###### Abstract  
This work is focused on deep learning methods, such as feedforward neural network (FNN) and convolutional neural network (CNN), for pathological voice detection using mel-frequency cepstral coefficients (MFCCs), linear prediction cepstrum coefficients (LPCCs), and higher-order statistics (HOSs) parameters. In total, 518 voice data samples were obtained from the publicly available Saarbruecken voice database (SVD), comprising recordings of 259 healthy and 259 pathological women and men, respectively, and using /a/, /i/, and /u/ vowels at normal pitch. Significant differences were observed between the normal and the pathological voice signals for normalized skewness (\(p\) = 0.000) and kurtosis (\(p\) = 0.000), except for normalized kurtosis (\(p\) = 0.051) that was estimated in the /u/ samples in women. These parameters are useful and meaningful for classifying pathological voice signals. The highest accuracy, 82.69%, was achieved by the CNN classifier with the LPCCs parameter in the /u/ vowel in men. The second-best performance, 80.77%, was obtained with a combination of the FNN classifier, MFCCs, and HOSs for the /i/ vowel samples in women. There was merit in combining the acoustic measures with HOS parameters for better characterization in terms of accuracy. The combination of various parameters and deep learning methods was also useful for distinguishing normal from pathological voices. p 2021 11 7149 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 1110 110 110 110 110 11  
Usually, a trained professional is needed, and this individual must undergo training in voice evaluation to accurately evaluate speech. Therefore, automatic pathological voice detection enables efficient speech evaluation in terms of time and cost, resulting in more speech impairment screening. The main motivation for realizing this work is the use of artificial intelligence to diagnose various diseases. This can lead to significant improvements in diagnosis and healthcare, as well as further improvements in human life [11; 12]. The ultimate goal of this research is to develop an intelligent pathological voice detection system that supports an accurate and objective diagnosis. This work is focused on deep learning methods, such as feedforward neural network (FNN) and convolutional neural network (CNN), for the detection of pathological speech using mel-frequency cepstral coefficients (MFCCs) and linear prediction cepstrum coefficients (LPCCs), as well as higher-order statistics (HOSs) parameters. Voice data were obtained from the publicly available Saarbrucken voice database (SVD). The author exported 518 samples from the SVD, comprising 259 healthy and 259 pathological female recordings of the /a/, /i/, and /u/ vowels at normal pitch. Additionally, 259 healthy and 259 pathological male recordings of the same vowels at normal pitch are used in this work. Normalized kurtosis and skewness are shown in the form of box plots to provide better visualizations of the normal and pathological voice signals for men and women in each /a/, /i/, and /u/ vowel. Finally, this work investigates voice performance using deep learning methods with various combinations of parameters. Therefore, the originality of this work can be found in its proposal of a new parameter and a novel deep learning method that combines HOSs, MFCCs, and LPCCs in the /a/, /i/, and /u/ voice signals of healthy and pathological individuals. The contribution of this paper can be summarized in the following points:  
* This paper introduces an intelligent pathological voice detection system that supports an accurate and objective diagnosis based on deep learning and the parameters introduced. * The suggested combinations of various parameters and deep learning methods can effectively distinguish normal from pathological voices. * A lot of experimental tests are performed to confirm the effectiveness of the pathological voice detection system using the Saarbrucken voice database.