  In recent years identity-vector (i-vector) based speaker verification (SV)
systems have become very successful. Nevertheless, environmental noise and
speech duration variability still have a significant effect on degrading the
performance of these systems. In many real-life applications, duration of
recordings are very short; as a result, extracted i-vectors cannot reliably
represent the attributes of the speaker. Here, we investigate the effect of
speech duration on the performance of three state-of-the-art speaker
recognition systems. In addition, using a variety of available score fusion
methods, we investigate the effect of score fusion for those speaker
verification techniques to benefit from the performance difference of different
methods under different enrollment and test speech duration conditions. This
technique performed significantly better than the baseline score fusion
methods.
