HPO Based Enhanced Elman Spike Neural Network for Detecting Speech of People with Dysarthria

Abstract

Motor speech condition called dysarthria is caused by a lack of movement in the lips, tongue, vocal cords, and diaphragm are a few of the muscles needed to produce speech. Speech that is slurred, sluggish, or inaccurate might be the initial sign of dysarthria, which varies in severity. Parkinson’s disease, muscular dystrophy, multiple sclerosis, brain tumors, brain damage, and amyotrophic lateral sclerosis are among the health problems that can result from dysarthria. This research develops an efficient method for extracting features and classifying dysarthria affected persons from speech signals. This suggested method uses a speech signal as its source. The supplied speech signal is pre-processed to improve the identification of dysarthria speech. Pre-processing methods like the Butterworth band pass filter and Savitzky Golay digital FIR filter are used to smoothing the raw data. After pre-processing, the signals are input into the feature extraction techniques, such as Yule-Walker Autoregressive modelling, Mel frequency cepstral coefficients and Perceptual Linear Predictive to extract the important features. The dysarthria speech is finally detected using an improved Elman Spike Neural Network (EESNN) algorithm-based classifier. Hunter Prey Optimization (HPO) is used to select the weights of EESNN optimally. The proposed algorithm achieves 94.25% accuracy and 94.26% specificity values. Thus this proposed approach is the best choice for predicting dysarthria disease using speech signal.