In this paper, we propose a Bayesian speaker adaptation technique based on the probabilistic principal component analysis (PPCA). The PPCA is employed to obtain the canonical speaker models which provide the a priori knowledge of the training speakers. The proposed approach is conveniently incorporated into the Bayesian adaptation framework where the parameters are adapted to the new speaker’s speech according to the maximum a posteriori (MAP) criterion. Through a number of continuous digit recognition experiments, we can find the effectiveness of the PPCA-based approach compared to the other adaptation approaches with a small amount of adaptation data.