Optimizing Voiceprint Modelling for Biometric Authentication and Security: Applications in Public Safety and Surveillance

Abstract

A novel biometric authentication framework based on voice recognition has recently gained prominence for applications in public security. This system employs a hybrid Deep Neural Network–Hidden Markov Model (DNN-HMM) architecture, optimized through the effective extraction of acoustic features using Mel-Frequency Cepstral Coefficients (MFCC). The distinctive innovation of this model lies in its ability to sustain an accuracy rate exceeding 95%, even under conditions of environmental noise and high intra-speaker variability. The system leverages a supervised learning framework that integrates the temporal modeling strengths of hidden Markov models with the discriminative capabilities of deep neural networks, thereby enabling real-time processing. Experimental results show that the system effectively resists threats like voice cloning and deepfake attacks, while also accelerating authentication procedures to meet strict cybersecurity standards. The model strictly adheres to confidentiality and informed consent requirements for voice data. Recent efforts to enhance algorithmic fairness have focused on mitigating linguistic biases related to diverse accents and dialects through comprehensive exploratory analyses. Future directions include integrating the system with multimodal biometric frameworks and expanding deployment via cloud-based infrastructures to ensure scalability. This advancement marks a significant step in intelligent voice authentication, harmonizing technological innovation with ethical accountability and robust security principles