Speech Signal Based Diagnostic Framework for Parkinson's Disease Classification using Particle Swarm Optimization

Parkinson's Disease (PD) which is considered as a progressive neurodegenerative disorder which affects the motor control and speech of the affected person. Speech signal analysis is considered as one of the promising non-invasive biomarkers for early PD diagnosis. However, the non-availability of accent specific datasets, for Indian-Accented Speech raises as an important limitation in developing rigorous diagnostic models for the prediction of PD. In this research work, we propose a synthetic dataset for speech signal for PD with Indian accent augmentation. This dataset is created without the necessity of real patient recordings. Healthy Indian-accented speech signals were generated using text-to-speech (TTS) systems and then they are transformed with pathology aware acoustic modifications to emulate characteristics pertaining to PD. The PD specific characteristics includes jitter, shimmer, reduced harmonics-to-noise ratio (HNR), monopitch, vowel-space reduction and festination. The dataset includes several tasks such as sustained vowels, diadochokinetic sequences, numbers and read passages which are annotated with several levels of severity like mild, moderate, severe and transformation parameters. Experiments and validation suggested that the synthetic samples matched closely with the PD feature distribution like increased jitter and shimmer and also lowered HNR. This confirms the realism of the acoustic degradation. A basic SVM which is trained on MFCC and wavelet features produced an average classification accuracy of 85.6% which demonstrates that the data set is discriminative between healthy and synthetic PD speech signals. The produced results is a resource for augmenting PD speech corpora and thus enabling a robust AI-driven diagnostic framework for Indian PD diagnosis in clinical application.