## 2 State of the Art Review of Signal Analysis Based Approaches for Analyzing Parkinson's Disease
As previously indicated in the main body of the state-of-the-art review, numerous aspects of voice are included in the voice characteristics of Parkinson's disease patients, further varying in relation to the native tongue of the speaker. The second step in DL-based approaches is to apply a classification or analysis to the retrieved voice characteristics, as vocal biomarkers are a potentially useful technique of monitoring symptoms and severity [57]. This section thus examines DL-related models and the effectiveness of strategies used for PD identification from voice. [PERSON] et al., tested multiple architectures of the 1D CNN model to accurately detect the illness using extracted speech data and obtained about 87% accuracy [58]. Sparse kernel transfer learning was proposed by [PERSON] et al. [59] to extract the effective structural information of PD speech characteristics from public datasets as source domain data. They also used a quick alternating direction multiplier iteration strategy to improve information extraction performance, attaining an overall accuracy of 86.7%. Similarly, [PERSON] et al. proposed a deep dual-side learning ensemble model with a weighted fusion mechanism to fuse the classification models into a classification ensemble model, attaining 98.4% accuracy [60]. On the [PERSON] dataset, [PERSON] et al. [61] used traditional CNN with an accuracy of 93.10%. An ensemble of convolutional neural networks with Gaussian blurring allowed a further increase in accuracy [62]. Similarly, the spectrum of the audio recordings was computed and utilized as an image input to the pre-trained ResNet18 architecture using the ImageNet and SVD databases with an accuracy of 97.1% in Ref. [63]. Alexnet adapted by Ref. [64] showed a very similar performance, while Densen had lower accuracy, albeit on a different dataset [65]. Combining the CNN classifier with additional iterative adaptive inverse filtering (IAIF) and quasi-closed phase (QCP) glottal inverse filtering algorithms can assist in utilizing the baseline and glottal information generated from each spoken utterance and the related healthy/PD classifications [66]. [PERSON] et al. [67] proposed adopting a method in which weak learners are bagged and boosted before making predictions, which will certainly provide better outcomes than basic ensemble voting and stacking. The suggested technique achieved an accuracy of 94.12% by enhancing ensemble classifiers. [PERSON] et al. [68] using UCI's Parkinson's Telemonitoring Voice Data Set of patients, suggested an approach for predicting [PERSON]'s disease severity using deep neural networks with 82% accuracy in controlled conditions, while a very similar approach with mRMR feature selection improved the accuracy levels on the same dataset [69]. [PERSON] et al. [70] evaluated the potential of a deep neural network (DNN) and long short-term memory (LSTM) network-based model for predicting [PERSON]'s illness using speech samples from a person with 97.1% accuracy. [PERSON] et al., proposed an improvement, using a Bidirectional long-short term memory (LSTM) model to identify PD by capturing time-series dynamic aspects of a speech stream [71]. The authors of Ref. [72] developed an SSWA-based attention-based LSTM that provided improved performance with 92.5% accuracy on an Indian language dataset. The experiments by [PERSON] et al. [73] showed that DF-EMD can be utilized to identify PD efficiently since the high-frequency section of the speech signal carries more information concerning PD. [PERSON] and [PERSON] investigated the capability of acoustic-based DL approaches, discovering that combining these techniques results in around a 3% improvement in performance [74].