The importance of phonetic variability for short duration speaker verification is widely acknowledged. This paper assesses the performance of Probabilistic Linear Discriminant Analysis (PLDA) and i-vector normalization for a text-dependent verification task. We show that using a class definition based on both speaker and pho-netic content significantly improves the performance of a state-of-the-art system. We also compare four models for computing the verification scores using multiple enrollment utterances and show that using PLDA intrinsic scoring obtains the best performance in this context. This study suggests that such scoring regime remains to be optimized.