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

Abstract

A pioneering biometric authentication approach rooted deeply in voice recognition emerges for public security contexts swiftly nowadays. A hybrid DNN-HMM model emerges optimised rather cleverly by extracting acoustic features effectively via Mel frequency cepstral coefficients obviously in discussion. Novelty lies in system's capacity quite remarkably maintaining an accuracy rate above 95% in environments riddled with noise and substantial intra-speaker variability. A supervised learning architecture underpins system functionality by leveraging temporal advantages of hidden Markov models alongside discriminatory power of deep neural networks thereby enabling processing in real time. Findings suggest resilience against voice cloning and deepfake attacks gets a boost alongside expedited authentication procedures dovetailing with stringent cybersecurity operational protocols now. Model design strictly complies with confidentiality ethics and explicit consent standards regarding voice data. Efforts made towards improving algorithmic fairness amidst diverse accents and dialects have been highlighted through an exploratory analysis of linguistic biases undertaken painstakingly. Future prospects include integration with other biometric systems pretty soon and extension via cloud infrastructures ensuring scalability remarkably well. This breakthrough signifies substantial progress in intelligent voice authentication offering reconciliation of security with technological prowess and moral rectitude quite harmoniously.