Phonetic variability is one of the primary challenges in short duration speaker verification. This paper proposes a novel method that modifies the standard normal distribution prior in the total variability model to use a mixture of Gaussians as the prior distribution. The proposed speaker-phonetic vectors are then estimated from the posterior probability of latent variables, and each vector has a phonetic meaning. Unlike the standard total variability model, the proposed method can incorporate a phoneme classifier to perform soft content matching, which has the potential to solve the phonetic variability problem. Parameter estimation and scoring formulae for speaker-phonetic vectors method are presented. Experimental results obtained using NIST 2010 data show that the proposed technique leads to relative improvements of more than 30% when fused with total variability model and tested on 3 second duration test files.