Recent research in improving speaker verification systems to detect spoofed speech has seen a concentrated focus on English language, while the performance of such systems in other languages remains unexplored.This paper introduces the VSASV dataset for Spoofing-Aware Speaker Verification (SASV) in Vietnamese language.The dataset comprises over 174,000 spoofed utterances and 164,000 authentic utterances from 1,382 speakers, which were generated with the latest spoofing techniques to encourage the development of SASV systems in this language.We also provide experimental results on the efficacy of the different state-of-the-art anti-spoofing systems on Vietnamese language.

We propose a modified Fitzhugh-Nagumo neuron (MFNN) model. Based on this model, an integer-order MFNN system (case A) and a fractional-order MFNN system (case B) were investigated. In the presence of electromagnetic induction and radiation, memductance and induction can show a variety of distributions. Fractional-order magnetic flux can then be considered. Indeed, a fractional-order setting can be acceptable for non-uniform diffusion. In the case of an MFNN system with integer-order discontinuous magnetic flux, the system has chaotic and non-chaotic attractors. Dynamical analysis of the system shows the birth and death of period doubling, which is a sign of antimonotonicity. Such a behavior has not been studied previously in the dynamics of neurons. In an MFNN system with fractional-order discontinuous magnetic flux, different attractors such as chaotic and periodic attractors can be observed. However, there is no sign of antimonotonicity.