In recent years, speaker verification technologies have received an extensive amount of attention. Designing and developing machines that could communicate with humans is believed to be one of the primary motivations behind such developments. Speaker verification technologies apply 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 corpus used in this study is the MirasVoice speech corpus (MVSC). This corpus is a bilingual English and Farsi speech corpus. 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 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. Results collected in this study indicate that this can narrow the degradation gap caused by the language mismatch.

Speech processing, automatic speech and speaker recognition are the major area of interests in the field of computational linguistics. Research and development of computer and human interaction, forensic technologies and dialogue systems have been the motivating factor behind this interest. In this paper, JSpeech is introduced, a multilingual corpus. This corpus contains 1332 hours of conversational speech from 47 different languages. This corpus can be used in a variety of studies, created from 106 public chat group the effect of language variability on the performance of speaker recognition systems and automatic language detection. To this end, we include speaker verification results obtained for this corpus using a state of the art method based on 3D convolutional neural network.