In recent years, speaker verification technologies have received an extensive amount of attention. Designing and developing machines that could communicate with humans are believed to be one of the primary motivations behind such developments. Speaker verification technologies are applied to numerous fields such as security, Biometrics, and forensics.

In this paper, the authors study the effects of different languages on the performance of the automatic speaker verification (ASV) system. The MirasVoice speech corpus (MVSC), a bilingual English and Farsi speech corpus, is used in this study. This study collects results from both an I-vector based ASV system and a GMM-UBM based ASV system. The experimental results show that a mismatch between the enrolled data used for training and verification data can lead to a significant decrease in the overall system efficiency. This study shows that it is best to use an i-vector based framework with data from the English language used in the enrollment phase to improve the robustness of the ASV systems. The achieved results in this study indicate that this can narrow the degradation gap caused by the language mismatch.