## 2 State of the Art Review of Signal Analysis Based Approaches for Analyzing Parkinson's Disease
Identity confounding is a less well-known issue in which several voice samples are obtained from each subject and these samples appear in both training and testing data, therefore because the model has trained to identify features of certain subjects and is utilizing that knowledge to identify the label in the test set, the model's performance is too optimistic. Furthermore, adopting a simple classifier does not lead to high prediction accuracy, thus deep learning based techniques for improving model performance include data normalization [45], feature selection, and feature extraction [46]. This is a sort of data leakage in which information is transferred between the training and test sets accidentally, resulting in exaggerated performance measures [47]. More fundamentally, studies that merely categorize patients as having Parkinson's disease or not having it have limited value in terms of enhancing their quality of life. Although techniques to improve diagnostic accuracy are needed, diagnosis is usually achieved only after the disease has advanced to a more severe stage, i.e., when symptoms appear. [PERSON] et al. [48] proposed to address the above-mentioned issues, with the aim of creating a hybrid system capable of automatically performing acoustic analysis of voice signals to identify PD. The suggested smart system employs linear discriminant analysis to reduce dimensionality and a genetic algorithm to optimize the hyperparameters of a neural network utilized as a predictive model. Furthermore, to eliminate subject overlap, they excluded one participant from validation and attained a 95% accuracy. [PERSON] et al. [31] attempted to investigate the scalability of voice as a population screening tool for PD, and performed the Parkinson's Speech Initiative (PVI) research, a PD characterization study using telephone-quality voice. PVI was the first large-scale study of its sort to gather speech data from PwP and control participants in free-living acoustic circumstances, with the goal of distinguishing PD individuals from controls using phonations acquired in non-acoustically controlled situations. [PERSON] et al. [49] used two complexity measures: fractal dimension (FD) and normalized mutual information (NMI) to compare the voices of Parkinson's disease (PD) and age-matched control (CO) subjects when uttering three phonemes. They found that the voices of PD patients have lower FD and NMI between voice recordings of PD-CO and PD-PD is higher than CO-CO. This shows that using NMI from the sample voice in combination with the known CO and PD groups can be utilized to detect PD sounds. [PERSON] et al. [50] proposed an ensemble of convolutional neural networks for detecting PD from speech samples. For the dataset, the solution obtained 99% accuracy. To tackle the problem of imbalanced data samples, [PERSON] et al. [51] employed a Synthetic Minority Over-Sampling Technique, followed by a Random Forest model to categorize the samples. [PERSON] et al. [52] achieved an accuracy of roughly 94% for a Wrappers feature subset selection preceding an SVM. [PERSON] et al. [53] acquired a greater efficiency by using a KNN technique followed by minimal average maximum tree and singular value decomposition as feature extractors. According to the findings of [PERSON] et al. [54], automated analysis of various phonemes should be used. An approach for categorizing PD that is sensitive to privacy has been presented by [PERSON] et al. [55]. Voice characteristics collected from running speech signals were recovered from passively recorded voice calls. To fuse and predict speech characteristics, language-aware training of multiple- and single-instance learning classifiers was used, yielding an AUC of 0.84. Overview of Deep Learning Based Approaches to PD Speech Analysis  
Deep learning (DL) architectures and algorithms are rapidly being employed in research to handle complex PD voice signal processing issues [56]. The initial step is to convert the input speech stream into voice feature vectors or tensors that DL models can assess.