EVALUATION OF THE SCALABILITY OF VOICE EMBEDDING MODELS IN BIOMETRIC SPEAKER VERIFICATION SYSTEMS

Abstract

The rapid expansion of digital platforms in the financial sector, public administration, e-commerce, and service systems has created a growing demand for highly reliable and scalable user authentication technologies. In this context, biometric methods — particularly voice-based authentication systems — demonstrate significant potential due to their natural ease of interaction, minimal hardware requirements, and seamless integration into voice-driven interfaces. However, the increasing number of users and the diversity of usage scenarios introduce new challenges for researchers and developers. Modern systems must ensure high accuracy in real time, maintain stable performance as data volumes grow, and provide resilience against cyberattacks, including those involving synthetic or manipulated speech. A critical requirement is the ability of models to generate compact, invariant, and robust voice embeddings that enable efficient comparison and classification within large-scale databases. This paper presents a comparative analysis of the scalability of contemporary neural architectures for speaker verification, with emphasis on their performance, computational complexity, and behavior as the number of enrolled users increases. The study examines model optimization techniques, indexed embedding-based search methods, and the role of representative multilingual corpora in enhancing accuracy under conditions of acoustic and linguistic variability. Particular attention is given to protection against spoofing attacks and the use of specialized synthetic speech detection methods as an essential component of scalable voice biometric systems. The results highlight the need for a comprehensive approach to designing modern voice authentication systems, in which architectural engineering decisions are combined with requirements for information security, high performance, and adaptability in the rapidly evolving landscape of digital services.