Optimizing Voice Biometric Verification in Banking with Machine Learning for Speaker Identification

Biometric verification is essential for secure identity verification and authentication during banking transactions using fingerprints, facial features, irises, and voices. Among these methods, voice biometrics is a promising alternative owing to its potential for robust and convenient user authentication. However, their effectiveness is significantly challenged by variations in the voice caused by different device configurations and environmental conditions. These variations can reduce the effectiveness of speaker identification and undermine the reliability of voice-based systems for securing online transactions. For an effective comparative solution, this study addresses these challenges by focusing on the difficulties posed by voice variations due to differences in device hardware, microphone quality, and environmental noise. Our approach employs machine-learning techniques using advanced speech enhancement methods to improve the consistency and accuracy of voice biometric verification across diverse devices. Specifically, we employ an adaptive filter model that enhances signal extraction, noise suppression, and predictive precision. Furthermore, our empirical demonstration showed that the adaptive filter significantly improved the accuracy of voice biometric systems by mitigating the impact of device-induced voice variations. In addition, we evaluate the performance of this model using a range of metrics.