Parkinson's disease (PD) is the unique forms of disorder get impacted by non-functioning neurodegenerative parts. Vocal features plays vital role in determining the PD detection in the early stages. Analyzing the vocal patterns in-depth towards the feature extraction is important. Audio data is the complex form of data having numerous peaks of threshold points that determine the covariate information about the input signal. The proposed approach is focused on deep feature analysis of vocal patterns with spectrogram and cepstral features. The input raw vocal signals are preprocessed where signals are segmented into frames and sampled into normalized frequency, channeled into two different phases of analysis. The first phase extract the features of the vocal signals using Mel Frequency Cepstral Co-efficient (MFCC) and Gammatone Frequency Cepstral Co-efficient (GFCC). The feature extracted samples are intended to calculate Sharpness, Roughness and Fluctuation strength of the input signals. The statistical measures captured using the feature maps are formulated and tested for regression with CatBoost regression algorithm (CBR). The second phase of operation performs converting voice signal into spectrogram. Further the spectrogram converted signals are separated into training images and testing images and processed with Deep Convolution Neural Network (DCNN). The predicted labels from both the phases of operations are further cross validated with N-times of repeated testing using Dual Perceptron Neural Network (DPNN). The Novel Feature Fused Regression (FFR) mapped Deep Network (DN) (FFR+DN) architecture is formed to acquire the uniqueness of the input data. The performance measure is formulated using Accuracy, Sensitivity, Specificity and MCC. The proposed approach is achieved with accuracy of 94% and compared with various existing state-of-art approaches.
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