The total variability factor space in speaker verification system architecture based on Factor Analysis (FA) has greatly improved speaker recognition performances. Carrying out channel compensation in a low dimensional total factor space, rather than in the GMM supervector space, allows for the application of new techniques. We propose here new intersession compensation and scoring methods. Furthermore, this new approach contributes to a better understanding of the session variability characteristics in the total factor space.