## 4 Discussion
### Literature Review
Their highest reported accuracy was 100% in all binary classifiers that paired the control group with one of the pathologies. In addition, classification accuracies of 99.08%, 98.86%, and 88.72% were obtained for the pairs Nodule/Neurological, Edema/Neurological, and Edema/Nodule, respectively. [PERSON] et al. [43] used the Saarbruuecken Voice Database and selected vowel/a/ recordings. For feature extraction the calculated MFCC. Classification was done using the Online Sequential Extreme Learning Machine (OSELM) algorithm. The best reported accuracy was 85%. [PERSON] [44] used the Far Eastern Memorial Hospital (FEMH) voice disorder detection challenge database which includes 50 normal voice samples and 150 samples of common voice disorders, including vocal nodules, polyps, and cysts (\"phonontauma\"), glottis neoplasm, and unilateral vocal paralysis. The voice samples are recordings for the speakers saying the vowel/a/. Feature extraction was done using MFCC. For classification, the author used the Long Short-Term Memory (LSTM) Recurrent Neural Network. The best reported result was 22% sensitivity, 97.1% specificity, and 56% unweighted average recall (UAR) [45]. [PERSON] et al. used the FEMH database. Feature extraction was done usingMFCC. The ML classifiers used are SVM, RF, KNN, Gradient Boosting (GB), and Ensemble Learning (EL). The authors applied multi-class classification and the best reported accuracy was 68.48%. [PERSON] et al. [46] used a database from a speech therapist comprising two groups of patients, for a total of 12 speakers with vocal fold nodules, 8 speakers with vocal fold paralysis, and 11 speakers with normal voices. The vocal task was the sustained vowel/a/. They used inverse filtering to obtain the glottal signal and then extracted the following features: F0, jitter, shimmer, closing phase, opening phase, normalized amplitude quotient, amplitude quotient, closed quotient, open quotient, quasi-open quotient, speed quotient difference between harmonics H1 and H2, and harmonics richness factor. The classifiers used are artificial Neural Networks (ANN), SVM, and the Hidden Markov Model (HMM). The best reported result was 97.2% with multi-class classification [47]. [PERSON] et al. used the FEMH database. MFCCs were the features extracted. Classifiers used were ANN, SVM, and GMM. The best reported accuracy was 99.32% in binary classification using ANN on the MEEI database. Table 5 reports a synoptic overview of the literature review of voice analysis for multi-pathology detection of dysphonic/neurodegenerative conditions. A thorough review by [PERSON] et al. [20] details more information regarding existing public datasets, feature toolboxes, trends in processing solutions, algorithms, and state of the art, concluding that voice analysis is still hindered by the problem of small and/or unclean datasets, and that acoustic features are often standardized into selective subsets that may contain only partial information, thus not allowing an extensive analysis. Extensive research is done on the detection of PD [4; 48; 49; 50] or ASD [51; 52; 53; 51] from voices. Here, we expand our previous research developing a novel multi-class ML approach to distinguish de novo recordings of PD, ASD, and healthy control subjects using three different classifiers that inherently handle multi-class classification. This was obtained by using vigorous testing accuracy optimization, and cross- and statistical validations. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Study** & **Database** & **Pathology** & **Vocal Tasks** & **Feature Domains** & \begin{tabular}{c} **Feature** \\ **Selection** \\ \end{tabular}  & **Classifier** & **Accuracy** \\ \hline \multirow{4}{*}{[PERSON] et al. [59]} & MEEI, PdA, & \begin{tabular}{c} PD, ASD, conversion \\ dysphonic, erythema, \\ nodules, polyps, \\ oedems, carcinomas \\ \end{tabular}  & /a/ & \begin{tabular}{c} Phonation, tongue \\ movement, speech \\ quality, spectrum, \\ wavelet, EMD \\ non-linear dynamics \\ \end{tabular}