# An Efficient SMOTE-Based Deep Learning Model for Voice Pathology Detection
###### Abstract  
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. p 2023  
/i, a, u/ vowels produced by a normal voice, the /i, a, u/ vowels with high and low pitches, and the German sentence \"Guten Morgen, wiegeht es Ihnen?\". This dataset is pretty recent and is useful for research related to voice disorders [14; 15; 16; 17; 18]. In conclusion, the distributions of pathological and normal voices are disproportionate in these two databases [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 13; 14; 15; 16; 17; 18]. Therefore, considering the accuracy metric as the evaluation result of a classifier, the performance of the constructed classification model for pathological voice may be better than the actual performance. This is because, when trained on imbalanced datasets, it is generally a biased classifier that achieves better performances for majority classes and poorer performances for minority classes [19; 20; 21]. In real-world applications, imbalanced class data are caused by insufficient and disproportionate samples in the utilized disordered voice database. This also prevents algorithms that detect various types of pathological speech from performing well in the existing VPD systems. However, collecting balanced class datasets in experiments on medical diagnosis and rehabilitation is very difficult because signal acquisition through the patient's larynxoscope and labeling through manual supervision are required. Given its importance, the diagnosis of pathological voices using disproportionate data has attracted researchers' attention [22; 23]. Therefore, when using a class-imbalanced pathological speech database, imbalanced learning methods must be implemented using oversampling to model an effective VPD system. Few studies have applied oversampling methods to the SVD and MEEI [2; 24; 25; 26]; however, here, we introduce two representative papers. Recent work has demonstrated the development of a system to automatically classify pathological and normal speech on an imbalanced class dataset, utilizing a fuzzy clustering synthetic minority oversampling technique algorithm (FC-SMOTE) [24]. The accuracy (90%) obtained from FC-SMOTE with a convolutional neural network (CNN) was superior to that of the traditional oversampling algorithms using imbalanced data [24]. [PERSON] et al. proposed CGAN-IFCM combining a conditional generative adversarial network (CGAN) and an improved fuzzy c-means clustering (IFCM) algorithm [22]. The classification ability of the CGAN-IFCM exceeded that of existing models, with an accuracy of 95.15%.