## 2 Related Works  
A variety of voice pathology detection algorithms have been published in the literature. In this section, some of these algorithms that are closely related to our work are presented. Deep neural network (DNN)-based algorithms have been investigated in [23] to detect pathological voices. The authors investigated eight voice pathologies: vocal fold nodules, polyps, cysts, neoplasm, atrophy, vocal palsy, sulcus and spasmodic dysphonia. They have used several classification algorithms in their work. The results showed that the DNN-based classifier achieved the highest accuracy of 94.26% and 90.52% for male and female speakers, respectively. Vocal disorders, namely neoplasm, phono-trauma, and vocal palsy, have been investigated [27]. The authors have used a machine learning algorithm named dense net recurrent neural network (DNRNN) in their work. The results showed that the DNRNN algorithm achieved an accuracy of 71%. Multiple neural networks have been used in [28] to detect voice pathology. The authors have used multilayer perceptron neural network (MLPNN), general regression neural network (GRNN), and probabilistic neural network (PNN) in their work. The authors achieved the highest accuracy of 100% with the MLPNN. Some existing algorithms use multiple voice features to improve detection accuracy. For example, the researchers in [29] have used six voice features, namely jitter, shimmer, harmonic-to-noise ratio (HNR), soft phonation index (SPI), amplitude perturbation quotient (APQ), and relative average perturbation (RAP). In that study, the authors investigated several voice pathologies: cyst, edema, laryngitis, nodule, palsy, polyp, and glottis cancer. Several classifiers were used in their work, and the Gaussian mixture model (GMM)-based method provided the highest accuracy of 95.2%. A similar voice pathology detection algorithm has been presented in [30]. In the first step, the voice features, namely Mel-frequency cepstral coefficients (MFCCs), linear frequency cepstral coefficients (LFCC), and zero-crossing rate (ZCR), are extracted from the voice samples. In the second step, the voice samples are classified by using an artificial neural network (ANN). The authors claimed that their proposed algorithm requires less computation compared to other similar existing algorithms. Support vector machine (SVM) and radial basis function neural network (RBFNN) have been used in [31] to detect voice pathology. In that work, the authors used several audio features: signal energy, pitch, formant frequencies, mean square residual signal, reflection coefficients, jitter, and shimmer. Then, they combined these voice features to form a feature vector. Finally, the feature set was used for classification. The results showed that RBFNN achieved an accuracy of 91%. On the other hand, the SVM attained an accuracy of 83%. Stuttering has been addressed in [32]. In that work, the authors proposed a method to improve the performance of an automatic speech recognizer. Specifically, the authors developed a classifier that can better detect stuttering in speech signals. That work used ANN, hidden Markov model (HMM), and SVM as the classifiers. The results showed that these algorithms could detect stuttered voices with an accuracy of 85% and 78% for males and females, respectively. Four voice attributes, namely roughness, breathiness, asthma, and strain, have been addressed in [33]. The authors have proposed a method that uses the higher-order local autocorrelation (HLAC) features extracted by an algorithm called automatic topology-generated autoregressive HMM (AR-HMM) analysis. The proposed algorithm used a feed-forward neural network (FFNN)-based classifier for voice pathology detection. The achieved accuracy was 87.75%. Some researchers claimed that the spectrogram is the most suitable voice feature for pathological voice detection as it traces different frequencies and their occurrences in time. For example, spectrogram was used to detect pathological voice disorder due to vocal cord paralysis (Reinke's edema) in [21].