In speaker verification, two independent stochastic models, i.e. a client model and a non-client (world) model, are generally used to verify the claimed identity using a likelihood ratio score. This paper investigates a variant of this approach based on a common hidden process for both models. In this framework, both models share the same topology, which is conditioned by the underlying phonetic structure of the utterance. Then, two different output distributions are defined corresponding to the client vs. world hypotheses. Based on this idea, a synchronous decoding algorithm and the corresponding training algorithm are derived. Our first experiments on the SESP telephone database indicate a slight improvement with respect to a baseline system using independent alignments. Moreover, synchronous alignment offers a reduced complexity during the decoding process. Interesting perspectives can be expected. Keywords : Stochastic Modeling, HMM, Synchronous Alignment, EM algorithm