Optimizing Speech Scrambling through Nature-Inspired Algorithms

This research explores the integration of nature-inspired optimization algorithms into speech scrambling techniques to enhance their efficiency and robustness. It can be difficult to strike a compromise between security, computational complexity, and scrambling quality when using traditional voice scrambling techniques. This paper suggests using nature-mimicking algorithms to optimise speech scrambling procedures, including Particle Swarm Optimisation, Ant Colony Optimisation, and Genetic Algorithms. By using these algorithms, speech data secrecy is increased, computational overhead is reduced, and encryption quality is improved. As part of the research methodology, these algorithms' capacity to adapt to speech scrambling is thoroughly examined, and their effects on computing efficiency and security are evaluated. The outcomes of the experiments show encouraging progress in speech scrambling optimisation while preserving high-grade encryption, establishing nature-inspired optimisation algorithms as a feasible method for enhancing the efficacy of speech confidentiality measures in many contexts.