In this paper, we investigate on the role of dynamic information on the performances of AR-vector models for speaker recognition. To this purpose, we design an experimental protocol that destroys the time structure of speech frame sequences, which we compare to a more conventional one, i.e. keeping the natural time order. These results are also compared with those obtained with a (single) Gaussian model. Several measures are systematically investigated in the three cases, and different ways of symmetrisation are tested. We observe that the destruction of the time order can be a factor of improvement for the AR-vector models, and that results obtained with the Gaussian model are merely always better. In most cases, symmetrisation is beneficial.