# An Efficient SMOTE-Based Deep Learning Model for Voice Pathology Detection
###### Abstract
The synthetic minority oversampling technique (SMOTE) is the oversampling method most commonly used to manage imbalanced data [27]. The SMOTE improves the random oversampling (ROS) method to reduce the risk of overfitting, but it can be a source of overgeneralization [28]. The proposed SMOTE-based methods include Borderline-SMOTE [29], adaptive synthetic sampling (ADASYN) [30], and Random-SMOTE [31]. However, most of these approaches are highly complex or mitigate only some of the drawbacks of SMOTE [32]. In imbalanced deep learning scenarios, overlapping between the classes and unclear boundaries affects the performance of oversampling techniques. Eventually, noisy samples without an information value are generated. In particular, the k-nearest neighbors algorithm can be useful for a pathological voice dataset with ambiguous boundaries and overlapping between classes [33]. Thus, we use this advantage of the k-nearest neighbors method in imbalanced learning as an integrated technique for oversampling tasks. Furthermore, related studies [32] mainly use the k-nearest neighbors algorithm. Various oversampling methods based on the k-nearest neighbors algorithm are used in this study; these methods can overcome the influences of unclear points and noise and the simultaneously imbalanced distributions within and between classes. Finally, representative minority samples with as many discriminating features as possible should be generated to increase the accuracy of pathological voice classification models. Most pathological voice detection and intelligent diagnostic studies typically use MEEI and SVD as standard databases for VPD systems, but they do not consider imbalances in the number of pathological and normal samples in each database [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 14; 15; 16; 17; 18]. Accuracy is not suitable for an imbalanced class dataset because it makes model performances inaccurate as a measure of model evaluation. To address these problems, this work proposes a VPD system that combines various oversampling methods based on SMOTE, ADASYN, and Borderline-SMOTE and deep learning models such as CNN and feedforward neural network (FNN) to classify between normal and pathological voices with the imbalancedSVD. In addition, this study proposes oversampled linear predictive coefficients (LPCs) and mel-frequency cepstral coefficients (MFCCs) as the input parameters of the model. Then, the balanced class datasets with the oversampled MFCC or LPC inputs are utilized to validate and evaluate the combinations of deep learning models in the modeling phase of the VPD system. Finally, evaluation metrics [34] such as recall, specificity, G, and F1 are presented for the classifier model. The contributions of this study can be summarized as follows:  
* This paper introduces efficient deep learning models based on various oversampling methods, such as the SMOTE, Borderline-SMOTE, and ADASYN, and directly applies them to feature parameters for VPD. * The suggested combinations of the oversampled MFCCs, LPCs, and deep learning methods can efficiently classify pathological and normal voices. * Several experiments are conducted to verify the usefulness of the developed VPD system using the SVD. * The results highlight the excellence of the proposed classification system, which integrates a CNN and LPCs based on the SMOTE in terms of monitoring voice disorders; it is an effective and reliable system.