## 2 Related Works
The authors used CNN as the classifier in their work. The spectrograms of pathological and control speech were applied to the input of a convolutional deep belief network (CDBN). The results showed that a small dataset was enough to train the CDBN and to achieve high accuracy. The authors achieved 77% and 71% accuracy for CNN and CDBN, respectively. In [34], the author have also used spectrogram. They argued that voice pathology detection using spectrogram is affected by jitter, shimmer, and HNR. However, the measurement of jitter, shimmer, and HNR is not independent and may provide ambiguous information. For example, the addition of random noise increases the jitter measurement, and the introduction of jitter leads to a reduction in HNR. The authors suggested removing the effects of jitter and shimmer on the speech spectrum to improve the detection accuracy of voice pathology by using spectrogram. To identify dysphonic voice, a new marker, called the dysphonic marker index (DMI), has been introduced in [35]. This marker consists of four acoustic parameters. The authors have employed a regression algorithm to relate these features, and they defined a threshold value to discriminate pathological voices from healthy ones. They have achieved an accuracy of 82.2% for the classification task. A novel computer-aided pathological voice classification system was proposed in [36]. In that work, the authors used a deep-connected ResNet for classification. The model used two databases and achieved almost similar accuracies (81.6% and 82.2%) for both databases. Hence, the authors concluded that the proposed method is data-independent. A long-short-term memory (LSTM) auto-encoder hybrid model with a multi-task learning solution was presented in [37]. The authors used dysphonia, depression, and Parkinson's voice samples. The spectrogram features were extracted from the voice features and applied to the classifiers' input for the voice pathology detection. The proposed method achieved an accuracy of 85% for all samples related to the dysphonia, depression, and Parkinson's. The online sequential extreme leaning machine (OSELM) was used in [38] to detect voice pathology. The authors used 600 vowel samples. They extracted MFCC features from the samples and applied them to the OSELM for classification. The algorithm in [38] achieved an accuracy of 85%, sensitivity of 87%, and specificity of 87%. Deep learning algorithms, namely feed forward neural network (FNN) and CNN, were used in [39] to detect voice pathology. Three voice features, namely the MFCCs, linear prediction cepstral coefficients (LPCCs), and higher-order statistics (HOSs), of 518 vowel samples (259 healthy and 259 mixed pathologies) were used in that work. The authors used '/a/', '/i/', and '/u/' vowels at normal pitch in the work. They achieved the highest accuracy of 82.69% using the CNN with the LPCCs of the vowel sound '/u/' of male samples. The CNN and MFCCs as features were used in [40] to discriminate pathological voices from normal voices. The work in [40] used the vowel sample '/a/' of 189 normal and 552 pathological samples. The work investigated four pathologies: vocal atrophy, unilateral vocal paralysis, organic vocal fold lesions, and adductor spasmodic dysphonia. The results showed an overall accuracy of 66.9%, a sensitivity of 66%, and a specificity of 91% with their algorithm. A pre-trained network (ResNet34) was used in [41]. The authors used 150 healthy and 150 pathological '/a/' vowel samples. First, the vowel samples were framed and windowed. Then, the spectrograms were computed from the samples, and the pre-trained network was used for classification. The authors tested the proposed algorithm with 200 healthy and 874 pathological samples. The authors achieved an accuracy of 95.41%, an F1 score of 94.22%, and a recall of 96.13% in the work. The performances of two algorithms, namely CNN and RNN, were compared in [42]. The authors used the vowel samples in that work.