voice; imbalanced learning; voice pathology classification; SMOTE; ADASYN; Borderline-SMOTE; deep learning; intelligent medical diagnosis system; DISEASE DETECTION; IMBALANCED DATA;
D O I
10.3390/app13063571
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
The Saarbruecken Voice Database (SVD) is a public database used by voice pathology detection systems. However, the distributions of the pathological and normal voice samples show a clear class imbalance. This study aims to develop a system for the classification of pathological and normal voices that uses efficient deep learning models based on various oversampling methods, such as the adaptive synthetic sampling (ADASYN), synthetic minority oversampling technique (SMOTE), and Borderline-SMOTE directly applied to feature parameters. The suggested combinations of oversampled linear predictive coefficients (LPCs), mel-frequency cepstral coefficients (MFCCs), and deep learning methods can efficiently classify pathological and normal voices. The balanced datasets from ADASYN, SMOTE, and Borderline-SMOTE are used to validate and evaluate the various deep learning models. The experiments are conducted using model evaluation metrics such as the recall, specificity, G, and F1 value. The experimental results suggest that the proposed voice pathology detection (VPD) system integrating the LPCs oversampled by the SMOTE and a convolutional neural network (CNN) can effectively yield the highest accuracy at 98.89% when classifying pathological and normal voices. Finally, the performances of oversampling algorithms such as the ADASYN, SMOTE, and Borderline-SMOTE are discussed. Furthermore, the performance of SMOTE is superior to conventional imbalanced data oversampling algorithms, and it can be used to diagnose pathological signals in real-world applications.
引用
收藏
页数:16
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