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 a
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