Optimized intelligent speech signal verification system for identifying authorized users

Speech processing is today's trending topic in the digital industry for making authentication to keep aware of unauthorized ones. However, analyzing the signal feature using conventional filtering or the neural models is insufficient due to the present signal's several noisy features. Hence, incorporating the different noise elimination filters has maximized the algorithm complexity in verifying the user speech signal. So, the present study built a novel Chimp-based recursive Speech Identification (CbRSI) system for the speech processing domain to verify the authenticated users through the speech signal data. To make signal processing the most straightforward task was activated the filtering function to recognize and neglect the noisy features. Consequently, the filtered audio data is imported as the classification phase input then the features-selecting process is performed. Finally, authenticated users were found and validated the performance by matching the analyzed signal features with the saved audio features. Hence, a novel CbRSI earned the finest user verification exactness score of 98.2%, which is the most satisfactory outcome compared to past studies. Therefore, the implemented solution is the most required framework for verifying authenticated users.