Computational intelligence in chromosomal primitive extraction and speaker recognition

Abstract

This research presents an innovative approach leveraging computational intelligence for chromosomal primitive extraction and speaker recognition. The study emphasizes real-time digital signal processing (DSP) embedded systems integrating chromosomal-inspired techniques to enhance auditory feature extraction and speaker identification accuracy. By applying Gamma chromosomal factors, Mel-Frequency Cepstral Coefficients (MFCC) are refined through convolution, emulating human cochlear functionality. This integration aligns well with the perceptual auditory mechanisms and computational intelligence paradigms. The proposed methodology incorporates feature extraction techniques like Linear Predictive Coding (LPC), Linear Predictive Cepstral Coefficients (LPCC), and MFCC, followed by robust classifiers such as Support Vector Machines (SVM), Artificial Neural Networks (ANN), and Recurrent Self-Organizing Maps (RSOM). Experimental results demonstrate superior performance of RSOM, achieving a recognition rate of up to 99.7% with Gamma-enhanced MFCCs, compared to 98.6% for SVM and 91% for SOM. The RSOM model effectively identifies speakers across diverse conditions, albeit with slightly increased response times due to its dynamic recurrence loop. This work addresses challenges like environmental noise and variability in speech styles by introducing the Gamma chromosomal factor, a logarithmic nonlinear enhancement model. The experimental setup, executed on DSP boards using Python, highlights the advantages of computationally intelligent systems in real-world applications such as biometric authentication and decision-making systems. These findings underscore the potential of chromosomal-inspired computational techniques to advance speaker recognition technology, offering high accuracy and reliability in adverse conditions. Future research will focus on optimizing architectural and software frameworks to improve response times and further integrate this approach into constrained real-time systems.