Speaker Recognition Using MFCC-BPNN-HHO

Speaker recognition plays a pivotal role in speech processing. This paper proposes an enhancement to the Backpropagation Neural Network (BPNN) by incorporating Harris Hawks Optimization (HHO) for weight optimization, and evaluates its performance compared to the standalone BPNN. Both methods employ Mel Frequency Cepstral Coefficients (MFCC) for feature extraction from input data. The study assesses the proposed system on a dataset comprising 10 speakers, with each providing 10 utterances. Results demonstrate that the integrated MFCC-BPNN-HHO approach outperforms the standalone BPNN, achieving enhanced accuracy in speaker recognition tasks. Specifically, the accuracy of the BPNN-HHO was found to be significantly higher than that of the BPNN alone, indicating the effectiveness of the HHO optimization technique in improving speaker recognition accuracy. This study underscores the potential of integrating optimization algorithms like HHO with BPNN to further refine speaker recognition systems and contribute to advancements in speech processing technology. This approach has promising applications in access control, identity verification and other security-related domains where biometric authentication is essential.