Assessment of quality degradation in modern voice deepfake detectors under cross-linguistic shift from English to Ukrainian - Vynogradov, Ivan (Technology audit and production reserves, 2026)
The research object is the processes and algorithms of automated discrimination between real and synthesized speech (anti-spoofing systems) when they function in conditions of a pronounced linguistic shift. The research solves the scientific and practical issue of quantitatively assessing the critical degradation of the precision of modern neurolinguistic detectors, using the example of the AASIST architecture with graph attention, when they encounter high-quality voice attacks in the Ukrainian. Special attention was paid to attacks formed using industrial new-generation neural vocoders, which are practically not represented in classic English training samples. The essence of the obtained results lies in establishing and mathematically confirming the existence of a significant “generalization gap” in cross-language testing. It was experimentally proven that the transition from the English acoustic domain into the Ukrainian domain causes the growth of the equal error rate (EER) coefficient by 2.5–3.5 times. In the most advanced synthesis systems, the EER reached a critical threshold of 25.64%, which indicates the loss of the system’s protective capabilities in this language domain. These results were obtained through the usage of an experimental stand, which unites the AASIST model and closed-end commercial APIs of neural speech cloning. Unlike standard tests on archive databases, the suggested approach, using a specially EXT dataset that includes five independent attack groups, allowed for modeling real cyber threat scenarios. In practice, these results can be used in the design of voice biometric authentication systems in the banking and governmental sectors of Ukraine and justify the mandatory necessity of linguistic adaptation and deep fine-tuning of classifiers using localized datasets to achieve the required level of information security.