A Novel Machine Learning-Based Artificial Voice Box

Patients may experience a great deal of discomfort while undergoing rigorous medical procedures for the identification of vocal abnormalities. As a result, there has been a lot of interest in automated speech recognition and disorder detection approaches in recent years, and these methods have shown to be effective. Voice recordings have been acquired from the Saarbruecken Voice Database for the purpose of this study. The signals undergo preprocessing using Hybrid Wiener Filter Discrete Wavelet Transforms in order to de-noise and eliminate any silence that may have been there (HWFDWT). Cat Swarm Optimization is used to extract features, and Mel Frequency Cepstrum Coefficients are taken into account (CSOMFCC). Classification using Modified Optimized Back Propagation Network Disorder voice Classification is then used to sort the features in the end (MOBPNDC). In terms of Accuracy, Precision, Recall, F-Measure, and Time period, the classification scheme beats the current Support Vector Machine (SVM) and Back Propagation Neural Network (BPNN) approaches. The neural speech system is a gadget that enables individuals who are unable to talk to communicate their thoughts and emotions with the outside world. It is a piece of equipment that can record the electric pulses that are generated by the brain and turn them into a synthetic voice. - Provide an overview of the concept or solution that you want to build. The electrical activity of the brain will be recorded and then sent into a synthesiser. The Synthesizer will convert the signal into voice when it has finished decoding it. The voice that has been deciphered is then supplied to an artificial voice box. The brain's electrical activity is used to generate an artificial voice, which is then output via the box.